Computational 
Analysis 
of 
Alternative 


Splicing 
in 
Human 
and 
Mice 


Dissertation 
zur 
Erlangung 
des 
akademischen 
Grades 
doctor 
rerum 
naturalium 
(Dr. 
rer. 
nat.) 



vorgelegt 
dem 
Rat 
der 
Biologisch-Pharmazeutischen 
Fakultat 
der 
Friedrich-Schiller-Universitat 
Jena 


von 
MSc 
Ralf 
H. 
Bortfeldt 
geboren 
am 
16. 
Februar 
1977 
in 
Berlin 



Die 
vorliegende 
Arbeit 
wurde 
am 
Lehrstuhl 
fur 
Bioinformatik 
der 
biologischpharmazeutischen 
Fakultat 
der 
Friedrich 
Schiller 
Universitat 
unter 
Leitung 
von 
Prof. 
Dr. 
Stefan 
Schuster 
angefertigt. 


Gutachter: 


1. 
Prof. 
Dr. 
Stefan 
Schuster, 
Friedrich 
Schiller 
Universitat 
Jena 
2. 
PD 
Dr. 
Matthias 
Platzer, 
FLI 
-Leibniz-Institut 
fur 
Altersforschung 
Jena 
3. 
PD 
Dr. 
Jurgen 
Klee, 
Institut 
fur 
Molekularbiologie 
und 
BioInformatik, 
Charite 
Campus 
Benjamin 
Franklin, 
Berlin 
Tag 
der 
oentlichen 
Verteidigung: 
04. 
Mai 
2009 



Summary 


The 
presented 
dissertation 
focuses 
on 
the 
computational 
analysis 
of 
the 
molecular 
biological 
mechanism 
called 
Alternative 
Splicing 
and 
integrates 
the 
topics 
Prediction 
and 
validation 
of 
alternative 
splice 
forms\, 
Characterization 
and 


"” 
functional 
impact 
of 
short 
5. 
splice 
site 
(ss) 
variations“ 
and 
Petri 
Net 
modeling 


"
of 
the 
spliceosomal 
assembly 
pathway\. 
In 
the 
rst 
part 
dierent 
features 
were 
analyzed 
in 
their 
potential 
to 
distinguish 
two 
classes 
of 
splice 
sites, 
alternative 
and 
reference 
splice 
sites. 
These 
were 
computationally 
derived 
earlier 
from 
transcript 
alignments 
of 
Expressed 
Sequence 
Tags“ 
(ESTs) 
and 
mRNA 
and 


"
stored 
in 
the 
Eased 
database. 
A 
central 
question 
was 
to 
what 
extent 
dierent 
features 
contribute 
to 
discrimination 
of 
both 
types 
of 
splice 
sites. 
In 
particular 
the 
features 
splice 
site 


"
score\, 
transcript 
coverage“ 
and 
splicing 
factor 
binding 
sites“ 
in 
vicinity 
of 
the 


""
considered 
splice 
sites 
were 
investigated. 
The 
applied 
scoring 
system 
describes 
the 
agreement 
of 
the 
considered 
splice 
site 
to 
an 
entropy 
model 
of 
human 
splice 
sites 
and 
thus 
makes 
it 
possible 
to 
capture 
for 
example 
the 
complementarity 
of 
the 
donor 
site 
to 
the 
recognizing 
motif 
within 
the 
small 
nuclear 
RNA 
(snRNA) 
of 
the 
spliceosomal 
U1 
complex. 
For 
comparison 
reference 
splice 
sites 
were 
collected 
from 
transcripts 
that 
were 
not 
predictive 
of 
alternative 
splicing, 
and 
subjected 
to 
the 
same 
analyses. 
The 
results 
showed 
that 
alternative 
splice 
(AS) 
forms 
identied 
by 
the 
Eased 
pipeline 
were 
in 
two 
of 
three 
analyzed 
features 
statistically 
separable. 
Alternative 
splice 
sites 
could 
especially 
be 
discriminated 
from 
constitutive 
splice 
sites 
by 
a 
lower 
splice 
site 
score 
and 
an 
increased 
presence 
of 
splicing 
factor 
binding 
motifs 
in 
the 
vicinity 
of 
alternative 
splice 
sites. 
Additionally, 
a 
positive 
correlation 
within 
the 
compared 
classes 
of 
alternative 
and 
reference 
splice 
forms 
could 
be 
observed, 
in 
the 
dependency 
between 
transcript 
coverage 
and 
the 
splice 
site 
score. 
This 
dependency 
shows 
that 
the 
condence 
in 
the 
AS 
form 
predicting 


iii 



Summary 


process 
raises 
with 
the 
number 
of 
available 
transcripts 
and 
similar 
as 
it 
can 
be 
observed 
within 
the 
set 
of 
reference 
splice 
forms. 
The 
results 
have 
been 
published 
in 
the 
book 
series 
Modelling 
and 
Simulation 
in 


"
Science, 
Engineering 
and 
Technology“ 
in 
Birkhauser 
/ 
Springer 
(1). 


With 
the 
ongoing 
development 
of 
prediction 
and 
annotation 
techniques 
for 
alternative 
splice 
forms, 
distinct 
splice 
patterns 
have 
been 
discovered 
and 
analyzed 
in 
more 
detail. 
Most 
comprehensively 
the 
class 
of 
skipped 
exons 
(SE) 
has 
been 
studied 
over 
the 
past 
decade, 
followed 
by 
alternative 
5. 
and 
3. 
splice 
site 
exons. 
Nevertheless 
only 
recently, 
the 
interesting 
concept 
termed 
subtle 
alternative 
splicing 
has 
been 
introduced 
based 
on 
the 
ndings 
of 
a 
special 
pattern 
of 
human 
alternative 
acceptor 
splice 
forms 
of 
NAGNAG 
type 
(2). 
In 
conjunction 
with 
this 
nding, 
the 
second 
part 
of 
the 
presented 
thesis 
focuses 
on 
a 
subtle 
alternative 
splicing 
pattern 
with 
the 
motif 
GYNNGY 
at 
the 
donor 
site, 
which 
occurs 
almost 
as 
frequently 
as 
splice 
sites 
with 
the 
NAGNAG 
motif. 
In 
contrast, 
splicing 
at 
GYNNGY 
tandem 
donors 
implies 
an 
mRNA 
variation, 
which 
is 
not 
a 
multiple 
of 
the 
codon 
length 
and 
hence 
indicates 
a 
more 
intricate 
role 
in 
mRNA 
turnover. 
For 
example, 
changing 
the 
reading 
frame 
may 
result 
in 
a 
premature 
stop 
codon 
in 
the 
matured 
transcript, 
potentially 
draining 
the 
mRNA 
from 
the 
pool 
of 
expressed 
transcripts. 
The 
motif 
GYNNGY 
itself 
also 
serves 
as 
recognition 
site 
for 
U1 
and 
U5 
snRNA 
motifs, 
hence 
the 
splicing 
machinery 
has 
to 
distinguish 
between 
these 
two 
overlapping 
donor 
sites. 
The 
analysis 
of 
such 
small 
splicing 
variations 
requires 
a 
dataset 
of 
higher 
resolution 
and 
sensitivity 
near 
exon 
intron 
junctions. 
To 
this 
end 
the 
Hollywood 
database 
provided 
a 
thoroughly 
prepared 
dataset 
of 
alternative 
splice 
patterns 
(3). 
Among 
the 
alternative 
exons, 
overlapping 
donor 
splice 
sites 
constituted 
more 
than 
80% 
of 
the 
total 
set 
of 
predicted 
alternative 
5. 
splice 
sites. 
Among 
these, 
41% 
of 
the 
alternative 
donor 
sites 
showed 
the 
motif 
GYNNGY, 
thus 
being 
the 
most 
frequently 
tandem 
donor 
variant. 
In 
silico 
and 
experimental 
characterization 
of 
alternative 
splice 
variants 
of 
this 
GYNNGY 
type 
showed 
distinct 
properties 
in 
splice 
site 
strength, 
sequence 
conservation, 
presence 
of 
splicing 
enhancer 
motifs 
and 
functional 
consequences 
according 
to 
utilization 
levels 
of 
the 
alternative 
overlapping 
donor 
splice 
sites. 
It 
turned 
out 
that 
those 
AS 
forms 
which 
predominantly 
utilized 
the 
downstream 
(proximal) 
donor 
over 
the 
rare 
upstream 
(distal) 
donor 
(type-I) 
were 
clearly 
distinguishable 
from 
constitutive 
exons. 
In 
contrast, 
the 
group 
of 
AS 
forms 
predominantly 
utilizing 



Summary 


the 
upstream 
donor 
over 
the 
alternative 
downstream 
donor 
(type-II), 
was 
more 
similar 
to 
constitutive 
exons 
with 
respect 
to 
the 
analyzed 
features. 
Summarizing, 
the 
results 
point 
to 
the 
existence 
of 
a 
biological 
mechanism, 
which 
involves 
frame 
shifted 
alternative 
splice 
variants, 
continuously 
produced 
at 
low 
levels 
for 
the 
cause 
of 
either 
a 
directed 
nonsense-mediated 
mRNA 
decay 
or 
truncated 
proteins. 
The 
mere 
observation 
of 
this 
phenomenon 
questions 
the 
absolute 
eectiveness 
of 
the 
NMD 
mechanism 
and 
connetcs 
to 
new 
theories 
about 
the 
processing 
of 
primary 
transcripts 
beyond 
the 
purpose 
of 
coding 
for 
functional 
proteins. 
This 
work 
is 
published 
in 
the 
journal 
BMC 
Genomics 
(4). 


In 
the 
last 
part 
of 
this 
thesis, 
a 
computational 
basis 
for 
investigating 
the 
concurrent 
processes 
of 
spliceosome 
assembly 
was 
established. 
The 
spliceosome 
is 
the 
catalytically 
active 
key 
component 
of 
the 
splicing 
process, 
whereupon 
a 
functional 
macro 
complex 
is 
assembled 
from 
proteins 
and 
snRNA 
via 
a 
multitude 
of 
signaling 
reactions 
within 
the 
nucleus 
of 
eukaryotic 
cells. 
Petri 
net 
theory 
was 
applied 
to 
model 
the 
reactions 
and 
interactions 
of 
the 
spliceosome 
assembly 
pathway. 
Initially 
a 
high 
degree 
of 
biological 
knowledge 
has 
to 
be 
screened 
in 
order 
to 
draft 
a 
Petri 
Net 
model 
but 
the 
resulting 
graph 
structure 
allows 
an 
easier 
validation 
and 
exploitation 
of 
the 
accumulating 
experimental 
data. 
Calculation 
of 
semi 
positive 
t-invariants 
serves 
as 
a 
means 
of 
validating 
existing 
-and 
uncover 
new 
signal 

ows 
within 
the 
spliceosomal 
assembly 
process. 
The 
biologically 
and 
mathematically 
validated 
model 
proves 
as 
being 
suitable 
for 
simulation 
and 
classication 
of 
signaling 
pathways. 
A 
set 
of 
71 
invariant 
pathways 
could 
be 
delineated 
from 
a 
network 
of 
reaction, 
and 
being 
subjected 
to 
further 
structural 
classication 
by 
means 
of 
clustering 
and 
decomposition 
into 
partial 
pathways 
with 
high 
degree 
of 
shared 
information 

ow. 
This 
approach 
serves 
well 
to 
integrate 
experimental 
data 
from 
literature 
and 
structure 
molecular 
biological 
information. 
As 
consequence, 
Petri 
nets 
help 
to 
uncover 
weaknesses 
of 
existing 
partial 
models, 
to 
uncover 
inconsistent 
hypothesizing 
and 
nally 
to 
generate 
new 
insights 
into 
molecular 
biological 
mechanism. 



Summary 



Zusammenfassung 


Die 
vorliegende 
Dissertation 
konzentriert 
sich 
auf 
bioinformatische 
Analysen 
zum 
molekularbiologischen 
Mechanismus 
des 
Alternativen 
Spleiens 
und 
verbindet 
die 
Themen 
Vorhersage 
und 
Bewertung 
von 
alternativen 
Spleifor


"
men\, 
Charakterisierung 
und 
funktionelle 
Auswirkungen 
von 
kleinen 
Variatio


"
nen 
des 
5. 
Spleisignals“ 
und 
Modellierung 
der 
Bildung 
des 
spleiosomalen 
Pro


"
teinkomplexes 
mittels 
Petri-Netzen\. 


Im 
ersten 
Teil 
der 
Arbeit 
wurden 
verschiedene 
Merkmale 
auf 
ihr 
Potential 
hin 
analysiert, 
zwei 
Typen 
von 
Spleistellen, 
alternative 
und 
Referenzspleistellen, 
zu 
unterscheiden. 
Diese 
wurden 
zuvor 
in 
silcio 
aus 
Transkript 
Alignments 
von 


Expressed 
Sequence 
Tags“ 
(ESTs) 
und 
mRNA 
abgeleitet 
und 
mit 
zusatzlichen 


"
Informationen 
in 
der 
Eased-Datenbank 
gespeichert. 
Eine 
zentrale 
Frage 
war, 
in 
welchem 
Mae 
verschiedene 
Merkmale 
zur 
Diskriminierung 
beider 
Klassen 
von 
Spleiereignissen 
beitragen. 
Besonders 
die 
Merkmale 
Spleisig


"
nalstarke\, 
Transkripthaugkeit“ 
und 
Spleifaktor-Bindstellen“ 
im 
Bereich 


""
der 
betrachtetenSpleistellen 
wurden 
analysiert. 
Das 
verwendete 
Scoringmaß 
fur 


¨ 


die 
Spleistellen 
beschreibt 
die 
Ubereinstimmung 
der 
betrachteten 
Spleistelle 
zu 
einem 
Entropiemodell 
von 
humanen 
Spleistellen 
und 
ermoglicht 
so 
z.B. 
die 
Komplementaritat 
des 
Donor-Motivs 
zur 
Erkennungssequenz 
innerhalb 
der 
small 
nuclear 
RNA 
(snRNA) 
der 
spleiosomalen 
Untereinheit 
U1 
zu 
bewerten. 
Zum 
Vergleich 
wurden 
Referenzspleistellen 
aus 
Transkripten 
zusammengestellt, 
welche 
keine 
Spleivariationen 
zeigten, 
und 
auf 
die 
gleichen 
Merkmale 
untersucht. 
Die 
Ergebnisse 
belegten, 
dass 
durch 
den 
Eased 
Algorithmus 
generierte 
AS 
Formen 
in 
zwei 
der 
drei 
untersuchten 
Merkmale 
von 
den 
Referenzspleiformen 
unterschieden 
werden 
konnten. 
Alternative 
Spleistellen 
liessen 
sich 
dabei 
von 
konstitutiven 
Spleistellen 
besonders 
durch 
einen 
niedrigeren 
Spleistellen-Score 
und 
vermehrtes 
Auftreten 
von 
Bindemotiven 
fur 
SR 
Proteine 
in 
der 
naheren 
Umgebung 
der 
Spleistellen 
abgrenzen. 
Zusatzlich 
konnte 
eine 
positive 
Korre


vii 



Zusammenfassung 


lation 
in 
beiden 
Vergleichsklassen 
beobachtet 
werden, 
zwischen 
der 
Haugkeit 
von 
gespleiten 
Transkripten 
und 
dem 
Spleistellen-Score. 
Diese 
Abhangigkeit 
zeigt, 
dass 
das 
Vertrauen 
in 
die 
vorhergesagten 
alternativen 
Spleiformen 
mit 
der 
Anzahl 
verfugbarer 
Transkripte 
steigt, 
und 
zwar 
in 
gleicher 
Weise, 
wie 
es 
bei 
konstitutiven 
Transkripten 
beobachtet 
werden 
kann. 
Die 
Ergebnisse 
dieser 
Studie 
wurden 
in 
der 
Buchserie 
Modelling 
and 
Simulation 


"
in 
Science, 
Engineering 
and 
Technology“ 
des 
Birkhauser 
/ 
Springer 
Verlags 
(1) 


veroentlicht. 


Mit 
der 
voranschreitenden 
Entwicklung 
von 
Techniken 
zur 
Vorhersage 
und 
Annotation 
von 
alternativen 
Spleiformen 
wurden 
diverse 
Spleimuster 
entdeckt 
und 
detaillierter 
untersucht. 
Am 
umfangreichsten 
wurde 
uber 
die 
letzten 
Jahre 



hinweg 
Spleimuster 
untersucht, 
welche 
vollstandige 
Exons 
in 
Transkripten 
ubergehen 
oder 
einfugen 
(Kassettenexons\). 
Weitere 
gut 
untersuchte 
Beispiele 


"
existieren 
fur 
Spleiformen, 
welche 
Exons 
an 
der 
angrenzenden 
5. 
Spleistelle 
(Donor) 
oder 
3. 
Spleistelle 
(Akzeptor) 
variieren. 
Erst 
kurzlich 
wurde 
das 
interessante 
Konzept 
subtilen 
alternativen 
Spleiens 
vorgestellt. 
Dieses 
Konzept 
basiert 
auf 
Beobachtungen 
von 
Spleivorgangen 
an 
sogenannten 
Tandem-Akzeptor 
Spleisignalen 
mit 
dem 
Motiv 
NAGNAG, 
welche 
im 
Humanentrankriptom 
aber 
auch 
in 
Transkripten 
anderer 
Eukaryoten 
gefunden 
wurde 
(2). 
In 
Anknupfung 
an 
diese 
Entdeckung 
befasst 
sich 
der 
zweite 
Teil 
der 
vorliegenden 
Arbeit 
mit 
einem 
weiteren 
subtilen 
alternativen 
Spleimuster, 
welches 
ahnlich 
haug, 
jedoch 
an 
Donor-Spleisignalen 
auftritt. 
Im 
Unterschied 
zu 
der 
drei 
Nukleotide 
umfassenden 
Variation 
am 
Akzeptor 
lasst 
die 
vier 
Nukleotide 
betreende 
Donorvariation 
auf 
eine 
komplexe 
Rolle 
im 
RNA-Reifungsprozess 
schlieen. 
Durch 
die 
Verschiebung 
des 
Leserasters 
kann 
z.B. 
ein 
deutlich 
haugeres 
Auftreten 
von 
vorzeitigen 
Stopcodons 
in 
reifen 
mRNAs 
beobachtet 
werden. 
Das 
Spleisignalmotiv 
GYNNGY 
selbst 
dient 
dabei 
als 
Erkennungssequenz 
fur 
die 
zwei 
wichtigen 
spleiosomalen 
Ribonukleoproteinkomplexe 
U1 
und 
U5, 
deren 
Bindung 
durch 
die 
zwei 
Die 
Analyse 


uberlappenden 
Motive 
beein
usst 
wird. 
dieses 
speziellen 
Spleimusters 
erforderte 
einen 
Datensatz 
mit 
einer 
nukleotidgenauen 
Au
osung 
der 
alternativen 
Spleistellen, 
um 
die 
kurzen 
Variationen 
charakterisieren 
zu 
konnen. 
Ein 
entsprechender 
Datensatz 
wurde 
aus 
der 
Holl
ywood-Datenbank 
(3) 
gewonnen, 
welche 
im 
Gegensatz 
zur 
Eased 
Datenbank 
mehr 
Spleimuster 
klassiziert 
und 
auch 
Transkriptvariationen 
von 
weniger 
als 
zehn 
10 
Nukleotiden 
berucksichtigt. 
Die 
Daten 
zeigten, 
dass 
kurze 
Variationen 



Zusammenfassung 


mit 
uberlappenden 
Donormotiven 
mehr 
als 
80% 
aller 
nachweisbaren 
alternativen 
Donor 
Spleiereignisse 
Dabei 
unter 
diesen 


ausmachen. 
tritt 
uberlappenden 
Donoren 
mit 
41% 
am 
haugsten 
das 
Motiv 
GYNNGY 
auf. 
Mittels 
in 
silico 
Analysen 
und 
experimenteller 
Validierung 
wurde 
die 
Plausibilitat 
dieses 
subtilen 
Spleimusters 
bestatigt. 
Sequenzanalysen 
zeigten 
ausserdem 
ausgepragte 
Charakteristika 
bezuglich 
des 
Spleistellen-Scores 
(Nukleotidzusammensetzung), 
Konservierung, 
Vorliegen 
von 
potentiellen 
Bindemotiven 
fur 
Spleifaktoren 
sowie 
funktionelle 
Auswirkungen 
in 
Abhangigkeit 
der 
Nachweishaugkeit 
beider 


uberlappender 
Donoren 
in 
gespleiten 
Transkripten. 
Ein 
Ergebnis 
deutet 
darauf 
hin, 
dass 
besonders 
die 
GYNNGY 
Spleiformen, 
welche 
seltener 
am 
ersten 
(distalen) 
Donor 
gegenuber 
dem 
zweiten 
(proximalen) 
Donor 
gespleit 
werden, 
in 
allen 
oben 
genannten 
Merkmalen 
deutlich 
von 
konstitutiven 
Spleiformen 
(mit 
ahnlichem 
Donor 
Motiv) 
unterschieden 
werden 
konnen. 
Gleiches 
konnte 
fur 
den 
umgekehrten 
Fall, 
bei 
dem 
die 
erste 
Spleistelle 
hauger 
gespleit 
wird 
als 
die 
zweite 
Spleistelle, 
nur 
bedingt 
nachgewiesen 
werden. 
Zusammenfassend 
deuten 
die 
Ergebnisse 
auf 
einen 
biologischen 
Mechanismus 
hin, 
welcher 
alternative 
Spleivarianten 
erzeugt, 
die 
das 
Leseraster 
verschieben 
und 
kontinuierlich 
in 
geringen 
Mengen 
generiert 
werden, 
entweder 
zum 
gezielten 
Auslosen 
des 
NMD 
Mechanismus 
oder 
zur 
Erzeugung 
verkurzter 
Proteine. 
Die 
alleinige 
Beobachtung 
dieses 
Phanomens 
stellt 
die 
uneingeschrankte 
Wirksamkeit 
des 
Nonsense 
Mediated 
RNA 
Decay“ 
(NMD) 
Mechanismus 
in 
Frage 
und 
knupft

"
an 
neue 
Theorien 
von 
artranskripten 
an, 
jenseits

uber 
die 
Verarbeitung 
Prim
ihrer 
klassischen 
Funktion 
funktionelle 
Proteine 
zu 
kodieren. 
Diese 
Arbeit 
wurde 
im 
Journal 
BMC 
Genomics 
veroentlicht 
(4). 


Das 
Spleiosom 
ist 
die 
katalytisch 
aktive 
Schlusselkomponente 
des 
Spleiens, 
wobei 
durch 
eine 
Vielzahl 
von 
Signalreaktionen 
ein 
funktioneller 
Makrokomplex 
aus 
Proteinen 
und 
kleinen 
nuklearen 
RNAs 
an 
Primartranskripten 
im 
Zellkern 
von 
Eukaryoten 
aufgebaut 
wird. 
Die 
spleiosomalen 
Signalreaktionen 
wurden 
aus 
experimenteller 
Fachliteratur 
zusammengestellt 
und 
mit 
Hilfe 
der 
Petri 
Netz 
Theorie 
modelliert. 
Im 
Gegensatz 
zu 
bisherigen 
Anwendungen 
von 
Petri 
Netzen 
zur 
Analyse 
metabolischer 
Netzwerke, 
wurde 
in 
diesem 
Ansatz, 
bedingt 
durch 
das 
Fehlen 
stochiometrischer 
Daten, 
ein 
binarer 
Entscheidungsbaum 
modelliert. 
Dabei 
entsprechen 
Token 
auf 
den 
Knoten 
des 
Netzwerks 
einem 
Informationsgehalt 
oder 
Zustand, 
der, 
wenn 
gegeben, 
eine 
Reaktion 
ermoglicht. 
Durch 
diese 
Interpretation 
wird 
es 
moglich, 
Kanten 
im 
Netzwerk 
konstant 
mit 
einem 
Gewicht 



Zusammenfassung 


von 
eins 
zu 
modelieren. 
Im 
Gegensatz 
zu 
ungerichteten 
Protein-Protein 
Wechselwirkungsnetzwerken, 
erforderte 
dieser 
Ansatz 
die 
Einordnung 
von 
Protein-
Protein 
sowieo 
Protein-RNA 
Wechselwirkungen 
in 
eine 
Abfolge 
zeitlich 
geordneter 
Reaktionen. 


Die 
Darstellung 
des 
spleisomalen 
Signalwegs 
als 
Petri 
Netz 
eronet 
unter 
der 
Annahme 
eines 
Steady 
State 
Systems 
die 
Berechnung 
von 
mimimalen, 
semipostivien 
T-Invarianten, 
welche 
zur 
Validierung 
des 
Modells 
herangezogen 
werden 
konnen. 
Die 
darauf 
aufbauenden 
Analysemoglichkeiten 
sowie 
die 
leichte 
Erweiterbarkeit 
des 
Modells 
rechtfertigen 
den 
Modellierungsansatz 
im 
Hinblick 
auf 
die 
stetig 
wachsende 
Menge 
experimenteller 
Daten. 
Die 
T-Invarianten 
beschreiben 
strukturelle 
Eigenschaften 
des 
spleiosomalen 
Signalnetzwerks 
und 
bilden 
eine 
Grundlage 
fur 
die 
Identizierung 
neuer 
Signalwege. 
Insbesondere 
die 
Eigenschaft 
nebenlauge 
Prozesse 
abbilden 
zu 
konnen 
ermoglichte 
es, 
mit 
Hilfe 
des 
Petrinetzes 
die 
verschiedenen 
Varianten 
der 
Initiierung 
des 
spleisomalen 
Signalnetzwerks 
darzustellen 
und 
zu 
vergleichen. 


Das 
Modellnetzwerk 
eignet 
sich 
daruberhinaus 
sowohl 
fur 
die 
animierte 
Simulation 
der 
Reaktionen 
des 
Spleiosom-Aufbaus 
als 
auch 
fur 
die 
Klassizierung 
von 
Signalwegen, 
um 
wichtige 
Knotenpunkte 
im 
Netzwerk 
zu 
identizieren. 
Zusamenfassend 
eronet 
dieser 
Modellansatz 
die 
Moglichkeit, 
die 
groen 
Mengen 
an 
Proteininteraktionsdaten 
aus 


oentlichen 
Datenbanken 
und 
Publikationen 
sinnvoll 
fur 
die 
Erstellung 
neuer-und 
zur 
Analyse 
bestehender 
Hypothesen 
einzusetzen. 



Contents 


Summary 
iii 


Zusammenfassung 
vii 


Contents 
xiii 


Abbreviations 
xv 


1 
Validation 
of 
Alternative 
Splice 
Forms 
Detected 
in 
silico 
1 


1.1 
Introduction 
.............................. 
1 


1.1.1 
AlternativeSplicing...................... 
1 


1.1.2 
History 
of 
Computational 
Analyses 
on 
Alternative 
Splicing 
3 


1.1.3 
Splicing 
Factors 
and 
their 
Role 
in 
Regulating 
Alternative 
Splicing 
............................ 
6 


1.2 
Methods 
................................ 
7 


1.2.1 
TheEASEDDatabase 
.................... 
7 


1.2.2 
Locating 
Alternative 
Splice 
Forms 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
7 


1.2.3 
DataPreparationandRenement 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
10 


1.2.4 
SpliceSiteScoring 
...................... 
11 


1.2.5 
Scanning 
for 
Splice-Enhancing 
Motifs 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
13 


1.2.6 
Data 
Partitioning 
for 
Statistical 
Analysis 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
14 


1.3 
Results................................. 
15 


1.3.1 
AnalysisofEASEDSpliceForms 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
15 


1.3.2 
General 
Characterization 
of 
Splice 
Site 
Attributes 
. 
. 
. 
. 
. 
15 


1.3.3 
Characterizing 
Specic 
Attributes 
of 
Alternative 
and 
ReferenceSpliceSites 
...................... 
18 


1.4 
Discussion............................... 
21 


xi 



CONTENTS 


2 
Analysis 
of 
Overlapping 
Donor 
Splice 
Sites 
23 


2.1 
Introduction 
.............................. 
23 


2.1.1 
SubtleSpliceVariants 
.................... 
23 


2.1.2 
Proposed 
Mechanisms 
for 
Regulating 
the 
Donor 
Splice 
Site 
25 


2.2 
Methods 
................................ 
27 


2.2.1 
DataSetofAlternativeExons 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
27 


2.2.2 
Spliced-Alignments 
...................... 
27 


2.2.3 
Classication 
of 
Major 
and 
Minor 
Tandem 
Donors 
. 
. 
. 
. 
. 
28 


2.2.4 
Statistical 
Analysis 
of 
Splice 
Sites 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
29 


2.2.5 
Identication 
of 
Non-Sense 
Codons 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
30 


2.2.6 
Detection 
of 
Sequence 
Conservation 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
30 


2.2.7 
ExperimentalAssay...................... 
30 


2.2.8 
Presence 
of 
Predicted 
Splicing-Regulatory 
Elements 
. 
. 
. 
. 
31 


2.2.9 
GeneOntology(GO)Annotations 
.............. 
31 


2.3 
Results................................. 
33 


2.3.1 
Biased 
Extensions 
of 
Alternative 
50ss 
and 
30ssExons 
. 
. 
. 
33 


2.3.2 
TandemDonorsandAcceptors 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
37 


2.3.3 
Experimental 
Validation 
of 
Tandem 
Donors 
. 
. 
. 
. 
. 
. 
. 
. 
39 


2.3.4 
Two 
Distinct 
Levels 
of 
A5E 
Proximal 
and 
Distal 
Splicing 
. 
42 


2.3.5 
Splice 
Sites 
of 
A5Es 
Score 
Dierently 
between 
Type 
I 
and 
II 
45 


2.3.6 
Discriminating 
A5E4 
versus 
Constitutively 
Spliced 
Exons 
49 


2.3.7 
Nucleotide 
Conservation 
around 
Major 
and 
Minor 
A5E4 
SpliceSites 
.......................... 
50 


2.3.8 
Higher 
Levels 
of 
Intron 
Conservation 
near 
Type 
I 
Tandem 
Donors............................. 
53 


2.3.9 
Occurrence 
of 
Splicing 
Signals 
in 
Exon 
Flanking 
Sequences 
54 


2.3.10 
A5E4 
Splicing 
Exons 
often 
Produce 
NMD 
Target 
Substrates............................. 
56 


2.4 
Discussion............................... 
60 


3 
Petrinet 
Analysis 
of 
Spliceosome 
Assembly 
67 


3.1 
Introduction 
.............................. 
67 


3.1.1 
TheSpliceosome 
....................... 
67 


3.1.2 
The 
Basic 
Steps 
of 
Spliceosome 
Assembly 
. 
. 
. 
. 
. 
. 
. 
. 
. 
68 


3.1.3 
ModelingtheSpliceosome 
.................. 
73 


3.2 
Methods 
................................ 
78 



CONTENTS 


3.2.1 
DenitionofPetriNets.................... 
78 


3.2.2 
ModelRenement 
...................... 
84 


3.2.3 
DataPreparation 
....................... 
87 


3.3 
Results................................. 
88 


3.3.1 
Application 
of 
PN 
Modules 
in 
Splicing 
Models 
. 
. 
. 
. 
. 
. 
. 
88 


3.3.2 
Dening 
Biological 
Reactions 
of 
the 
Spliceosome 
Assembly 
Pathway 
............................ 
92 


3.3.3 
Invariant 
Signaling 
Pathways 
in 
Spliceosome 
Assembly 
. 
. 
93 


3.3.4 
Decomposition 
of 
the 
Spliceosomal 
Network 
into 
Functional 
Units.............................. 
100 


3.4 
Discussion............................... 
111 


Conclusions 
115 


Erklarung 
127 


Glossary 
129 


Bibliography 
131 


Danksagung 
147 


A 
Supplements 
to 
Chapter 
1 
149 


A.1 
TheEASEDDatabaseScheme.................... 
149 


B 
Supplements 
to 
Chapter 
2 
153 


B.1 
Sim4AlignmentErrors 
........................ 
153 


B.2 
A5E4 
Splice 
Site 
Scores 
in 
M. 
musculus 
............. 
155 


B.3 
BaseCompositionofTandemSpliceSites. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
156 


C 
Supplements 
to 
Chapter 
3 
159 


C.1 
Description 
of 
Reactions 
of 
the 
Spliceosomal 
Model 
Network 
. 
. 
. 
159 


C.2 
Application 
of 
the 
Integrated 
Net 
Analyszer 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
166 


C.3 
T-Invariants 
Computed 
for 
the 
Spliceosomal 
Assembly 
Network 
. 
167 



CONTENTS 



Abbreviations 


50ss 
5. 
splice 
site 
30ss 
3. 
splice 
site 
AS, 
as 
alternative 
splicing, 
alternatively 
spliced 
asf 
alternative 
splice 
form 
ass 
alternative 
splice 
site 
A3E 
alternative 
30ss 
exon 
A5E 
alternative 
50ss 
exon 
ACE 
alternative 
conserved 
exon 
AFE 
alternative 
rst 
exon 
ALE 
alternative 
last 
exon 
AP 
alternative 
polyadenylation 
BP 
Branchpoint 
CE 
constitutive 
exon 
cDNA 
complementary 
DNA 
CDS 
coding 
sequence 
css 
constitutive 
splice 
site 
d	4 
pseudo 
4 
tandem 
donor 
site 
without 
evidence 
of 
AS; 
located 
upstream 
of 
a 
constitutively 
spliced 
donor 
D4 
(d4) 
distal-major 
(distal-minor) 
donor 
DTC 
delayed 
termination 
codon 
E5 
exon 
5. 
end 
E3 
exon 
3. 
end 
ESE 
exon 
splicing 
enhancer 
ESS 
exon 
splicing 
silencer 
EST 
expressed 
sequence 
tag 
hsp 
high 
scoring 
segment 
pair 
I5 
intron 
5. 
end 


xv 



Abbreviations 


I3 
intron 
3. 
end 
ISS 
intron 
splicing 
silencer 
MCTS 
maximal 
common 
transition 
set 
MXE 
mutually 
exclusive 
exon 
NMD 
nonsense 
mediated 
RNA 
decay 
nt 
nucleotide 
p	4 
pseudo 
4 
tandem 
donor 
site 
without 
evidence 
of 
AS; 
located 
downstream 
of 
a 
constitutively 
spliced 
donor 
P4 
(p4) 
proximal-major 
(proximal-minor) 
tandem 
donor 
PN 
Petri 
net 
PolyA 
polyadenylated 
tail 
of 
mature 
mRNA 
PPT 
polypyrimidine 
tract 
PTC 
premature 
termination 
codon 
RI 
retained 
intron 
rss 
reference 
splice 
site 
RNP 
ribonucleoprotein 
RRM 
RNA 
recognition 
motif 
SE 
skipped 
exon 
(cassette 
exon) 
snRNA 
small 
nuclear 
RNA 
SRp 
proteine 
(splicing 
factors) 
with 
Serine-aRginine 
rich 
domains 



Chapter 
1 


Validation 
of 
Alternative 
Splice 
Forms 
Detected 
in 
silico 


1.1 
Introduction 
1.1.1 
Alternative 
Splicing 
The 
central 
dogma 
of 
molecular 
biology 
postulates 
the 
directed 

ow 
of 
genomically 
encoded 
information 
in 
the 
nucleus 
via 
messenger 
RNA 
(mRNA) 
to 
the 
cytoplasm 
where 
the 
message 
is 
translated 
into 
proteins 
.In 
higher 
organisms, 
this 
information 

ow 
becomes 
dynamically 
modulated 
by 
RNA 
maturation 
processes 
among 
which 
splicing 
is 
a 
prominent 
example. 
Splicing 
describes 
the 
process 
by 
which 
parts 
(introns) 
of 
the 
pre-cursor 
messenger 
RNA 
are 
catalytically 
excised 
by 
a 
ribozyme 
complex 
while 
remaining 
pieces 
(exons) 
are 
ligated 
and 
constitute 
the 
coding 
sequence. 
Furthermore, 
some 
parts 
of 
the 
spliced 
transcript 
remain 
untranslated 
at 
the 
5. 
and 
3. 
end 
of 
the 
mRNA. 
The 
recognition 
of 
the 
donor 
and 
acceptor 
splice 
sites 
at 
the 
exon-intron 
boundaries 
are 
sensitive 
steps 
in 
initializing 
the 
splicing 
reaction. 
Only 
a 
few 
years 
after 
discovery 
of 
the 
splicing 
mechanism 
in 
1977 
(5), 
it 
was 
clear 
that 
mRNAs 
from 
the 
same 
genomic 
region 
could 
dier 
in 
length 
and 
nucleotide 
composition. 
Successively, 
it 
was 
shown 
that 
alternative 
splice 
reactions 
can 
take 
place 
when 
splicing 
signals 
are 
switched 
on 
and 
off 
or 
occur 
in 
competing 
proximity 
to 
each 
other 
(6). 
Today, 
alternative 
splicing 
(AS) 
has 
become 
a 
paradigm 
to 
explain 
the 
increasing 
morphological 
complexity 
as 
compared 
to 
genome 
size, 
that 
is 
observable 
in 
eukaryotes 
from 
protozoans 
via 
nematodes, 
arthropods 
to 
vertebrates 
(7, 
8). 
AS 
events 
are 
categorized 
in 
dierent 
patterns 
of 
splice 
site 
selection 
and 
one 


1 



Alternative 
Splicing 


can 
distinguish 
the 
four 
basic 
types: 
exon-skipping 
(SE), 
in 
which 
mRNA 
isoforms 
dier 
by 
the 
inclusion/exclusion 
of 
an 
exon; 
alternative 
50ss 
exon 
(A5E) 
or 
alternative 
30ss 
exon 
(A3E), 
in 
which 
isoforms 
dier 
in 
the 
usage 
of 
a 
50ss 
or 
30ss, 
respectively; 
and 
retained-intron 
types 
(RI), 
in 
which 
isoforms 
dier 
by 
the 
presence/absence 
of 
an 
un-spliced 
intron 
(9). 
These 
types 
are 
not 
necessarily 
mutually 
exclusive 
and 
more 
complex 
types 
of 
AS 
events 
can 
be 
constructed 
from 
such 
canonical 
types. 
In 
addition, 
AS 
holds 
the 
possibility 
to 
control 
gene 
expression 
at 
the 
post-transcriptional 
level 
via 
the 
non-sense 
mediated 
mRNA 
decay 
(NMD) 
pathway. 
To 
prevent 
aberrantly 
or 
deliberately 
incorrectly 
spliced 
transcripts 
that 
prematurely 
terminate 
translation, 
NMD 
ensures 
that 
only 
correctly 
spliced 
mRNAs 
that 
contain 
the 
full 
(or 
nearly 
so) 
message 
are 
subsequently 
utilized 
for 
protein 
synthesis. 
Therefore, 
NMD 
scans 
newly 
synthesized 
mRNA 
for 
the 
presence 
of 
one 
or 
more 
premature-termination 
codons 
(PTCs), 
and, 
if 
detected, 
can 
selectively 
degrade 
defective 
mRNAs 
(10). 
Furthermore, 
pathological 
complexity 
can 
be 
ascribed 
to 
misregulated 
splicing 
with 
cancer 
as 
one 
of 
the 
most 
disastrous 
examples 
(11). 
The 
mechanism 
of 
alternative 
splicing 
has 
attracted 
a 
wide 
range 
of 
scientic 
research 
addressing 
the 
problem 
with 
computational 
strategies 
and 
tools 
(12, 
13, 
14, 
15, 
16). 
A 
variety 
of 
databases 
have 
been 
designed 
to 
collect 
alternative 
splice 
forms 
(17) 
along 
with 
accompanying 
experimental 
conditions 
like 
source 
tissue, 
developmental 
stage 
or 
pathological 
information 
(cf. 
Table 
1.1). 
Purication 
of 
spliceosomal 
components 
(see 
chapter 
3.1.1) 
and 
in 
vitro 
splicing 
assays 
showed 
that 
regulatory 
proteins 
are 
involved 
in 
initiating 
the 
splice 
mechanism 
and 
maintaining 
spliceosomal 
activities 
(18, 
19, 
20). 
Most 
of 
the 
known 
splice 
regulatory 
proteins 
are 
members 
of 
a 
protein 
family 
that 
share 
serine-arginine 
domains, 
called 
SR 
proteins. 
Additionally, 
SR 
proteins 
can 
bind 
via 
RNA 
recognition 
domains 
to 
specic 
sequence 
motifs 
to 
promote 
alternative 
splice 
site 
usage 
and 
exon 
denition 
(7, 
21). 
Considering 
the 
growing 
percentage 
of 
genes 
that 
are 
aected 
by 
alternative 
splicing 
transcripts 
there 
must 
be 
a 
dense 
regulatory 
network, 
which 
receives 
and 
forward 
signals 
required 
to 
perform 
the 
surgical 
task 
of 
splicing. 
Consequently 
eukaryotic 
cells 
have 
a 
means 
to 
react 
appropriately 
to 
dierent 
environmental 
conditions 
by 
triggering 
the 
splicing 
of 
dierent 
isoforms. 
Over 
the 
last 
decade 
experimental 
studies 
have 
amply 
provided 
examples 
of 
alternative 
splice 
events 
whose 
regulation 
depends 
on 
the 
presence 
and 
distribution 
of 
cis-elements 
and 
the 
concentrations 
of 
their 
recognizing 
trans-factors 
(6, 
22). 
These 
preliminary 
works 
paved 
the 
way 
for 
a 
variety 



Introduction 


of 
computational 
studies, 
some 
of 
their 
covered 
topics 
brie
y 
summarized 
in 
the 
following 
list: 


i. 
splice 
site 
modeling 
(23), 
ii. 
splice 
site 
plasticity 
(2), 
iii. 
exon/intron 
sequence 
composition 
(24) 
iv. 
splicing 
pattern 
statistics 
(25) 
and 
v. 
splicing 
enhancer 
and 
silencer 
prediction 
(26, 
27). 
1.1.2 
History 
of 
Computational 
Analyses 
on 
Alternative 
Splicing 
The 
computational 
detection 
of 
alternative 
splice 
forms 
based 
on 
sequence 
analysis 
is 
tightly 
connected 
to 
the 
identication 
of 
genes 
within 
genomes. 
That 
is 
because 
mature 
transcripts 
still 
bear 
a 
strong 
resemblance 
to 
the 
genomic 
loci 
from 
which 
they 
were 
transcribed. 
Hence, 
the 
comparison 
of 
processed 
(and 
thus 
assumed 
to 
be 
matured) 
RNA 
transcripts 
to 
genomic 
sequence 
can 
serve 
to 
identify 
intronic 
regions 
which 
were 
removed 
from 
the 
primary 
transcript 
during 
the 
splicing 
process. 
One 
of 
the 
rst 
approaches 
to 
nd 
gene 
regions 
consisted 
of 
aligning 
complete 
(full-length) 
RNA 
transcripts 
against 
genomic 
sequence 
and 
thus 
pinpointing 
the 
location 
of 
genes. 
UniGene 
cluster 
for 
example 
successively 
incorporate 
transcripts 
(mRNA 
and 
EST 
sequences) 
into 
initial 
alignments 
forming 
gene 
centered 
clusters 
of 
spliced 
sequences 
(28). 
Since 
then, 
spliced 
alignments 
have 
been 
included 
into 
several 
gene 
prediction 
pipelines 
(12, 
29) 
as 
for 
example 
the 
Transcript 
Assembly 
Program 
(12). 
They 
constitute 
an 
extrinsic 
or 
indirect 
approach 
to 
gene 
prediction 
as 
contrasted 
by 
ab 
initio 
methods 
which 
make 
directly 
use 
of 
statistical 
properties 
of 
the 
genomic 
sequence 
such 
as 
the 
GC 
content 
(30). 
From 
the 
initial 
estimates 
of 
30%-40% 
of 
the 
human 
genes 
being 
alternatively 
spliced 
(31, 
32), 
by 
improving 
experimental 
and 
computational 
techniques 
this 
estimate 
has 
constantly 
increased 
over 
the 
past 
years, 
ranging 
presently 
at 
more 
than 
70% 
(33, 
34, 
35). 
Along 
with 
the 
improvement 
of 
detecting 
alternative 
splice 
patterns, 
databases 
and 
web 
application 
were 
developed 
for 
storing 
and 
analyzing 
the 
wealth 
of 
data. 
Two 
main 
types 
of 
alternative 
splicing 
data 
repositories 
can 
be 
distinguished: 
i) 
AS 



History 
of 
computational 
analyses 
on 
alternative 
splicing 


databases 
created 
by 
text 
mining, 
storing 
manually 
curated 
or 
collected 
AS 
events 
reported 
in 
the 
literature 
and 
ii) 
computationally 
detected 
and 
annotated 
AS 
databases.(cf. 
Table 
1.1). 


The 
quality 
of 
EST 
sequences 
is 
often 
compromised 
by 
sequencing 
errors. 
Additionally, 
ESTs 
can 
be 
overrepresented 
in 
the 
5. 
or 
3. 
region 
of 
gene, 
posing 
an 
unbalanced 
transcript 
coverage. 
This 
encouraged 
scientists 
to 
explore 
also 
other 
possibilities 
for 
corroborating 
the 
evidence 
on 
alternative 
splice 
patterns. 
One 
strategy 
pursues 
the 
interspecies 
comparison 
of 
splice 
patterns 
to 
conrm 
AS 
events 
by 
their 
sequence 
conservation 
in 
other 
species 
(36, 
37). 
However, 
this 
is 
often 
not 
feasible 
on 
a 
genome 
wide 
scale, 
due 
to 
the 
incomplete 
sequence 
coverage 
in 
non-human 
species. 
Another 
approach 
is 
the 
articial 
construction 
of 
splice 
junction 
probes 
based 
on 
reference 
mRNA 
sequences 
and 
their 
hybridization 
with 
RNA 
cell 
extracts 
(which 
can 
be 
from 
various 
tissues) 
on 
microarray 
chips 
(35, 
38). 
While 
producing 
a 
large 
amount 
of 
information 
conrming 
speci
c 
AS 
patterns 
and 
giving 
insights 
into 
their 
regulation, 
microarray 
and 
high 
throughput 
sequencing 
approaches 
constitute 
a 
considerable 
nancial 
eort. 
Beside 
genomic 
and 
mRNA 
sequence 
information, 
also 
the 
wealth 
of 
protein 
sequences 
has 
been 
used 
to 
predict 
and 
analyze 
AS 
forms 
(39, 
40). 
A 
third 
possibility, 
reported 
by 
Hiller 
et 
al. 
uses 
protein 
family 
domains 
(PFAM) 
to 
reconstruct 
AS 
events 
(41). 
This 
approach 
classies 
novel 
AS 
events 
by 
evaluating 
the 
PFAM 
score 
after 
computationally 
removing 
exons 
or 
retaining 
introns 
in 
reference 
transcript 
structures 
and 
translating 
them 
into 
protein 
sequence. 



Table 
1.1
: 
Historica
l 
outlin
e 
o
f 
alternativ
e 
splicin
g 
databases
. 
AFE=alternativ
e 
rs
t 
exon
; 
ALE=alternativ
e 
las
t 
exon
AP=alternativ
polyadeny
ation. 
Fo
r 
furthe
r 
explanatio
n 
o
f 
acronym
s 
se
e 
reference
s 
an
d 
glossary
; 
n
a 
= 
n
o 
informatio
n 
available
. 


Yea
r 
Databas
e 
Dat
a 
Descriptio
n 
Collecte
d 
Type
s 
o
f 
A
S 
Pat-Numbe
r 
o
A
Sequence
Ref
tern
s 


199
9 


200
0 
200
1 


200
2 


200
2 
200
3 
200
3 
200
4 
200
4 
200
5 
200
5 
200
5 
200
6 
200
6 


200
6 
200
7 


Asd
b 
Collection 
of 
protein 
and 
DNA 
sequence 
entries, 
labeled 
as 
"VARSPLIC” 
(Swiss-Prot) 
and 
"Alternative 
Spliced” 
(Gen-
Bank) 
ASFinde
r 
BLAST 
of 
EST 
against 
cDNA 
sequences 
SpliceD
B 
Collection 
of 
(non)canonical 
splice 
site 
pairs 
from 
GenBank 
sequences 
SpliceNes
t 
Aligning 
ESTs 
from 
UniGene 
cluster 
alignment 
against 
genomic 
sequence 
(Sim4)
; 
ltering 
and 
delinieation 
of 
AS 
patterns 
PalsD
B 
Comparing 
UniGene 
cluster 
transcripts 
ProSplice
r 
Alignment 
EST 
and 
mRNA 
(Sim4
) 
and 
protein 
(TBLASTN) 
sequences 
to 
genomic 
sequence 
(Ensembl
) 
Asa
p 
Mapping 
UniGene 
sequences 
to 
genome 
(BLAST)
; 
analyzing 
EST 
(mRNA) 
cluster 
with 
GeneMiner 
Ease
d 
Aligning 
mRNAs 
to 
dbEST 
(BLAST)
; 
ltering 
and 
mapping 
of 
AS 
mRNAs 
to 
Ensemb
l 
genes 
AS
D 
Manually 
curated 
EST 
(mRNA) 
alignments 
(BLAST) 
against 
genomic 
sequence 
FastD
B 
EST 
(mRNA) 
alignments 
(Sim4
) 
against 
protein 
coding 
gene 
sequences 
(Ensembl
) 
SpliceInf
o 
AS 
forms 
derived 
from 
ProSplice
r 
and 
from 
mRNA-protein 
sequence 
comparison 
to 
genomic 
sequence 
(Ensembl
) 
Maas
e 
Semi-automated 
AS 
analysis 
of 
manually 
provided 
gene 
IDs 
by 
BLAT 
and 
Sim
4 
Astr
a 
Mapping 
of 
mRNAs 
(UniGene) 
to 
genome 
by 
Megablas
t 
and 
Al
n 
Hollywoo
d 
Aligning 
mRNA 
vs 
genomic 
sequence 
(Ensembl)
; 
subsequent 
alignments 
of 
EST 
against 
the 
mRNA 
hits 
(Sim4
) 
within 
the 
genome 
TassD
B 
Aligning 
ESTs 
(BLAST) 
against 
RefSeq 
splice 
site 
junctions 
which 
show 
speci
c 
splice 
cite 
patterns 
AltTran
s 
Extending 
the 
AltSplic
e 
DB 
for 
alternative 
PolyA 
sites 
SpliceMine
r 
Web 
Frontend 
to 
the 
"Evidence 
Viewer” 
DB 
of 
non 
redundant 
human 
splice 
variants 
with 
complete 
CDS 
BiPas
s 
Two-step 
alignments 
of 
EST 
(dbEST) 
and 
mRNA 
(GenBank) 
to 
genomic 
sequence 
by 
BLAST 
and 
Sim
4 


n/
a 


Inserts, 
Deletions 
n/
a 


SE, 
RI, 
A5E, 
A3E 


inserts, 
deletions 
SE, 
A5E, 
A3E 
SE, 
A5E, 
A3E 
Inserts, 
Deletions 
CE, 
A5E, 
A3E, 
SE, 
RI, 
MXE 
CE, 
A5E, 
A3E, 
SE, 
RI 
SE, 
A5E, 
A3E, 
RI 
A5E, 
A3E, 
SE, 
RI, 
MXE 


AFE, 
ALE, 
A5E, 
A3E, 
SE, 
RI, 
MXE 
CE, 
A5E, 
A3E, 
SE, 
RI, 
MXE 


A3E 
(NagNag)
, 
A5E 
(GynGyn
) 


AP 
no 
classication 


AFE 
, 
ALE, 
SE, 
A5E, 
A3E 


1,200 
DNA 
Sequences 


2,747 
AS 
cDNAs 
(1,797 
AS 
genes) 
28,468 
ss 
pairs 


14,900 
AS 
UniGene 
clusters 


14,106 
AS 
genes 
n/
a 


11,717 
AS 
genes 


18,308 
AS 
transcripts 
(14,792 
AS 


genes) 
2,581 
AS 
Events 
(AltExtron)
8,314 
AS 
genes 
(AltSplice


11,071 
AS 
genes 


6,309 
AS 
genes 


1,007 
AS 
genes 


12,470 
AS 
cDNAs 
(4,931 
AS 
genes) 


10,800 
AS 
genesa 


10,995 
GynGyn
11,964 
NagNa
patterns 
2,053 
AP 
genes 
na 


na 


(42, 
43) 


(44) 
(45) 
(46, 
47) 
(48) 
(49) 
(34, 
50, 
51) 


(52) 
(53, 
54) 
(55) 
(56) 
(57) 
(58) 
(3) 
(16) 
(59) 
(60) 
(61) 
Introduction 


5 


a

number 
referring 
to 
AS 
events 
of 
internal 
exons 



Splicing 
Factors 
and 
their 
Role 
in 
Regulating 
Alternative 
Splicing 


1.1.3 
Splicing 
Factors 
and 
their 
Role 
in 
Regulating 
Alternative 
Splicing 
Many 
proteins 
have 
been 
found 
to 
in
uence 
alternative 
splicing 
events. 
Especially 
SR 
proteins, 
a 
family 
of 
proteins 
that 
share 
serine-arginine 
rich 
conserved 
domains, 
are 
frequently 
located 
near 
sites 
of 
active 
splicing. 
These 
proteins 
can 
interact 
via 
their 
RS 
domains 
and 
thus 
provide 
bridging 
functions 
in 
spliceosome 
assembly 
and 
splice 
site 
denition 
(62). 
Furthermore 
SR 
proteins 
possess 
RNA 
recognition 
domains 
and 
have 
been 
shown 
to 
recognize 
a 
variety 
of 
cis-elements. 
According 
to 
location 
and 
eect 
on 
the 
splicing 
process, 
these 
cis-elements 
are 
classied 
as 
exonic 
or 
intronic 
splicing 
enhancer 
(ESE, 
ISE) 
or 
-silencer 
(ESS, 
ISS) 
elements. 
Understanding 
the 
role 
of 
SR 
proteins 
in 
AS 
regulation 
is 
complicated 
by 
the 
fact 
that 
the 
recognized 
sequence 
motif 
alone 
is 
often 
insucient 
to 
determine 
their 
function 
as 
positive 
or 
negative 
splicing 
regulator. 
Studies 
have 
shown 
that 
more 
than 
one 
copy 
of 
a 
high 
anity 
SR 
protein 
binding 
site 
can 
eciently 
activate 
splicing 
as 
was 
shown 
for 
three 
sequential 
SRp 
binding 
motifs 
binding 
ASF/SF2 
and 
SRp40 
(63, 
64). 
This 
nding 
was 
explained 
by 
the 
action 
of 
cooperative 
binding 
of 
several 
splicing 
factors 
of 
the 
same 
type 
resulting 
in 
a 
higher 
specicity 
that 
helps 
in 
outcompeting 
other 
trans-factors 
with 
lower 
RNA 
binding 
anity 
in 
this 
specic 
region. 
Contrary 
to 
the 
single 
type 
factor 
binding 
model 
other 
studies 
have 
shown 
that 
also 
combinations 
of 
dierent 
SR 
proteins 
can 
enhance 
splicing 
by 
recognizing 
and 
binding 
to 
similar 
adjacent 
enhancer 
motifs 
(65). 
A 
variation 
of 
this 
model 
is 
shown 
by 
another 
example 
where 
exon 
5 
and 
6 
of 
the 
human 
caldesmon 
gene 
showed 
multiple 
purine 
rich 
repeats, 
sharing 
at 
least 
32nt, 
which 
promoted 
alternative 
splicing 
of 
an 
internal 
50ss 
within 
exon 
5 
(66). 
Since 
most 
of 
the 
reported 
ESEs 
show 
lengths 
between 
5-19 
nucleotides 
(62), 
longer 
motifs 
may 
consist 
of 
several 
overlapping 
sub 
motifs 
that 
increase 
the 
binding 
anity 
of 
its 
recognizing 
trans-factors 
(67). 
Even 
the 
possibility 
of 
a 
composite 
splice 
regulatory 
element 
has 
been 
demonstrated, 
consisting 
of 
dierent 
motifs 
within 
exon 
5 
of 
an 
transcript 
of 
the 
CD44 
gene. 
These 
motifs 
were 
required 
in 
a 
directly 
adjacent 
location 
to 
activate 
a 
functional 
splice 
complex, 
also 
suggesting 
interactions 
of 
the 
respective 
binding 
proteins 
(68). 



Methods 


1.2 
Methods 
1.2.1 
The 
EASED 
Database 
All 
analyses 
in 
this 
chapter 
are 
based 
on 
the 
Extended 
Alternative 
Spliced 
EST 


"
Database“ 
(Eased)1, 
which 
was 
developed 
as 
a 
repository 
of 
annotated 
putative 
alternative 
splice 
forms 
(52). 
The 
database 
uses 
mRNA 
and 
gene 
sequence 
material 
of 
the 
Ensembl 
database2 
version 
19.34a 
and 
ESTs 
from 
the 
dbEST 
database3 
(December 
2003) 
(69) 
and 
is 
currently 
restricted 
to 
the 
species 
Homo 
sapiens. 
The 
information 
is 
organized 
in 
a 
top-down 
order 
from 
genes 
via 
transcripts 
to 
individual 
alternative 
splice 
events 
and 
their 
associated 
features. 
In 
general 
the 
identication 
of 
alternative 
splice 
forms 
(asf) 
is 
based 
on 
an 
mRNA 
-EST 
alignments 
with 
a 
stringent 
set 
of 
parameters 
(see 
1.2.2). 
The 
Eased 
project 
constitutes 
one 
of 
the 
earliest 
eorts 
to 
combine 
an 
algorithm 
for 
locating 
alternative 
splice 
forms 
with 
annotation 
of 
a 
variety 
of 
secondary, 
but 
medically 
relevant 
information 
such 
as 
associated 
diseases 
(for 
example 
cancer), 
prevalence 
for 
a 
specic 
tissue 
or 
developmental 
state(52). 
An 
overview 
on 
the 
performed 
analyses 
is 
given 
in 
Figure 
1.1. 


1.2.2 
Locating 
Alternative 
Splice 
Forms 
Spliced 
transcripts, 
present 
as 
ESTs 
or 
fully 
sequenced 
mRNAs, 
are 
composed 
of 
exons 
after 
excision 
of 
introns 
from 
a 
transcribed 
precursor 
transcript. 
Hence, 
an 
alignment 
of 
mRNAs 
against 
the 
genomic 
sequence 
reveals 
intronic 
regions 
as 
gaps 
within 
the 
spliced 
(mature) 
mRNA 
(Figure 
1.2). 
Compared 
to 
full-length 
mRNAs, 
much 
ore 
ESTs 
are 
available, 
providing 
snapshots 
of 
the 
outcome 
of 
dierent 
splicing 
processes. 
Since 
a 
direct 
alignment 
of 
ESTs 
against 
genomic 
sequence 
is 
in 
many 
ways 
impractical, 
because 
of 
the 
large 
search 
space 
and 
possibility 
of 
unspecic 
hits, 
the 
available 
Ensembl 
transcripts 
(31.500 
as 
of 
Ensembl 
version 
19.34a) 
were 
used 
to 
restrict 
the 
analysis 
to 
genomic 
regions 
of 
known 
genes. 
Thus, 
full 
length 
transcripts 
were 
aligned 
against 
all 
available 
human 
ESTs 
from 
dbEST 
(5.5 
Million 
as 
of 
December 
2003), 
using 
the 
WUBLAST 
program4 
. 
In 
order 
to 
prevent 
unspecic 
hits 
and 
to 
reduce 
the 
compu


1http://eased.bioinf.mdc-berlin.de 
2http://www.ensembl.org 
3http://www.ncbi.nlm.nih.gov/dbEST 
4http://blast.wustl.edu/ 



Locating 
Alternative 
Splice 
Forms 


HsPreprocessing:
-filtering GT..AG-sequence extractionGenes.pmTranscripts.pmAsfs.pmStore.pmextraction of genomic 
sequence around ass and 
rss up to window size NPre-Analysis:
-data classification: 
ass vs. rsstotal splice site score, 
total transcript evidenceTranscript EvidenceAnalysesSplice Site ScoreEnhancer Motif 
Occurrencesubclasses:
E5, E3, I5, I3EASEDAS_Projekt124.03.2008
Figure 
1.1: 
Work
ow 
of 
data 
preparation 
and 
analyses 
performed 
with 
Eased 
dataset. 
The 
grey 
box 
to 
the 
right 
indicates 
the 
Perl 
modules 
programmed 
for 
pre-processing 
the 
Eased 
data. 
Donor 
and 
acceptor 
splice 
site 
context 
were 
partitioned 
into 
E3=exon 
3. 
end, 
I3=intron 
5. 
end, 
I3=intron 
3. 
end 
and 
E5=exon 
5. 
end. 


tational 
load, 
known 
repetitive 
sequences 
as 
collected 
in 
RepBase5) 
were 
masked 
in 
all 
mRNA 
transcripts 
prior 
to 
the 
alignments. 
The 
longest 
available 
Ensembl 
transcript 
of 
each 
gene 
locus 
provided 
an 
initial 
exon 
intron 
annotation, 
denoting 
a 
set 
of 
constitutive“ 
or 
reference 
donors 
and 
acceptors. 
Taking 
the 
longest 


” 
mRNA 
as 
a 
reference 
holds 
the 
chance 
to 
observe 
a 
maximal 
number 
of 
alignment 
gaps 
against 
the 
available 
EST 
sequences 
and 
hence, 
to 
increase 
the 
number 
of 
identied 
alternative 
splice 
junctions. 
Each 
mRNA-EST 
alignment 
was 
ltered 
against 
a 
stringent 
set 
of 
quality 
criteria. 
The 
aligning 
blocks 
(hsps) 
had 
to 
show 
at 
least 
98% 
identity 
over 
a 
length 
of 
100 
nt 
to 
qualify 
as 
exons 
of 
further 
use 
(see 
details 
in 
(52)). 
Dierences 
between 
exon 
structures 
of 
ESTs 
and 
the 
exon 
locations 
of 
the 
reference 
transcripts 
were 
used 
as 
indicators 
of 
alternative 
splice 


5http://www.girinst.org/repbase 



Methods 


GTSKIPINSERTEabcdE1E4E3E2efghAB
Figure 
1.2: 
Modeling 
alternative 
splice 
patterns 
by 
transcript 
alignments: 
(A) 
a 
fulll
ength 
reference 
transcript 
(T 
) 
with 
known 
exon-intron 
structure 
serves 
as 
anchoring 
region 
within 
the 
genome 
(G); 
(B) 
ESTs 
(E) 
are 
aligned 
against 
T 
to 
annotate 
alternative 
splicing 
patterns. 
Two 
main 
types 
of 
alternative 
splice 
events 
are 
distinguished 
in 
the 
Eased 
database 
which 
are 
SKIP 
and 
INSERT 
events 
(dened 
relative 
to 
the 
reference 
transcript). 
SKIP 
events 
are 
further 
distinguished 
into 
the 
AS 
pattern 
a-d 
resulting 
in 
deleted 
exonic 
sequence 
relative 
to 
T 
. 
INSERT 
events 
fall 
into 
categories 
e-h 
showing 
dierent 
pattern 
of 
inserted 
exonic 
sequence. 
Each 
AS 
type 
creates 
relative 
to 
T 
one 
ore 
more 
alternative 
splice 
sites 
(ass); 
splice 
sites 
in 
T 
and 
E 
mapping 
to 
the 
same 
position 
in 
G 
are 
considered 
as 
reference 
splice 
sites 
(rss); 
exonic 
regions 
in 
the 
genome 
are 
labeled 
E1-E4; 
alternative 
parts 
as 
indicated 
by 
the 
transcript 
alignments 
are 
shown 
in 
pink 
and 
red 
color 
if 
they 
are 
missing 
or 
inserted 
relative 
to 
T 
, 
respectively 


sites. 
According 
to 
the 
alignments, 
exon 
boundaries 
diering 
between 
ESTs 
and 


the 
reference 
transcripts 
were 
treated 
as 
alternative 
splice 
sites 
while 
the 
remain


ing 
exon 
boundaries 
being 
conform 
with 
the 
reference 
transcript 
where 
classied 


as 
reference 
splice 
sites. 
Hence, 
all 
EST 
boundaries 
matching 
a 
reference 
splice 


site 
were 
counted 
as 
evidence 
for 
the 
set 
of 
reference 
donor 
and 
acceptor 
sites. 


These 
splice 
sites 
are 
often 
labelled 
constitutive\. 
However, 
present 
lack 
of 
evi


” 
dence 
of 
AS 
near 
a 
constitutive 
splice 
site 
does 
not 
preclude 
that 
an 
alternative 


splice 
site 
becomes 
activated 
in 
this 
region 
under 
specic 
conditions. 
Being 
aware 


of 
this 
uncertainty, 
in 
the 
following 
I 
will 
refer 
to 
these 
splice 
sites 
as 
reference“ 


” 
instead 
of 
constitutive\. 


” 



Data 
Preparation 
and 
Renement 


1.2.3 
Data 
Preparation 
and 
Renement 
For 
creating 
the 
initial 
dataset, 
the 
database 
was 
queried 
with 
a 
threshold 
of 
at 
least 
5 
asf 
(EST) 
per 
gene 
to 
increase 
the 
chance 
of 
capturing 
a 
true 
alternative 
splice 
event. 
Next, 
all 
genes 
whose 
supporting 
EST 
did 
not 
indicate 
alternative 
splice 
events 
utilizing 
the 
canonical 
splice 
site 
dinucleotides 
GT..AG, 
were 
discarded. 
After 
renement 
the 
dataset 
consisted 
of 
2624 
genes 
with 
an 
average 
number 
of 
8 
alternative 
splice 
sites 
per 
gene 
and 
an 
average 
support 
of 
3 
EST 
per 
alternative 
splice 
site. 
In 
contrast, 
26 
ESTs 
per 
gene 
were 
indicative 
of 
reference 
splice 
sites. 
Due 
to 
the 
ltering 
steps 
the 
number 
of 
initially 
available 
ESTs 
and 
annotated 
splice 
events 
decreased 
considerably 
as 
documented 
in 
Box 
1.2.1. 
As 
a 
further 
parameter, 
the 
genomic 
sequence 


Box 
1.2.1 
EASED 
gures 


window 
around 
each 


Filtering 
steps 
and 
discarded 
sequences 
from 
the 
initial 


donor 
and 
acceptor 
site

towards 
the 
nal 
dataset; 
(*) 
number 
of 
asf 
satisfying 
pred
iction 
criteria 
(**) 
number 
of 
asf 
strictly 
meeting 
the 
cri


was 
set 
to 
50 
nt 
up

terion 
of 
canonical 
GT..AG 
splice 
sites 


and 
downstream 
of 
the 
Processing 
Step 
Number 
Source 
splice 
site 
(Figure 
1.3).
blasted 
ESTs 
5,427,257 
dbEST 
Database 
Ensembl 
Genes 
(ENSG) 
23,531 
Ensembl 
Database 
The 
window 
size 
was 
(19.34a) 
chosen 
for 
locating 
known 
Ensembl 
Transcripts 
31,609 
(ENST) 
splice 
regulatory 
motifs 
Ensembl 
Exons 
(ENSE) 
225,897 
and 
restricted 
to 
50 
nt 
EST, 
matching 
= 
1 
ENST 
3,947,548 
after 
inspecting 
the 
exon 
asf 
predicting 
EST* 
428,474 
predicted 
as 
sites 
102,104 
Eased 
Database 
length 
distribution 
of 
ENST 
matching 
= 
1 
asf 
ENSG 
matching 
= 
1 
asf 
21,044 
15,426 
transcripts 
present 
in 
ENSG 
matching 
= 
5 
asf 
2624 
the 
Eased 
database. 


assAcceptors** 
3862 
More 
than 
70% 
(90%) 
assDonors** 
rssAcceptors** 
3705 
25526 
Perl 
scripts 
of 
the 
represented 
exons 
rssDonors** 
25103 
(introns) 
are 
longer 
than 


100 
nt 
(1.2), 
allowing 


to 
span 
a 
window 
of 
50 
nt 
into 
5. 
and 
3. 
direction) 
around 
each 
splice 
site 
which 
for 
the 
majority 
of 
exons 
and 
introns 
does 
not 
overlap 
between 
the 
donor 
and 
acceptor 
site. 
These 
clearly 
separable 
regions 
were 
used 
for 
the 
enhancer 
motif 
analysis. 
Finally 
each 
splice 
site 
was 
stored 
as 
alternative-(ass) 
or 
reference 
(rss) 
splice 
site 
together 
with 
a 
number 
of 
supporting 
transcripts 
(mRNA 
and/or 
EST). 
The 
main 
relationship 
one 
gene 
. 
n 
complete 
transcripts 
(mRNA) 
. 
m 
partial 



Methods 
11 


transcripts 
(ESTs) 
. 
x 
alternative 
splice 
events 
is 
re
ected 
in 
the 
Eased 
table 
structure 
(Figure 
A.1, 
Table 
A.1). 
Supporting 
tables 
contain 
additional 
data 
as 
for 
example 
corresponding 
protein 
information 
on 
mRNAs 
or 
tissue 
and 
developmental 
information 
on 
ESTs. 
The 
analyses 
were 
restricted 
to 
Homo 
sapiens 
as 
this 
species 
provides 
the 
most 
abundant 
sequence 
information. 
Data 
for 
subsequent 
analyses 
was 
arranged 
and 
complemented 
via 
Perl 
scripts 
and 
the 
Eased 
database 
tables 
(Table 
A.1) 


Table 
1.2: 
Number 
and 
lengths 
(L) 
of 
Eased 
cDNA 
exons 
and 
introns 


Counts 
Exons 
Introns 
total 
L 
< 
100 
nt 
L 
= 
100 
nt 
mean 
(L) 
219,388 
64,062 
155,326 
(71%) 
262 
189,145 
15,760 
173,385 
(92%) 
5564 
GT..AG 
GC..AG 
-
-
169,732 
(97.9%) 
422 
(0.24%) 


1.2.4 
Splice 
Site 
Scoring 
In 
order 
to 
compare 
dierent 
splice 
sites, 
it 
is 
reasonable 
to 
apply 
a 
measure 
of 
information. 
The 
information 
content 
can 
be 
expressed 
as 
entropy, 
which 
is 
equivalent 
to 
the 
uncertainty 
to 
observe 
a 
specic 
event, 
for 
example, 
a 
specic 
nucleotide 
within 
a 
sequence 
motif. 
The 
entropy 
is 
calculated 
via 
probabilities, 
re
ecting 
an 
increasing 
certainty 
to 
observe 
a 
specic 
event. 
The 
more 
probable 
the 
event, 
the 
lower 
the 
uncertainty 
or 
entropy. 
Thus 
the 
entropy 
becomes 
close 
to 
zero 
if 
there 
is 
a 
high 
certainty 
about 
the 
event. 
For 
example, 
position 
+1 
and 
+2 
in 
eukaryotic 
splice 
sites 
are 
to 
>98% 
conserved 
to 
the 
bases 
guanine 
and 
thymine 
(33), 
lowering 
the 
uncertainty 
about 
this 
dinucleotide 
at 
human 
donor 
positions 
to 
0.14. 
According 
to 
the 
maximum 
entropy 
principle, 
of 
all 
possible 
distributions 
in 
the 
hypothesis 
space, 
the 
distribution 
that 
is 
the 
best 
approximation 
of 
the 
true 
distribution 
given 
what 
is 
known, 
is 
the 
one 
with 
the 
largest 
Shannon 
entropy 
(Equation 
1.1, 
(23)): 


H 
= 
- 
p 
· 
logb(p) 
(1.1) 


where 
p 
is 
the 
probability 
to 
observe 
an 
event. 
For 
example, 
the 
four 
bases 
A, 
C, 
G, 
T 
that 
are 
possible 
at 
each 
splice 
site 
position 
represent 
k 
observable 
events 
and 
the 
absolute 
entropy 
of 
a 
splice 
site 
motif 
of 
length 
k 
corresponds 
to 



Splice 
Site 
Scoring 


an 
information 
content 
I 
(Equation 
1.2). 
Consequently, 
splice 
site 
information 
scores 
are 
expressed 
in 
units 
of 
bits. 


k

I 
= 
- 
pi 
· 
log2(pi)[Bit] 
(1.2) 


i=1 


Often 
one 
does 
know 
only 
little 
about 
the 
true 
nature 
of 
a 
given 
set 
of 
sequences 
and 
has 
to 
compare 
the 
splice 
sites 
of 
dierent 
sets 
in 
order 
to 
capture 
meaningful 
and 
interpretable 
dierences. 
In 
this 
case 
it 
is 
more 
practical 
to 
calculate 
the 
relative 
entropy, 
also 
termed 
transinformation 
or 
Kullback-Leibler 
divergence 
(70), 
which 
estimates 
the 
distance“ 
between 
an 
observed 
(p) 
to 
an 
expected 
(q)

” 
frequency 
distribution 
(Equation 
1.3). 
The 
background 
distribution 
was 
taken 
as 
(q1;q2;q3;q4)= 
fA, 
G, 
C, 
T 
} 
= 
(0:3, 
0:2, 
0:2, 
0:3), 
owing 
to 
a 
GC 
content 
of 
41% 
on 
average 
observed 
in 
the 
human 
genome 
(33). 


k

pi

KL 
= 
pi 
· 
log2() 
(1.3) 
qi

i=1 


Finally, 
the 
information 
content 
of 
a 
splice 
site 
or 
sequence 
motif 
is 
the 
sum 
of 
KL 
over 
all 
considered 
sequence 
positions 
(Equation 
1.4) 
which 
for 
the 
donor 
site 
comprises 
n=9 
nucleotides 
(position 
-3 
to 
+6 
in 
exon-intron 
orientation) 
and 
n=23 
nucleotides 
(position 
-20 
to 
+3) 
for 
acceptor 
sites. 


n

S 
= 
KLj 
(1.4) 
j=1 


Measuring 
the 
splice 
site 
information 
content 
can 
be 
further 
improved 
by 
including 
marginal 
constraints. 
These 
can 
be 
imposed 
through 
neighborhood 
relations 
between 
nucleotide 
positions 
and 
their 
observed 
frequencies 
estimated 
from 
experimental 
data 
(23). 
This 
scoring 
scheme 
is 
implemented 
in 
the 
program 
MaxE
ntScan6 
and 
was 
used 
to 
calculate 
the 
splice 
site 
scores 
in 
all 
analyses. 
Alternative 
splice 
sites 
were 
treated 
as 
a 
joint 
dataset, 
that 
is 
not 
further 
separated 
into 
up-and 
downstream 
site 
per 
acceptor 
and 
donor. 
The 
score 
distribution 
of 
alternative 
donor 
and 
acceptor 
sites 
was 
compared 
to 
the 
respective 
set 
of 
reference 
splice 
sites, 
using 
the 
Wilcoxon 
rank-sum 
test. 


6http://genes.mit.edu/burgelab/maxent/ 



Methods 
13 


1.2.5 
Scanning 
for 
Splice-Enhancing 
Motifs 
To 
estimate 
the 
frequency 
of 
high 
scoring 
exon 
splicing 
enhancer 
(ESE) 
motifs 
within 
a 
dened 
context 
around 
the 
investigated 
splice 
sites, 
scoring 
matrices 
implemented 
in 
the 
software 
EseFinder 
(27) 
were 
used 
. 
The 
matrices 
contain 
nucleotide 
frequencies 
of 
short 
motifs 
that 
were 
shown 
to 
bind 
the 
SR 
proteins 
SF2/ASF 
(7mer), 
SC35 
(8mer), 
SRp40 
(7mer) 
and 
SRp55 
(6mer). 
Experiments 
have 
demonstrated 
the 
applicability 
of 
ESE 
consensus 
motifs 
in 
explaining 
splice 
events 
as 
exon 
skipping 
and 
mis-splicing 
due 
to 
point 
mutations 
within 
those 
motifs 
(71). 
Nevertheless, 
due 
to 
the 
degenerated 
nature 
of 
SRp 
binding 
motifs, 
the 
frequency 
of 
ESE 
motif 
hits 
was 
not 
considered 
alone 
but 
their 
occurrence 
in 
the 
context 
of 
additional 
characteristic 
information 
(splice 
site 
score, 
transcript 
support) 
evaluated. 
Binding 
motifs 
for 
these 
four 
SR 
proteins 
were 
scanned 
in 
a 
window 
of 
100 
nucleotides, 
composed 
of 
a 
two 
50 
nt 
regions 
up-and 
downstream 
of 
the 
predicted 
ass 
and 
rss 
(Figure 
1.3). 
Especially 
when 
parts 
of 
introns 
are 


NNN[N]GT45´ssmotif3´ssmotifIntronExonExonE3E5I5I350nt50nt50nt50nt[N]NNN20AG
Figure 
1.3: 
Sequence 
windows 
used 
for 
determining 
the 
ESE 
frequencies 
around 
the 
scored 
splice 
sites. 
The 
four 
validation 
classes 
are 
dened 
as 
I5, 
I3, 
E5 
and 
E3 
with 
the 
following 
convention: 
E 
= 
exon, 
I 
= 
intron, 
3 
= 
downstream 
region 
(3), 
5 
= 
upstream 
region(5). 
Each 
sequence 
region 
spanned 
50 
nt; 
intron-exon 
borders 
are 
dened 
by 
the 
donor-and 
acceptor 
splice 
site 
motifs 
which 
are 
also 
used 
for 
the 
splice 
site 
scoring. 


spliced 
into 
the 
mature 
transcript, 
one 
can 
expect 
to 
nd 
ESE 
motifs 
at 
both 
sides 
(up-and 
downstream) 
of 
splice 
sites. 
However, 
the 
scan 
window 
was 
kept 
with 
100 
nucleotides 
in 
a 
size 
that 
covers 
a 
distance 
to 
the 
splice 
site 
in 
which 
experimental 
data 
have 
shown 
the 
location 
of 
ESEs 
(72, 
73). 
Reducing 
the 
window 
size 
may 
certainly 
cause 
the 
loss 
of 
regulatory 
motifs 
for 
the 
analyses 
but 
enables 
to 
include 
more 
and 
especially 
shorter 
introns 
into 
the 
analyses 
(cf. 
Table 
1.2) 
while 
maintaining 
non-overlapping 
scan 
windows. 
There 
exists 
cases 
where 
regulatory 
motifs 
are 
located 
more 
than 
100 
nucleotides 
downstream 
of 
a 
donor 
site 
(74) 
and 
it 
is 
known 
that 
proximity 
of 
motifs 
to 
its 
target 
splice 
sites 
is 
governed 
by 
the 
RNA 
structure 
(75). 
However, 
looking 
in 
the 
close 
sequence 
proximity 
of 
splice 
sites 
should 
increase 
the 
chance 
to 
nd 
splice 
enhancer 
motifs 
regulating 
exactly 
the 
alternative 
splice 
site 
under 
scrutiny. 



Data 
Partitioning 
for 
Statistical 
Analysis 


1.2.6 
Data 
Partitioning 
for 
Statistical 
Analysis 
As 
shown 
in 
Figure 
1.3, 
four 
classes 
(data 
subsets 
or 
partitions) 
were 
created 
from 
the 
Eased 
data. 
These 
comprise 
the 
sets 
of 
donor 
(acceptor) 
splice 
sites 
of 
ass 
and 
rss 
splice 
forms. 
Due 
to 
the 
up-and 
downstream 
distinction 
of 
the 
splice 
site 
environment, 
the 
data 
subsets 
can 
also 
be 
analyzed 
with 
respect 
to 
dierences 
between 
exon 
and 
intron 
specic 
features 
as 
for 
example 
the 
frequency 
of 
SR 
protein 
binding 
motifs. 



Results 
15 


1.3 
Results 
1.3.1 
Analysis 
of 
EASED 
Splice 
Forms 
Up 
to 
now 
no 
perfect 
ab 
initio 
prediction 
algorithm 
for 
alternative 
splice 
events 
exists 
and 
an 
observed 
sequence 
feature 
if 
taken 
isolated 
may 
not 
be 
strong 
enough 
to 
validate 
a 
splice 
site 
to 
certainty. 
Based 
on 
the 
Eased 
database 
(52) 
dierent 
attributes 
were 
compared 
between 
putative 
alternative 
and 
constitutive 
splice 
forms. 
Genomic 
sequence 
features 
that 
are 
characteristic 
for 
spliced 
transcripts 
were 
used 
to 
evaluate 
the 
available 
set 
of 
annotated 
alternative 
splice 
sites. 
A 
central 
question 
was, 
whether 
multiple 
ESEs 
or 
enhancer 
repeats 
may 
contribute 
to 
the 
discrimination 
of 
these 
ass. 
The 
combination 
of 
several 
classes 
of 
information 
such 
as 
splice 
site 
score 
(Sss), 
frequency 
of 
(splice 
enhancing) 
SRp 
binding 
motifs 
(fese), 
transcript 
support 
(ft), 
showed 
to 
be 
a 
strong 
ensemble 
for 
dierentiating 
between 
the 
processes 
of 
both 
constitutive 
and 
alternative 
splicing. 
Given 
the 
fact 
that 
a 
higher 
transcript 
coverage 
(EST, 
mRNA) 
exists 
for 
the 
set 
of 
reference 
splice 
sites, 
their 
attributes 
are 
a 
reliable 
and 
informative 
source 
for 
the 
comparative 
description 
of 
putative 
alternative 
splice 
forms. 
Additionally, 
annotations 
inherent 
to 
EST 
records 
such 
as 
tissue 
type, 
disease 
and 
developmental 
state 
have 
previously 
been 
shown 
to 
discriminate 
reference 
from 
alternative 
splicing 
(13) 
and 
thus 
can 
be 
considered 
as 
supportive 
criterions 
for 
multiple-feature“ 


"

analysis 
strategies. 


1.3.2 
General 
Characterization 
of 
Splice 
Site 
Attributes 
Splice 
Site 
Score 


Comparison 
of 
the 
main 
attributes 
splice 
site 
score, 
transcript 
support 
and 
ESE 
frequency 
was 
done 
rst 
for 
the 
complete 
distribution 
of 
both 
splice 
site 
types 
(donors/acceptors) 
independent 
from 
partitioning 
into 
reference 
(rss) 
and 
alternative 
(ass) 
splice 
sites. 
The 
results 
indicate 
that 
the 
Sss 
between 
the 
whole 
unpartitioned 
distributions 
of 
donor 
and 
acceptor 
site 
vary 
signicantly 
(p< 
10..3 
, 
Wilcoxon-, 
t-and 
f-test) 
in 
their 
means 
and 
variances. 
The 
median 
of 
both 
distributions 
resides 
at 
Sss 
of 
~ 
8.5 
bit 
but 
donor 
splice 
sites 
tend 
to 
lower 
scores 
above 
the 
median 
compared 
to 
acceptor 
sites 
(Table 
1.3a). 



General 
Characterization 
of 
Splice 
Site 
Attributes 


Table 
1.3: 
Distributional 
characteristics 
of 
splice 
site 
score 
(Sss)(a) 
and 
transcript 
supp
ort 
(ft)(b) 
compared 
between 
donor 
and 
acceptor 
splice 
sites 


(a) 
Splice 
site 
score 
(b) 
Transcript 
support 
50ss 
30ss 
50ss 
30ss 


Minimum 
-44,399 
-46,658 
Minimum 
1 


1 
1st 
Quantile 
6,640 
6,483 
1st 
Quantile 
1 


1 
Median 
8,456 
8,450 
Median 
16 
16 
Mean 
6,976 
7,671 
Mean 
41 
37 
3rd 
Quantile 
9,652 
10,141 
3rd 
Quantile 
68 
63 
Maximum 
11,807 
15,589 
Maximum 
466 
326 


Transcript 
Support 


The 
number 
of 
transcripts 
that 
support 
donor 
and 
acceptor 
splice 
sites 
(ft 
= 
frequency 
of 
transcripts, 
that 
utilize 
a 
specic 
splice 
site) 
show 
dierent 
distributions 
in 
particular 
at 
higher 
transcript 
numbers 
per 
splice 
site 
(Figure 
1.4b). 
Means 
and 
variances 
of 
the 
splice 
site 
type 
dependent 
ft 
distributions 
vary 
signicantly 
(p< 
10..3) 
with 
a 
tendency 
to 
more 
transcripts 
at 
donor 
splice 
sites. 
The 
mode 
of 
both 
donor 
and 
acceptor 
ft 
distributions 
reside 
between 
1 
and 
20 
transcripts. 
The 
tendency 
of 
higher 
transcript 
support 
of 
donor 
sites 
means 
that 
more 
ESTs 
(mRNAs) 
show 
a 
clean 
donor 
site 
without 
a 
downstream 
acceptor 
site 
(transcript 
containing 
the 
rst 
exon) 
than 
vice 
versa 
a 
clean 
acceptor 
without 
an 
upstream 
donor 
site 
(transcripts 
containing 
the 
last 
exon) 
when 
mapped 
to 
the 
genomic 
sequence. 
This 
is 
indicative 
of 
partial 
transcripts 
which 
more 
often 
locate 
in 
the 
upstream 
than 
in 
the 
downstream 
region 
of 
a 
gene. 


(a) 
(b) 
(c) 
-3.5-112.545.578.510.5Splice Site ScoreFrequency01000200030004000
0.523.556.589.511.514Splice Site ScoreFrequency01000200030004000
5203550658095115135#Transcripts / acceptor splice siteFrequency0200040006000800010000
Figure 
1.4: 
Histograms 
of 
(a) 
donor 
splice 
site 
scores 
(Sss) 
and 
(b) 
acceptor 
splice 
site 
scores; 
(c) 
histogram 
of 
transcript 
frequencies 
(ft) 
conrming 
the 
analysed 
acceptor 
sites. 
Each 
bin 
shows 
how 
many 
splice 
sites 
are 
conrmed 
by 
the 
given 
number 
of 
observed 
transcripts. 



Results 
17 


Frequency 
of 
SRp 
Binding 
Motifs 


In 
investigating 
the 
repeated 
occurrence 
of 
SRp 
binding 
motifs 
(fese), 
at 
rst 
the 
total 
number 
of 
detected 
motifs 
were 
considered. 
Comparison 
of 
this 
combined 
ESE 
frequencies 
showed 
no 
signicant 
dierence 
(p> 
0:1, 
t-test) 
between 
the 
distributions 
in 
the 
exonic 
donor 
(E3)-and 
acceptor 
(E5) 
region 
but 
a 
signicant 
dierence 
between 
the 
intronic 
donor 
(I5)-and 
acceptor 
(I3) 
region 
(p< 
10..4). 
Also 
the 
occurrence 
of 
ESE 
motifs 
between 
adjacent 
exonic 
(E5) 
and 
intronic 
(I3) 
splice 
site 

anks 
diered 
signicantly 
at 
acceptor 
sites 
(p< 
10..3). 
Looking 
at 
the 
motifs 
of 
the 
specic 
SR 
protein 
ASF/SF2 
one 
observes 
similar 
fese 
distributions 
in 
the 
exonic 
part 
of 
5-and 
3´ 
splice 
sites 
but 
though 
they 
show 
signicant 
dierence 
in 
their 
variances 
(p< 
10..4), 
their 
means 
are 
not 
dierent 
(p> 
0:1, 
Wilcoxon 
test). 
In 
contrast, 
the 
number 
of 
ASF/SF2 
motifs 
in 
the 
intron 

anks 
of 
the 
splice 
sites 
vary 
signicantly 
in 
their 
variances 
and 
means 
(p< 
10..4) 
and 
the 
same 
eect 
was 
found 
between 
adjacent 
exonic 
and 
intronic 
splice 
site 
environments. 
A 
summary 
of 
variances 
and 
means, 
characterizing 
the 
fese 
distributions 
of 
all 
four 
tested 
SR 
proteins 
is 
listed 
in 
Table 
1.4. 


Table 
1.4: 
Summary 
of 
ESE 
motif 
frequency 
(fese) 
characteristics 
considering 
the 
whole 
set 
of 
splice 
site 
while 
distinguishing 
the 
intronic 
and 
exonic 
parts 
of 
donor 
and 
acceptor 
splice 
sites; 
p(; 
) 
denotes 
p-values 
in 
comparing 
means 
and 
variances 
between 
the 
motif 
frequencies 
at 
the 
specied 
exonic/intronic 
region; 
p-values 
of 
signicantly 
(p 
0.0001) 
dierent 
motif 
frequencies 
between 
the 
compared 
splice 
site 
regions 
are 
marked 
with 
(*) 


SRp 
Splice 
Site 
Region 
Median 
Mean 
Variance 
E3 
p(; 
2) 
I3 
p(; 
2) 
ASF/SF2 
E3 
E5 
I3 
I5 
4 
4 
2 
3 
3,934 
3,933 
2,502 
3,359 
5,285 
5,016 
4,229 
6,298 
– 
< 
1; 
< 
10..4* 
– 
< 
10..4* 
; 
< 
10..4* 
– 
< 
10..4* 
; 
< 
10..4* 
– 
< 
10..4* 
; 
< 
10..4* 
SC35 
E3 
E5 
I3 
I5 
4 
4 
3 
3 
3,901 
3,873 
2,928 
3,622 
3,989 
3,949 
3,467 
4,560 
– 
< 
10..1; 
< 
1 
– 
< 
10..4* 
; 
< 
10..4* 
– 
< 
10..4* 
; 
< 
10..4* 
– 
< 
10..4* 
; 
< 
10..4* 
SRp40 
E3 
E5 
I3 
I5 
4 
4 
3 
3 
3,796 
3,794 
2,823 
3,452 
3,145 
3,095 
2,522 
3,183 
– 
< 
1; 
< 
1 
– 
< 
10..4* 
; 
< 
1 
– 
< 
10..4* 
; 
< 
10..4* 
– 
< 
10..4* 
; 
< 
10..4* 
SRp55 
E3 
E5 
I3 
I5 
2 
2 
1 
2 
2,344 
2,334 
1,626 
1,947 
2,277 
2,236 
1,612 
1,965 
– 
< 
1; 
< 
1 
– 
< 
10..4* 
; 
< 
10..4* 
– 
< 
10..4* 
; 
< 
10..4* 
– 
< 
10..4* 
; 
< 
10..4* 



18 
Characterizing 
Specic 
Attributes 
of 
Alternative 
and 
Reference 
Splice 
Sites 


Occurrence 
of 
SRp 
Binding 
Motifs 
at 
Low 
and 
High 
Scoring 
Splice 
Sites 


Additionally, 
and 
in 
order 
to 
validate 
fese 
independently 
from 
our 
denition 
of 
ass 
and 
rss, 
all 
splice 
sites 
were 
separated 
by 
their 
Sss 
distributions 
(Figure 
1.4). 
Furthermore, 
only 
the 
tails 
of 
the 
score 
distribution 
(< 
0 
and 
> 
10) 
were 
selected. 
Beside 
the 
question 
whether 
the 
fese 
distribution 
vary 
signicantly 
between 
the 
low 
and 
high 
scoring 
sets, 
this 
served 
also 
to 
measure 
the 
enrichment 
of 
predicted 
asf 
in 
both 
score 
dependent 
data 
subsets 
(discussed 
in 
the 
section 
of 
ass 
and 
rss 
specic 
analyses). 
The 
total 
donor/acceptor 
splice 
sites 
partition 
in 
6%=4% 
with 
Sss 
< 
0 
contrasted 
by 
19%=25% 
with 
a 
score 
above 
10 
and 
thus 
almost 
consensus 
quality. 
Between 
these 
two 
sets 
of 
extreme-scoring 
splice 
sites 
signicant 
dierences 
in 
the 
means 
of 
their 
total 
(exonic) 
fese 
distributions 
were 
found 
. 
In 
particular, 
at 
both 
donor-and 
acceptor 
sites 
the 
motif 
distribution 
of 
the 
protein 
SC35 
tends 
on 
average 
to 
at 
least 
one 
motif 
more 
at 
low 
scoring 
than 
at 
high 
scoring 
splice 
sites. 
For 
two 
other 
SR 
proteins 
this 
eect 
was 
found 
only 
either 
at 
donor 
splice 
sites 
(SRp55) 
or 
at 
acceptor 
splice 
sites 
(ASF/SF2). 


1.3.3 
Characterizing 
Specic 
Attributes 
of 
Alternative 
and 
Reference 
Splice 
Sites 
Splice 
Site 
Score 
and 
Transcript 
Support 


Comparing 
Sss 
between 
reference 
and 
alternative 
donors 
shows 
a 
signicant 
difference 
in 
the 
mean 
values 
(p< 
10..4 
, 
Wilcoxon 
test), 
although 
the 
mode 
of 
the 
distribution 
still 
resides 
at 
scores 
between 
5 
and 
10. 
The 
same 
observation 
applies 
for 
the 
acceptor 
sites. 
Interestingly, 
the 
dierence 
of 
the 
means 
between 
donor-and 
acceptor 
score 
distributions 
is 
higher 
in 
ass 
than 
in 
rss 
(underlined 
in 
Table 
1.5). 
Considering 
the 
number 
of 
transcripts 
that 
support 
the 
splice 
sites, 
the 
dened 
rss 
are 
in 
generally 
better 
covered 
with 
transcripts 
than 
the 
predicted 
ass 
(at 
both 
donor 
and 
acceptor 
sites). 
The 
mode 
of 
both 
distributions 
can 
be 
found 
on 
a 
class 
level 
of 
1-20 
transcripts 
though 
the 
overall 
number 
of 
transcripts 
is 
approximately 
six 
times 
higher 
in 
the 
respective 
class 
level 
of 
the 
reference 
dataset. 
Between 
score 
and 
transcript 
support 
a 
strong 
dependency 
in 
both 
ass 
and 
rss 
(p< 
10..4 
, 
2-test) 
were 
found 
whereupon 
better 
scoring 
splice 
sites 
clearly 
show 
a 
better 
transcript 
support. 



Results 
19 


Table 
1.5: 
Splice 
site 
score 
characteristics 
in 
the 
ass 
and 
rss 
datasets. 
Underlined 
values 
emphasize 
the 
signicant 
dierence 
between 
mean 
values 
of 
ass-and 
rss 
score 
distributions 


Splice 
Site 
Type 
#Total 
Counts 
Median 
Mean 
SD 
Variance 
all 
donors 
ass 
donors 
28808 
3705 
8.456 
5.263 
6.976 
0:862 
5.475 
10.219 
29.971 
104.438 
rss 
donors 
25103 
8.626 
7:878 
3.557 
12.652 
all 
acceptors 
ass 
acceptors 
rss 
acceptors 
29388 
3862 
25526 
8.450 
5.997 
8.678 
7.671 
3:356 
8:324 
4.446 
8.035 
3.122 
19.765 
64.562 
9.747 


Table 
1.6: 
Analysis 
of 
variances 
(f-test) 
and 
means 
(Wilcoxon 
test) 
between 
ESE 
motif 
frequencies 
(fese) 
distributions 
at 
ass 
and 
rss 
splice 
sites 
in 
dierent 
pre-mRNA 
contexts 
(cf. 
Figure 
1.3); 
µ 
= 
sample 
mean, 
m 
= 
sample 
median, 
2 
= 
sample 
variance; 
p-values 
signicant 
at 
a 
0.05 
are 
marked 
with 
(*) 


SRp 
Region 
ass 
=m=2 
rss 
=m=2 
ass 
. 
rss 
p() 
ass 
. 
rss 
p(2) 
ASF/SF2 
E3 
E5 
I3 
I5 
4.148 
/ 
4 
/ 
5.862 
4.187 
/ 
4 
/ 
5.382 
2.910 
/ 
3 
/ 
4.268 
3.864 
/ 
4 
/ 
6.580 
3.903 
/ 
4 
/ 
5.192 
3.894 
/ 
4 
/ 
5.950 
2.440 
/ 
2 
/ 
4.194 
3.285 
/ 
3 
/ 
6.213 
< 
10..4* 
< 
10..4* 
< 
10..4* 
< 
10..4* 
< 
10..4* 
< 
10..3* 
> 
10..1 
< 
5 
· 
10..2* 
SC35 
E3 
E5 
I3 
I5 
4.026 
/ 
4 
/ 
4.068 
4.076 
/ 
4 
/ 
3.949 
3.254 
/ 
3 
/ 
3.434 
3.914 
/ 
4 
/ 
4.477 
3.883 
/ 
4 
/ 
3.975 
3.842 
/ 
4 
/ 
3.941 
2.878 
/ 
3 
/ 
3.454 
3.578 
/ 
3 
/ 
4.558 
< 
10..4* 
< 
10..4* 
< 
10..4* 
< 
10..4* 
> 
10..1 
> 
10..1 
> 
10..1 
> 
10..1 
SRp40 
E3 
E5 
I3 
I5 
3.769 
/ 
4 
/ 
3.299 
3.893 
/ 
4 
/ 
3.191 
3.081 
/ 
3 
/ 
2.583 
3.629 
/ 
3 
/ 
3.232 
3.800 
/ 
4 
/ 
3.122 
3.779 
/ 
4 
/ 
3.079 
2.784 
/ 
3 
/ 
2.501 
3.426 
/ 
3 
/ 
3.170 
> 
10..1 
< 
10..3* 
< 
10..4* 
< 
10..4* 
< 
5 
· 
10..2* 
> 
10..1 
> 
10..1 
> 
10..1 
SRp55 
E3 
E5 
I3 
I5 
2.334 
/ 
2 
/ 
2.253 
2.347 
/ 
2 
/ 
2.327 
1.695 
/ 
2 
/ 
1.719 
2.098 
/ 
2 
/ 
2.101 
2.346 
/ 
2 
/ 
2.281 
2.332 
/ 
2 
/ 
2.222 
1.616 
/ 
1 
/ 
1.595 
1.924 
/ 
2 
/ 
1.941 
> 
10..1 
> 
10..1 
< 
10..3* 
< 
10..4* 
> 
10..1 
< 
10..2* 
< 
10..2* 
< 
10..2* 


SRp 
Binding 
Motifs 
in 
ass 
and 
rss 
Splice 
Site 
Environments 


Considering 
fese 
at 
exonic 
and 
intronic 

anks 
of 
splice 
sites, 
one 
observes 
only 
subtle 
dierences 
between 
ass 
and 
rss. 
In 
case 
of 
the 
ASF/SF2 
motif 
fese 
at 
the 
exonic 
5. 
and 
3. 
region 
has 
its 
mode 
at 
a 
frequency 
of 
three 
motifs 
(except 
for 
the 
exonic 
5. 
end 
of 
ass). 
Nevertheless, 
there 
is 
a 
signicant 
dierence 
in 
means 
and 
variances 
between 
the 
number 
of 
ASF/SF2 
motif 
at 
the 
exonic 
part 
of 
ass 
and 
rss 
(Table 
1.6). 
In 
contrast, 
the 
intronic 
regions 
of 
ass 
and 
rss 
show 
generally 
a 
higher 
ASF/SF2 
motif 
abundance 
which 
is 
unexpected 
since 
this 
motif 
– 
as 



20 
Characterizing 
Specic 
Attributes 
of 
Alternative 
and 
Reference 
Splice 
Sites 


exonic 
splicing 
enhancer 
– 
should 
occur 
more 
frequently 
in 
exonic 
regions. 
There 
is 
no 
signicant 
dierence 
in 
the 
variances 
of 
the 
fese 
distributions 
between 
intronic 
ass 
and 
rss 
acceptor 
sites 
but 
also 
here 
the 
means 
vary 
signicantly. 
Considering 
the 
size 
of 
the 
data 
set, 
the 
results 
clearly 
indicate 
a 
tendency 
to 
more 
ASF/SF2 
motifs 
in 
the 

anking 
regions 
of 
predicted 
ass 
than 
in 
the 
set 
of 
rss. 
Table 
1.6 
summarizes 
the 
SRp 
specic 
fese 
distributions 
found 
for 
ass 
and 
rss, 
being 
compared 
also 
between 
the 
splice 
site 

anking 
pre-mRNA 
regions. 
The 
ASF/SF2 
and 
SC35 
motif 
frequencies 
show 
in 
the 
exonic 

anks 
of 
50-and 
30ss 
a 
tendency 
to 
more 
motifs 
in 
neighborhood 
of 
the 
ass 
than 
at 
rss 
splice 
sites. 
The 
same 
result 
was 
found 
for 
SRp40 
motif 
frequencies 
with 
exception 
of 
the 
exon 
3. 

anks. 
SRp55 
motifs 
appeared 
signicantly 
more 
frequent 
downstream 
of 
alternative 
50ss 
(I5) 
than 
downstream 
of 
rss. 
The 
means 
of 
the 
fese 
distribution 
of 
SRp 
SC35 
show 
a 
signicant 
dierence 
between 
ass 
and 
rss, 
although 
the 
variances 
do 
not 
convey 
this 
information. 
Based 
on 
these 
tests 
one 
can 
conclude 
that 
fese 
is 
dierent 
between 
mRNA 
regions 
around 
splice 
sites 
of 
ass 
and 
rss. 
While 
these 
motif 
modules 
might 
be 
individually 
subtle 
(e.g. 
between 
two 
splice 
sites) 
they 
appear 
to 
be 
signicant 
on 
the 
whole 
dataset. 



Discussion 


1.4 
Discussion 
For 
a 
dataset 
of 
computationally 
predicted 
alternative 
splice 
sites 
(ass) 
it 
was 
shown 
how 
inherent 
information 
can 
be 
utilized 
to 
validate 
the 
predictions 
by 
applying 
statistics 
on 
dierent 
features 
typical 
for 
splice 
sites. 
These 
features 
were 
compared 
between 
a 
set 
of 
predicted 
ass 
and 
splice 
sites 
arising 
from 
a 
set 
of 
mRNAs 
not 
predictive 
of 
ass 
by 
current 
experimental 
knowledge 
(rss). 
The 
results 
suggests 
that 
in 
spite 
of 
not 
predicting 
ass, 
the 
reference 
transcripts 
and 
their 
splice 
sites 
share 
similar 
characteristics 
to 
the 
alternative 
ones 
as 
for 
example 
the 
overlapping 
region 
in 
the 
splice 
site 
score 
(Sss) 
distributions 
(ranging 
from 
+5 
to 
-10 
bit) 
demonstrate. 
However, 
in 
the 
low 
scoring 
region 
both 
ass 
and 
rss 
separate 
clearly 
with 
the 
ass 
exhibiting 
more 
frequently 
scores 
below 
zero 
at 
both 
donor 
and 
acceptor 
sites. 
Thus, 
a 
signicant 
part 
of 
the 
predicted 
ass 
possess 
motifs 
incongruent 
to 
the 
splice 
site 
motifs 
found 
for 
human 
GT..AG 
reference 
splice 
sites. 
In 
fact, 
this 
observation 
could 
still 
be 
due 
to 
pseudo 
splice 
sites 
but 
as 
the 
test 
of 
Sss 
against 
the 
transcript 
support 
(ft) 
indicates, 
there 
exists 
a 
clear 
dependency 
between 
the 
number 
of 
transcripts 
that 
utilize 
these 
splice 
sites 
and 
the 
pertinent 
score 
in 
both 
the 
ass 
and 
rss. 
As 
a 
promising 
splice 
site 
feature, 
the 
binding 
motif 
frequency 
(fese) 
of 
spliceenhancing 
SR 
proteins 
(SRp) 
was 
investigated 
in 
context 
of 
exonic 
and 
intronic 
splice 
site 

anks 
and 
compared 
between 
ass 
and 
rss. 
Firstly, 
the 
donor/acceptor 
site 
specic 
occurrence 
of 
SRp 
motifs 
was 
analyzed 
independent 
from 
the 
classication 
into 
ass 
and 
rss. 
For 
both 
the 
exonic 
and 
intronic 

anks 
a 
higher 
variance 
for 
fese 
of 
the 
SR 
protein 
ASF/SF2 
was 
found 
at 
donor 
and 
acceptor 
sites 
but 
only 
in 
comparison 
between 
the 
intronic 
splice 
site 

anks 
on 
average 
1-2 
motifs 
more 
were 
found 
at 
the 
donor 
site. 
For 
the 
other 
SR 
proteins 
a 
similar 
trend 
towards 
more 
binding 
motifs 
at 
intronic 
donor 
compared 
to 
acceptor 
regions 
can 
be 
observed. 
This 
suggests 
a 
higher 
presence 
of 
these 
motifs 
at 
the 
intronic 

ank 
of 
donor 
sites, 
a 
surprising 
eect 
since 
the 
motifs 
were 
initially 
determined 
by 
consensus 
sequences 
made 
as 
exon“ 
splicing 
enhancer 
(27). 
Nevertheless, 
since 


” 
the 
rss 
make 
up 
the 
major 
fraction 
in 
these 
donor/acceptor 
-intronic 
/exonic 
datasets, 
this 
observation 
needed 
to 
be 
further 
investigated 
to 
derive 
conclusions 
on 
an 
eect 
that 
is 
present 
also 
in 
predicted 
ass. 
Hence, 
in 
the 
next 
step 
the 
dierences 
in 
fese 
of 
exonic 
donor 
and 
-acceptor 
sites 
between 
ass 
and 
rss 
were 
analyzed. 
The 
ASF/SF2 
motif 
was 
found 
to 
occur 
in 
a 
signicant 
fraction 
of 
predicted 
alternative 
splice 
sites 
(exonic 

anks) 
more 



Discussion 


frequently 
than 
in 
the 
set 
of 
reference 
splice 
sites. 
The 
median 
of 
both 
frequency 
distributions 
indicates 
as 
much 
as 
four 
ASF/SF2 
motifs 
at 
both 
alternative 
and 
reference 
splice 
site 

anks. 
However, 
the 
mean 
and 
variance 
indicate 
that 
at 
both 
5. 
and 
3. 
splice 
sites 
the 
pattern 
of 
ESE 
occurrence 
deviates 
between 
ass 
and 
rss 
with 
the 
tendency 
to 
at 
least 
one 
additional 
motif 
in 
the 

anking 
region 
of 
alternative 
splice 
sites. 
For 
example, 
one 
observes 
that 
ASF/SF2 
binding 
motifs 
occur 
in 
average 
more 
frequently 
in 
the 
intron 

anks 
of 
ass 
compared 
to 
rss, 
with 
about 
the 
same 
variance 
around 
the 
mean 
of 
four 
and 
three 
motifs 
respectively. 
Diverging 
characteristics 
(similar 
variances 
around 
dierent 
means) 
in 
the 
ass 
and 
rss 
specic 
motif 
frequencies 
of 
the 
three 
other 
types 
of 
SR 
proteins 
suggests 
that 
both 
datasets 
exhibit 
little 
variation 
around 
signicantly 
dierent 
pattern 
of 
ESE 
distribution. 
It 
will 
be 
interesting 
to 
follow 
up 
investigations 
on 
modules 
of 
SR 
protein 
binding 
motifs 
between 
predicted 
ass 
and 
rss 
to 
test 
computational 
predictions 
on 
multimerizing 
enhancer 
(or 
silencer) 
protein 
complexes 
which 
may 
bind 
to 
specic 
combination 
classes 
of 
cis-elements. 



Chapter 
2 


Analysis 
of 
Overlapping 
Donor 
Splice 
Sites 


2.1 
Introduction 
2.1.1 
Subtle 
Splice 
Variants 
Compared 
with 
skipped 
exons 
as 
the 
most 
prevalent 
type 
of 
AS 
in 
human 
and 
mammalian 
cells, 
A3Es 
and 
A5Es 
are 
thought 
to 
create 
more 
subtle 
changes, 
by 
aecting 
the 
choice 
of 
the 
30ss 
or 
50ss, 
respectively. 
Here, 
splice 
site 
usage 
gives 
rise 
to 
two 
types 
of 
exon 
segments 
– 
the 
'core’ 
common 
to 
both 
splice 
forms 
and 
the 
'extension’ 
that 
is 
present 
in 
only 
the 
longer 
isoform. 
Both 
types 
of 
AS 
events 
have 
been 
shown 
to 
play 
decisive 
roles 
during 
development, 
e.g., 
sex 
determination 
and 
dierentiation 
in 
Drosophila 
melanogaster 
(76) 
or 
developmental 
stage-related 
changes 
in 
the 
human 
CFTR 
gene 
(77), 
but 
also 
in 
human 
disease, 


e.g. 
50ss 
mutations 
in 
the 
tau 
gene 
(78). 
A3Es 
and 
A5Es 
are 
thought 
to 
be 
regulated 
by 
splicing-regulatory 
elements 
in 
exons 
and 
nearby 
exon 

anking 
regions, 
as 
well 
as 
trans-acting 
antagonistic 
splicing 
factors, 
which 
bind 
them 
and 
aect 
the 
choice 
of 
splice 
sites 
in 
a 
concentration 
dependent 
manner 
(79, 
80). 
Interestingly, 
computational 
studies 
showed 
that 
for 
both 
A3Es 
and 
A5Es 
the 
distribution 
of 
extensions, 
f(E), 
is 
markedly 
skewed 
toward 
short-range 
splice 
forms 
(81). 
In 
particular, 
alternative 
splice 
sites 
that 
are 
separated 
by 
the 
threenucleotide 
long 
motif 
Nag/Nag/ 
(where 
'/’ 
marks 
an 
inferred 
splice 
site) 
make 
up 
a 
predominant 
proportion 
of 
A3E 
events 
in 
a 
mammals, 
extending 
to 
invertebrates 
and 
plants 
(82, 
2). 
The 
frequent 
occurrence 
of 
the 
NAGNAG 
acceptor 
motif 
in 
the 
human 
genome, 
which 
can 
be 
observed 
at 
intron-exon 
borders 
of 
23 



Subtle 
Splice 
Variants 


30% 
of 
human 
reference 
transcripts 
when 
mapped 
to 
the 
genome, 
introduced 
the 
concept 
of 
subtle 
alternative 
splice 
events 
emerging 
from 
tandem 
splice 
sites 
and 
raised 
a 
series 
of 
questions. 
How 
can 
two 
splicing 
signals 
in 
such 
an 
extreme 
proximity 
be 
dierentiated 
by 
the 
splicing 
machinery? 
Which 
function 
can 
alternative 
mRNAs 
provide, 
being 
only 
dierent 
in 
a 
triplet? 
In 
contrast, 
the 
triplet 
variation 
GYNGYN 
at 
the 
donor 
site 
of 
exons 
is 
much 
less 
frequently 
found 
(83). 
The 
fraction 
of 
transcripts 
conrming 
this 
tandem 
donor 
is 
with 
1.4% 
a 
multiple 
lower 
compared 
to 
the 
fraction 
of 
transcripts 
conrming 
alternative 
splicing 
at 
NAGNAG 
acceptors 
(17.6%)(84). 
However, 
there 
is 
a 
subtle 
variation 
at 
the 
donor 
site 
which 
occurs 
strikingly 
frequent. 
The 
repertoire 
of 
donor 
splice 
site 
variations 
is 
severely 
dominated 
by 
a 
four 
nucleotide 
variation 
(81), 
which 
occurs 
more 
frequently 
than 
alternative 
splicing 
at 
the 
GYNGYN 
or 
at 
the 
NAGNAG 
motif. 
The 
donor 
splice 
site 
itself 
forms 
a 
consensus 
motif, 
which 
in 
higher 
eukaryotes 
contains 
two 
splice 
signals 
forming 
the 
core 
motif 
GYNNGY. 
As 
the 
50ss 
is 
the 
rst 
signal 
that 
is 
recognized 
by 
U1 
snRNP 
during 
spliceosome 
assembly 
it 
is 
conceivable 
that 
this 
donor 
motif 
is 
under 
selection 
pressure 
to 
maintain 
the 
crucial 
complementarity 
to 
U1 
snRNA. 
However, 
the 
question 
arises 
whether 
this 
complementarity 
alone 
can 
explain 
the 
shift 
from 
splicing 
at 
the 
distal 
(upstream) 
to 
splicing 
at 
the 
proximal 
(downstream) 
GY 
within 
this 
motif. 
Support 
from 
experimental 
studies 
regarding 
alternative 
splice 
forms 
that 
emerge 
from 
tandem 
splice 
sites 
is 
still 
very 
sparse. 
Hence, 
similarities 
and 
dierences 
between 
overlapping, 
non-overlapping 
and 
constitutive 
splice 
sites 
remain 
to 
be 
delineated 
and 
have 
been 
addressed 
by 
the 
work 
in 
this 
chapter. 



Introduction 


2.1.2 
Proposed 
Mechanisms 
for 
Regulating 
the 
Donor 
Splice 
Site 
In 
addition 
to 
the 
general 
models 
of 
splice 
site 
selection 
which 
make 
a 
basic 
distinction 
between 
the 
pairing 
of 
splice 
sites 
across 
the 
exon 
(exon-denition\) 


"
or 
the 
intron 
(intron-denition\) 
(85), 
several 
other 
models 
have 
been 
re


"
ported. 
One 
model 
considers 
the 
concentration 
of 
the 
antagonistic 
splicing 
factors 
SF2/ASF 
and 
hnRNP 
A1, 
such 
that 
higher 
doses 
of 
SF2/ASF 
enhance 
simultaneously 
U1 
snRNP 
binding 
to 
two 
competing 
splice 
sites 
(leading 
to 
use 
of 
the 
downstream 
ss) 
whereas 
higher 
doses 
of 
hnRNP 
A1 
decrease 
U1snRNP 
anity 
at 
both 
donor 
sites 
(86, 
87). 
Also 
normal 
concentrations 
of 
SF2/ASF 
were 
shown 
to 
be 
sucient 
in 
binding 
of 
U1-snRNP 
to 
both 
splice 
sites 
if 
the 
50ss 
are 
close 
to 
consensus. 
In 
case 
of 
high 
hnRNP 
occupation 
U1-snRNP 
can 
still 
bind 
a 
splice 
site 
(because 
of 
higher 
anity) 
but 
is 
shifted 
to 
that 
50ss 
that 
is 
closer 
to 
the 
nearest 
high-anity 
enhancer 
binding 
site. 
This 
model 
cannot 
be 
applied 
directly 
to 
splicing 
at 
overlapping 
donor 
splice 
sites, 
since 
sterical 
hindrance 
would 
prevent 
any 
case 
of 
double 
occupancy. 
However, 
it 
is 
conceivable, 
that 
high 
anity 
enhancer 
binding 
sites 
exist 
exclusively 
in 
proximal 
polarity 
(near 
to 
the 
weaker 
downstream 
donor 
of 
two 
overlapping 
donor 
sites), 
pulling 
U1-snRNP 
to 
this 
end 
of 
the 
tandem 
donor 
motif. 
This 
pull 
could 
even 
be 
increased 
if 
exon 
silencer 
locate 
in 
distal 
location 
to 
the 
weak 
donor, 
impairing 
U1-snRNP 
binding 
to 
the 
stronger 
splice 
site. 
Similar 
observations 
were 
made 
by 
Bai 
et 
al. 
at 
the 
30ss 
where 
ASF/SF2 
promoted 
use 
of 
a 
proximal 
30ss 
and 
hnRNP 
A1 
the 
distal 
30ss 
in 
a 
CGRP 
transcript 
in 
vivo 
(88). 
Another 
model 
of 
50ss 
selection 
was 
suggested 
based 
on 
a 
competition 
assay 
between 
5ss 
subclasses 
of 
strong, 
intermediate 
and 
weak 
strengths 
of 
5ss 
and 
taking 
into 
account 
the 
free 
binding 
energy 
of 
U1 
snRNA 
as 
well 
as 
exon-and 
intron 
binding 
splice 
factors 
(89). 
They 
demonstrated 
that 
splicing 
eciencies 
and 
50ss 
selection 
are 
dependent 
on 
whether 
the 
competing 
donor 
sites 
belong 
to 
dierent 
subclasses 
and 
upon 
their 
ability 
to 
form 
G. 
base 
pairs. 
However, 
their 
analysis 
also 
implied 
decreased 
splice 
eciency 
for 
5ss 
that 
are 
less 
than 
40 
nt 
apart 
due 
to 
the 
eect 
of 
sterical 
hindrance 
of 
U1 
snRNP 
binding. 
Finally 
a 
mechanism 
of 
oriented 
scanning 
has 
been 
proposed 
as 
a 
model 
for 
5ss 
selection 
(90) 
where 
within 
intron 
7 
of 
the 
human 
F7 
gene 
several 
monomeric 
repeats 
duplicate 
an 
authentic 
donor 
site, 
yet 
the 
most 
upstream 
donor 
remains 
the 
only 
selected 
50ss. 
The 
distance 
between 
these 
donor 
sites 
comprised 
37 
nucleotides 



Proposed 
Mechanisms 
for 
Regulating 
the 
Donor 
Splice 
Site 


and 
thus 
appearing 
rather 
as 
non-overlapping 
than 
overlapping 
donor 
sites. 
The 
authors 
showed 
that 
after 
mutating 
the 
wild 
type 
donor 
the 
next 
available 
and 
most 
upstream 
located 
pseudo 
splice 
site 
became 
activated. 
This 
example 
states 
an 
interesting 
contrast 
to 
the 
above 
mentioned 
models 
and 
suggest 
a 
model 
of 
reconstituting 
or 
sustained 
competition 
between 
alternative 
donor 
sites, 
both 
with 
high 
complementarity 
to 
the 
U1 
snRNA 
recognition 
site. 



Methods 
27 


2.2 
Methods 
2.2.1 
Data 
Set 
of 
Alternative 
Exons 
Exons 
of 
human 
and 
mouse 
genes 
were 
extracted 
from 
the 
Hollywood 
database 
(3). 
For 
two 
dierent 
transcripts 
aligned 
to 
a 
genomic 
locus, 
alternative 
50ss 
exons 
(A5Es) 
matched 
at 
their 
30ss, 
but 
exhibited 
exactly 
one 
short 
and 
one 
long 
splice 
form 
resulting 
from 
variation 
at 
the 
50ss. 
Alternative 
30ss 
exons 
(A3Es) 
matched 
at 
their 
50ss, 
but 
exhibited 
exactly 
one 
short 
and 
one 
long 
splice 
form 
resulting 
from 
variation 
at 
the 
30ss. 
Constitutive 
exons 
(CEs) 
were 
dened 
as 
exons 
of 
multi-exon 
genes 
that 
have 
as 
of 
date 
no 
transcript-supported 
evidence 
for 
undergoing 
any 
type 
of 
AS. 
In 
all 
AS 
events, 
A5Es, 
A3Es 
and 
CEs 
are 
internal 
exons, 
i.e., 
all 
exons 
of 
a 
transcript 
except 
the 
rst 
and 
last 
one, 
because 
these 
are 

anked 
only 
by 
one 
splice 
site 
and 
may 
contain 
additional 
regulatory 
sequences 
of 
splicing 
initiation 
and 
termination. 
Each 
exon 
had 
to 
be 

anked 
by 
/Gt 
or 
/Gc 
type 
splice 
sites 
at 
the 
donor 
site 
and 
Ag/ 
type 
splice 
sites 
at 
the 
acceptor 
site. 
U12-type 
introns 
were 
excluded 
from 
this 
analysis, 
because 
of 
their 
low 
fraction 
(less 
than 
1% 
of 
the 
human 
introns). 
Figure 
2.1 
gives 
an 
overview 
of 
the 
data 
preparation 
and 
applied 
methodology 
of 
this 
chapter. 


2.2.2 
Spliced-Alignments 
Manual 
inspection 
of 
A5Es 
with 
short 
extensions 
(E 
= 
6 
nucleotides), 
originally 
excluded 
in 
Hollywood, 
revealed 
a 
substantial 
amount 
of 
putative 
alignment 
artifacts 
due 
to 
misaligned 
nucleotides 
close 
to 
exon-intron 
junctions 
(see 
Appendix 
B.1). 
Alignments 
were 
derived 
for 
ESTs 
by 
the 
Sim4 
program 
(91), 
and 
were 
corroborated 
in 
a 
recent 
performance 
study 
of 
spliced-alignment 
algorithms 
(92). 
In 
particular, 
examples 
were 
found, 
where 
Sim4 
introduced 
shifts 
of 
EST 
nucleotides 
between 
genomic 
donor 
and 
acceptor 
sites 
at 
genomic 
loci 
that 
encode 
short 
varying 
alternative 
exon 
(cf. 
Figure 
2.2). 
To 
decrease 
the 
number 
of 
spurious 
alignments 
in 
the 
dataset 
of 
A5Es 
and 
A3Es, 
the 
original 
ESTs 
were 
used 
and 
created 
new 
transcript-to-genomic 
alignments, 
by 
utilizing 
two 
dierent 
algorithms: 
i) 
Blat 
(93), 
as 
stored 
in 
the 
UCSC 
database 
(http://genome.ucsc.edu); 
and 
ii) 
Exalin 
(92), 
with 
the 
parameter 
set 
(m, 
n, 
q, 
r, 
x) 
= 
(25, 
25, 
..25, 
..25, 
and 
- 
25). 
Manual 
inspection 
of 
control 
samples 
in 
the 
alignment 
results 
conrmed 
a 
clearly 
improved 
quality 
in 
the 
correct 
exon-intron 
boundary 
recognition. 
In 
all, 
about 
35% 
of 
all 
initial 
A5E 



Classication 
of 
Major 
and 
Minor 
Tandem 
Donors 


HOLLYWOODPreprocessing:
-filtering G[C/T]..AG-computing exon coordinates on chromosomal scale-comparing A5E / A3E with UCSC alignments-EXALIN: re-alignment of A5E / A3E transcriptsHsMmPre-Anaysis:
-data classification: A5E4, A3E3total transcript evidence/ss score-data subclasses:
A5E PA5E P4 and A5E DA5E D4-comparing classes: CE pseudo motifs (CE d4/p4) 
HsMmSplice Site Motifs-splice site score-pos.spec.nucleotides-compar.: pseudo 
motifs (CE)
Species Conservation-splice site motif-exon- / intron sequence-compar.: CE, SENMD-PTC localisation via RefSeqcomparison-exon- / intron sequence-compar.: CE, SECandidate Selection-RT-PCR analysis*
-estimation of impact on 
protein domainsHsHsHsMmHsESE / ESS-3 indpendent motif datasets-distribution in A5E4 exon/
intron flanks-compar.: pseudo motifs (CE)
HsHsFunction-testing enrichment of individual GO 
concepts (CC,BP,MF)
-comparing classes: pseudo motifs (CE)
Analyses
Figure 
2.1: 
Overview 
of 
methodology 
applied 
for 
analysis 
of 
subtle 
donor 
splice 
sites 


predictions 
( 
9%) 
of 
A5E4 
splicing 
exons 
could 
be 
conrmed 
with 
very 
similar 
numbers 
by 
both 
Blat 
and 
Exalin 
alignments 
(Table 
2.1), 
showing 
at 
least 
one 
qualitative 
aligning 
transcript. 
Increasing 
the 
stringency 
to 
require 
at 
least 
two 
transcripts 
conrming 
each 
donor 
variant, 
reduced 
the 
number 
of 
Exalin 
rened 
A5E 
further 
to 
27% 
(504/1,868) 
with 
a 
fraction 
of 
7.7% 
(39/504) 
of 
A5E4 
splicing 
exons. 
Subsequent 
analyses 
were 
performed 
using 
the 
subset 
conrmed 
by 
all 
three 
alignment 
methods, 
with 
at 
least 
one 
transcript 
for 
the 
minor 
variant, 
to 
keep 
the 
dataset 
in 
a 
reasonable 
size. 


2.2.3 
Classication 
of 
Major 
and 
Minor 
Tandem 
Donors 
The 
number 
of 
transcripts 
that 
aligned 
either 
to 
the 
distal 
N(d) 
or 
proximal 
N(p) 
donor 
was 
used 
to 
classify 
A5Es. 
To 
this 
end, 
i) 
the 
ratio 
R 
(0 
< 
R 
< 
1) 
of 
the 
lower 
over 
the 
higher 
transcript 
coverage 
was 
calculated 
as 



Methods 
29 


R 
= 
N(d) 
N(p) 
if 
N(d) 
< 
N(p) 
(2.1) 
or 
R 
= 
N(p) 
N(d) 
if 
N(p) 
< 
N(d) 
(2.2) 


where 
cases 
of 
R 
= 
1 
if 
N(d) 
= 
N(p) 
were 
discarded, 
because 
accounting 
for 
only 
2.3% 
ii) 
each 
donor 
was 
dened 
as 
major“ 
if 
it 
was 
oberved 
to 
be 
spliced 
in 
at 


"
least 
twice 
as 
much 
transcripts 
as 
the 
adjacent 
alternative 
splice 
site, 
which 
then 


became 
the 
minor“ 
splice 
site 
per 
denition. 
Hence, 
in 
equation 
2.1 
all 
donors 


” 
reaching 
a 
treshold 
of 
RT 
= 
0.5, 
dened 
the 
proximal 
donor 
as 
major 
splice 
site, 
while 
the 
same 
treshold 
in 
equation 
2.2 
dened 
the 
distal 
donor 
as 
major 
splice 
site. 
Further, 
in 
this 
analysis 
a 
minimal 
coverage 
of 
at 
least 
one 
transcript 
was 
required. 


2.2.4 
Statistical 
Analysis 
of 
Splice 
Sites 
The 
deviation 
of 
splice 
sites 
from 
the 
consensus 
is 
quantied 
by 
a 
maximumentropy 
scoring 
model, 
implemented 
in 
MaxEntScan 
and 
publicly 
available 


(94) 
(see 
also 
chapter 
1.2.4). 
The 
50ss 
model 
incorporates 
the 
last 
three 
(rst 
six) 
nucleotides 
of 
the 
exon 
(intron), 
and 
the 
30ss 
model 
incorporates 
the 
last 
20 
(rst 
three) 
nucleotides 
of 
the 
intron 
(exon). 
Sequence 
logos 
and 
pictograms 
were 
computed 
and 
displayed 
using 
the 
Weblogo 
tool 
with 
nite-sample 
size 
correction 
(95). 
P 
-values 
of 
splice 
site 
frequencies 
where 
calculated 
as 
follows: 
1) 
frequencies 
of 
occurrences 
at 
the 
considered 
at 
P4 
and 
P	4 
splicing 
exons, 
as 
well 
as 
D4 
and 
D	4 
splicing 
exons, 
where 
compared 
by 
a 
4x2 
contingency 
table 
and 
2-test; 
2) 
statistically 
signicant 
positions 
were 
selected 
at 
P< 
0:05; 
3) 
at 
the 
same 
position, 
the 
nucleotide 
(maximally 
two 
nucleotides) 
with 
the 
largest 
dierence 
of 
the 
frequency 
of 
occurrence 
between 
two 
types 
(e.g., 
P4 
and 
P	4) 
was 
subsequently 
tested 
against 
the 
remaining 
nucleotides 
by 
2x2 
contingency 
table 
and 
2-test, 
where 
P< 
0:05 
was 
considered 
as 
statistically 
signicant. 
The 
information 
along 
a 
sequence 
was 
calculated 
as 
the 
relative 
entropy, 
as 
described 
in 
Equation 
1.3 
(Chapter 
1.2.4). 

Experimental 
Assay 


2.2.5 
Identication 
of 
Non-Sense 
Codons 
For 
each 
A5E4 
splicing 
exon, 
the 
longest 
cDNA 
that 
mapped 
to 
the 
corresponding 
gene 
with 
annotated 
CDS 
start 
and 
end 
position 
was 
taken 
as 
a 
reference 
sequence. 
In 
most 
cases 
such 
a 
reference 
was 
only 
available 
for 
either 
the 
proximal 
or 
distal 
alternative 
splice 
form. 
Identication 
of 
mRNAs 
with 
the 
potential 
to 
trigger 
NMD 
was 
performed, 
by 
comparing 
the 
reading-frame 
after 
splicing 
at 
each 
tandem 
donor. 
Tandem 
events 
led 
to 
a 
new 
reading-fame, 
the 
rst 
downstream 
non-sense 
codon 
of 
which 
was 
detected 
and 
analyzed 
for 
PTCs 
occurring 
more 
than 
50 
nucleotides 
upstream 
of 
the 
last 
exon-exon 
junction 
to 
elicit 
NMD 
(10, 
96). 


2.2.6 
Detection 
of 
Sequence 
Conservation 
The 
core 
of 
A5E4 
splicing 
exons 
was 
matched 
against 
mouse 
genomic 
DNA 
(version 
mm03), 
using 
Blast 
with 
parameter 
values 
-a2 
-gT 
-W10 
-q-2 
-r3 
-e0.001. 
Signicant 
matches 
of 
similarity 
were 
ltered 
for 
canonical 
splice 
sites 
and 
the 
exon 

anking 
regions 
of 
200 
nucleotides 
were 
extracted 
from 
the 
genomic 
sequence. 
Subsequently, 
orthologous 
human 
and 
mouse 
intron 
regions 
were 
aligned 
using 
the 
Dna 
Block 
Aligner 
(97), 
with 
-nomatchn 
-gap 
0.02 
-blockopen 
0.2 
-umatch 
0.05 
-p, 
which 
detects 
block 
of 
conserved 
sequences 
located 
at 
possible 
dierent 
positions 
relative 
to 
splice 
junction. 
The 
sequence 
position 
of 
detected 
blocks 
of 
conservation 
was 
parsed 
and 
recorded 
with 
the 
script 
Dba-Parser 
(Holste, 
unpublished 
data) 
and 
plotted 
in 
a 
region 
of 
100 
nucleotides, 
with 
a 
moving-average 
of 
ten 
nucleotides. 
Exon 
conservation 
was 
determined 
by 
the 
score 
(Sort) 
from 
Clustalw 
alignments, 
self-alignment 
of 
the 
larger 
exons 
to 
yield 
the 
score 
Sid, 
and 
calculation 
of 
the 
normalized 
score 
Stot 
= 
Sort=Sid. 


2.2.7 
Experimental 
Assay 
Experimental 
conrmation 
of 
tandem 
splice 
forms 
was 
performed 
at 
the 
FritzLipmann-
Institute 
(FLI) 
Jena 
by 
Stefanie 
Schindler 
and 
Karol 
Szafranski, 
according 
to 
the 
following 
protocol 
(4): 


i. 
RT-PCR 
amplication: 
For 
validation 
of 
splice 
variants, 
nested 
PCR 
was 
performed 
using 
100 
ng 
cDNA 
templates 
from 
the 
Human 
Multiple 
Tissue 
cDNA 
Panels 
I 
and 
II 
(BD 
Biosciences). 
Splice 
variants 
were 
enriched 
for 
EST 
originating 
from 
dierent 
cDNA 
libraries 
and, 
for 
a 
given 
gene, 
suitable 

Methods 
31 


tissues 
were 
chosen 
according 
to 
the 
origin 
of 
ESTs 
for 
the 
minor 
splice 
variant 
or 
the 
expression 
prole 
found 
in 
the 
Stanford 
Source 
data 
base 
(98). 
Primers 
were 
obtained 
from 
Metabion. 
Nested 
RT-PCR 
reactions 
were 
set 
up 
with 
ReadyToGo 
PCR 
beads 
(Amersham) 
and 
10 
pmol 
primer 
in 
25 
l 
total 
volume, 
according 
to 
the 
manufacturer's 
instructions. 
The 
thermocycle 
protocol 
was 
1 
min 
30 
sec 
initial 
denaturation 
at 
93C, 
followed 
by 
25 
cycles 
of 
40 
sec 
denaturation 
at 
93C, 
40 
sec 
annealing 
at 
55C, 
1 
min 
extension 
at 
72C, 
and 
a 
nal 
4 
min 
extension 
step 
at 
72C. 
In 
the 
second 
round 
of 
nested 
PCR, 
2 
l 
rst-round 
product 
was 
amplied 
for 
30 
cycles. 
Ethanol-precipitated 
PCR 
products 
were 
directly 
sequenced 
using 
target-specic 
forward 
and 
reverse 
primers; 


ii. 
Sanger 
sequencing: 
Reactions 
were 
set 
up 
with 
200 
ng 
template 
DNA, 
10 
pmol 
primer, 
and 
BigDye 
v3.1 
(Applied 
Biosystems) 
in 
10 
l 
nal 
volume, 
according 
to 
the 
supplier's 
instructions. 
The 
thermocycle 
protocol 
was 
5 
min 
initial 
denaturation 
at 
95C, 
followed 
by 
29 
cycles 
of 
30 
s 
denaturation 
at 
95C, 
10 
s 
annealing 
at 
55C, 
4 
min 
extension 
at 
60C. 
After 
ethanol 
precipitation, 
automated 
sequence 
separation 
and 
detection 
was 
done 
on 
an 
ABI 
3730XL 
sequencer. 
Electropherograms 
were 
processed 
by 
Phred 
(99). 
After 
automated 
assembly 
(Staden 
package, 
(100)), 
sequence 
variations 
were 
veried 
by 
manual 
inspection 
using 
GAP4 
(Staden 
package). 
2.2.8 
Presence 
of 
Predicted 
Splicing-Regulatory 
Elements 
Searching 
for 
splicing 
regulatory 
elements 
in 
exon 

anking 
regions 
was 
performed 
by 
using 
the 
following 
data 
sets: 
176 
predicted 
exonic 
splicing 
silencers 
identied 
in 
Wang 
et 
al. 
(101), 
753 
predicted 
intronic 
enhancers 
and/or 
silcencers 
identied 
in 
Yeo 
et 
al. 
(102), 
and 
1,013 
putative 
exonic 
splicing 
silencers 
identied 
in 
Zhang 
et 
al. 
(103). 
All 
elements 
were 
searched 
for 
in 
a 
region 
of 
100 
nucleotides 

anking 
proximal 
tandem 
donors, 
and 
exact 
matches 
were 
counted 
in 
non-overlapping 
sequence 
windows 
of 
20 
nucleotides. 


2.2.9 
Gene 
Ontology 
(GO) 
Annotations 
GO-terms 
for 
genes 
with 
A5E4 
splicing 
exons 
(358 
GO 
terms), 
A5Es 
(1,414), 
and 
CEs 
(3,655) 
were 
obtained 
from 
the 
Ensembl 
database 
(www.emsembl.org), 
corresponding 
to 
129 
and 
1,283 
genes 
with 
A5E4 
splicing 
exons 
or 
A5Es, 
respec



Gene 
Ontology 
(GO) 
Annotations 


tively, 
and 
8,664 
genes 
of 
a 
control 
set. 
GO 
annotations 
for 
A5E4 
splicing 
exons 
of 
129 
of 
166 
genes 
(representing 
the 
total 
set 
of 
171 
A5E4 
splicing 
exons) 
were 
mapped, 
and 
the 
most 
frequent 
category 
annotations 
molecular 
function“ 
and 


” 
biological 
process“ 
were 
selected; 
in 
decreasing 
order: 
ATP 
binding\, 
Zinc 
ion 


""” 
binding\, 
Regulation 
of 
transcription, 
DNA-dependent\, 
Transferase 
activity\, 


""
Signal 
transduction\, 
Hydrolase 
activity\, 
RNA 
binding\, 
Protein 
binding\, 


""""
Transcription 
factor 
activity“ 
and 
DNA 
binding\. 
In 
order 
to 
compare 
the 
GO 


""
annotations 
of 
A5E4 
genes 
against 
a 
control, 
10,000 
genes 
with 
at 
least 
one 
pseudo 
splice 
site, 
d	4 
or 
p	4 
splicing 
exons 
(each 
comprising 
129 
genes) 
were 
sampled 
and 
the 
frequency 
of 
occurrence 
of 
a 
certain 
GO 
term 
was 
computed. 
The 
statistical 
signicance 
(P 
-value) 
was 
calculated 
analogous 
to 
(102), 
by 
assessing 
the 
frequency 
of 
occurrence 
that 
a 
certain 
GO-term 
was 
present 
in 
the 
control 
more 
frequently 
than 
in 
the 
A5E4 
gene 
set, 
divided 
by 
10,000. 
The 
outcome 
showed 
the 
following 
categories 
as 
signicant 
at 
the 
0.05 
percent 
level: 
Signal 
transduction“ 
(P4/d4 
vs 
5ss/d	4, 
0.07; 
D4/p4 
vs 
5ss/p	4,

"
0.15), 
RNA 
binding“ 
(0.0004; 
0.003), 
GTP 
binding“ 
(0.02; 
0.04), 
Electron 


""” 
transport“ 
(0.02; 
0.03), 
Protein 
biosynthesis“ 
(0.01; 
0.03), 
Signal 
transducer 


""
activity“ 
(0.04; 
0.08). 
To 
correct 
for 
multiple 
testing, 
a 
(conservative) 
Bonferroni 
correction 
(104) 
were 
applied, 
the 
P 
-value 
chosen 
was 
divided 
by 
the 
number 
of 
performed 
tests, 
and 
GO-terms 
occurring 
with 
Pc 
< 
0:05=10 
= 
0:005 
were 
considered 
as 
signicant. 



Results 
33 


2.3 
Results 
2.3.1 
Biased 
Extensions 
of 
Alternative 
50ss 
and 
30ss 
Exons 
Exon-skipping 
is 
the 
most 
prevalent 
AS 
type 
produced 
by 
the 
human 
spliceosome, 
as 
well 
as 
by 
all 
other 
mammals 
investigated 
to 
date, 
when 
averaged 
across 
dierent 
organ 
systems 
and 
cell 
types 
that 
can 
exhibit 
tissue-enriched 
splice 
forms 
(13, 
105). 
Internal 
alternative 
exons 
that 
involve 
exclusively 
either 
the 
30ss 
(A3Es) 
or 
the 
50ss 
(A5Es) 
are 
also 
abundantly 
produced, 
while 
the 
simultaneous 
alteration 
of 
30ss 
and 
50ss 
(producing 
exons 
that 
overlap 
but 
match 
neither 
splice 
site) 
are 
markedly 
less 
frequent. 
For 
A5Es 
the 
most 
distal 
splice 
site 
denes 
the 
exon 
core, 
while 
proximal 
sites 
(if 
more 
than 
one 
alternative 
choice 
is 
possible) 
are 
exon 
extensions 
only 
included 
in 
selected 
mRNAs. 
Out 
of 
a 
collection 
of 
~ 
37, 
400 
transcript-inferred 
human 
alternative 
exons 
maintained 
in 
the 
Hollywood 
database 
(3), 
AS 
events 
of 
about 
10,300 
A5Es 
and 
9,200 
A3Es 
were 
ltered 
for 
short/long 
exon 
splice 
variants 
of 
solely 
one 
proximal/one 
distal 
50ss, 
while 
being 
constitutively 
spliced 
at 
the 
opposite 
site, 
and 
resulted 
to 
5,275 
A5Es 
and 
4,497 
A3Es. 
Stringent 
alignment 
criteria 
were 
imposed 
on 
all 
transcripts: 
1) 
ESTs 
were 
required 
to 
overlap 
at 
least 
one 
the 
co-aligned 
cDNAs; 
2) 
the 
rst 
and 
last 
aligned 
segments 
of 
ESTs 
were 
required 
to 
be 
at 
least 
30 
nucleotides 
in 
length 
with 
90% 
sequence 
identity; 
3) 
the 
entire 
EST 
sequence 
alignment 
was 
required 
to 
extend 
over 
at 
least 
90% 
of 
the 
length 
of 
the 
EST 
with 
at 
least 
90% 
sequence 
identity; 
and 
4) 
realignments 
of 
ESTs 
with 
two 
other 
algorithms 
were 
required 
to 
agree 
in 
three 
out 
of 
all 
three 
independent 
alignments 
(see 
below, 
as 
well 
as 
Methods). 
The 
resulting 
dataset 
of 
identical 
computational 
inferences 
of 
three 
methods 
contained 
1,868 
(~ 
18%) 
A5Es 
and 
3,301 
(~ 
36%) 
A3Es. 
Alternative 
exons 
were 
subdivided 
into 
their 
core 
and 
extension 
parts, 
where 
the 
latter 
is 
the 
sequence 
between 
the 
distal 
and 
proximal 
splice 
sites. 
The 
extension 


(E) 
included 
lengths 
up 
to 
about 
250 
nucleotides, 
with 
quickly 
decreasing 
transcript 
coverage/utilization 
as 
E 
increases. 
Larger 
extensions 
existed, 
albeit 
with 
barely 
more 
than 
a 
few 
transcripts. 
For 
the 
sake 
of 
simplicity, 
the 
boundary 
between 
A5E 
(A3E) 
overlapping 
and 
non-overlapping 
splices 
was 
dened 
at 
E> 
6(E> 
18) 
nucleotides 
and 
the 
distribution 
f(E) 
for 
E 
=1, 
2, 
:::, 
18 
nucleotides 
was 
displayed 
in 
a 
window 
across 
the 
boundary 
region. 
Noticeably, 
the 
obtained 
distribution 
f(E) 
for 
both 
A5Es 
and 
A3Es 
was 
highly 
biased 
for 
extensions 
with 
overlapping 
splice 
sites. 
Figure 
2.2 
shows 
(in 
the 
upper-left 

Biased 
Extensions 
of 
Alternative 
50ss 
and 
30ss 
Exons 


panel) 
that 
for 
extensions 
at 
the 
50ss 
the 
bias 
is 
caused 
predominantly 
by 
a 
peak 
at 
E 
= 
4 
nucleotides. 
It 
further 
shows 
for 
A5Es 
that 
short 
extensions 
exhibit 
a 
small 
but 
persistent 
pattern 
periodically 
occurring 
at 
E 
=6, 
9, 
12, 
15, 
and 
18 
nucleotides, 
all 
multiples 
of 
three, 
and 
thus, 
preserving 
the 
reading-frame. 
These 
patterns 
of 
AS 
for 
short 
extensions 
were 
in 
accord, 
both 
qualitatively 
and 
in 
good 
approximation 
quantitatively, 
in 
an 
independent, 
comparative 
analysis 
for 
the 
mouse 
Mus 
musculus 
(Figure 
2.2, 
lower-left 
panel). 
Overall, 
the 
median 
sizes 
of 
inferred 
alternative 
exons 
showed 
that 
SEs 
and 
A5Es 
tend 
to 
be 
shorter 
than 
CEs 
and 
A3Es, 
while 
overlapping 
and 
skewed 
to 
larger 
sizes 
(Figure 
2.3). 


Somewhat 
unexpectedly, 
Figure 
2.2 
was 
also 
indicative 
that 
dierent 
splicealignment 
algorithms 
gave 
rise 
to 
strikingly 
dierent 
outcomes, 
particularly 
when 
faced 
with 
alignments 
involving 
short 
extensions. 
The 
fraction 
of 
short 
extensions 
(E 
= 
6) 
ranges 
between 
17-38% 
among 
several 
standard 
algorithms, 
with. 
Sim4 
predicting 
most 
liberally 
almost 
40% 
of 
A5Es 
as 
such 
donor 
variations. 
This 
alignment 
algorithm 
also 
suggests 
a 
strong 
tendency 
toward 
E 
= 
4 
nucleotides 
(27% 
cf. 
Table 
2.1). 
A 
conservative 
approach 
was 
taken 
to 
substantiate 
the 
identied 
A5E 
events, 
by 
realigning 
all 
corresponding 
transcripts 
to 
the 
same 
genomic 
sequence 
with 
two 
other 
algorithms, 
Exalin 
and 
Blat 
(the 
latter 
lacks 
an 
explicit 
splice 
site 
model). 
The 
results 
showed 
that 
for 
E 
= 
4 
the 
proportion 
of 
A5E 
events 
derived 
from 
Sim4 
(28%) 
was 
markedly 
higher 
than 
alignments 
derived 
from 
Exalin 
or 
Blat 
-yet 
the 
bias 
for 
extensions 
was 
consistently 
shown 
at 
E 
= 
4 
nucleotides, 
though 
with 
a 
lower 
proportion 
of 
9% 
(Table 
2.1). 
Manual 
inspection 
of 
selected 
Sim4 
alignments 
showed 
apparent 
sequence 
inconsistencies, 
when 
compared 
to 
the 
secondary 
alignments 
(see 
Figure 
B.1). 
In 
all, 
1,868 
of 
5,275 
A5Es 
were 
taken 
for 
further 
analysis, 
where 
9% 
(171/1,868) 
accounted 
for 
E 
= 
4 
nucleotides 
extensions. 



Results 
35 


H.sapiensM.musculus123456789111315170.000.050.100.150.200.250.30FrequencyAB1234567891113151700.050.100.150.200.250.301234567891113151712346789111315170.000.050.100.150.200.250.303´ssexonextension[nt]5´ssexonextension[nt]
24681012141618CD2468101214161800.050.100.150.200.250.30
Figure 
2.2: 
Occurrence 
of 
extensions 
(E 
=1, 
2, 
:::, 
18 
nucleotides) 
for 
A5Es 
(A,C) 
and 
A3Es 
(B,D), 
with 
human 
and 
mouse 
exons 
in 
the 
top 
and 
bottom 
panels, 
respectively. 
Extensions 
were 
inferred 
from 
three 
dierent 
alignment 
algorithms 
(colored 
as 
blue, 
Sim4; 
red, 
Blat; 
and 
green, 
Exalin) 
of 
cDNAs/ESTs 
to 
genomic 
DNA. 
The 
distribution 
f(E) 
for 
A5Es 
was 
markedly 
biased 
for 
extensions 
(E) 
with 
overlapping 
splice 
sites, 
with 
a 
peak 
at 
E 
= 
4 
nucleotides. 
Exon 
extensions 
exhibited 
relatively 
smaller 
but 
persistent 
periodic 
peaks 
at 
E 
=6, 
9, 
12, 
15, 
and 
18 
nucleotides. 
f(E) 
for 
A3Es 
also 
displayed 
a 
bias 
for 
overlapping 
splice 
sites, 
with 
a 
peak 
at 
E 
= 
3 
nucleotides 
and 
smaller 
peaks 
at 
4-6 
nucleotides. 
The 
program 
Sim4 
predicted 
signicantly 
more 
extensions 
at 
E 
= 
4 
nucleotides 
as 
compared 
to 
Blat 
and 
Exalin 
predictions 
of 
the 
same 
initial 
set 
of 
cDNAs/ESTs, 
which 
was 
indicative 
of 
spurious 
alignments. 
A 
comparative 
analysis 
of 
alternative 
exons 
in 
M. 
musculus 
corroborated 
the 
above 
patterns. 



Biased 
Extensions 
of 
Alternative 
50ss 
and 
30ss 
Exons 


A5EP4
-2-1012100150200250-2-1012100300500A5ED4
-3-2-10123050010001500A3E3
-2020CEd4
-202010002000CEp4
-3-2-10123010002000SE01002003004005000000A5EP4
A5ED4
CEd4
CEp4
A3E3
SE2000400000QuantilesofnormaldistributionExonlengthsABExonLengths
Figure 
2.3: 
Length 
distribution 
of 
human 
exons. 
(A) 
box-plots 
of 
constitutive 
and 
alternative 
exon 
lengths 
(median 
and 
interquartile 
range, 
whiskers“ 
denote 
the 
1.5-fold 


"

interquartile 
range). 
Major-proximal 
(P4) 
and 
major-distal 
(D4) 
splicing 
exons, 
cons
titutive 
50ss 
with 
distal 
pseudo 
(d	4) 
and 
proximal 
pseudo 
(p	4) 
splicing 
exons, 
and 
skipped 
exons 
(SEs). 
(B) 
quantile-quantile-plots 
of 
exon 
lengths 
(x-axes, 
quantiles 
of 
normal 
distribution; 
y-axes, 
empirical 
data). 


Table 
2.1: 
Characterization 
of 
alternative 
donor 
splice 
events, 
involving 
exactly 
two 
dierent 
exon 
isoforms. 
Column 
one 
describs 
the 
initial 
dataset 
from 
the 
Hollywood 
database 
(aligned 
with 
Sim4, 
column 
two 
and 
three 
the 
same 
dataset 
after 
renement 
by 
i) 
realigning 
the 
transcript 
isoform 
with 
a 
more 
recent 
spliced 
alignment 
algorithm 
(Exalin) 
and 
ii) 
by 
computing 
the 
overlap 
of 
exon 
isoforms 
with 
those 
stored 
in 
the 
UCSC 
repository 
(computed 
as 
Blat 
alignments), 
respectively. 
tx 
= 
transcript; 
E 
= 
extension 
= 
nucleotide 
dierence 
between 
two 
A5E 
isoforms. 


HOLLYWOOD 
UCSC 
FSU 
(Sim4) 
min. 
1 
tx 
> 
1 
tx 
(Blat) 
min. 
1 
tx 
> 
1 
tx 
(Exalin) 
min. 
1 
tx 
> 
1 
tx 
total 
E 
= 
6 
E 
= 
4 
E 
= 
3 
5,275 
2,011 
(38.1%) 
1,493 
(28.3%) 
76 
( 
1.4%) 
1,284 
555 
(43.2%) 
406 
(31.6%) 
18 
( 
1.4%) 
1,926 
333 
(17.3%) 
179 
( 
9.3%) 
40 
( 
2.1%) 
NA 
NA 
NA 
NA 
1,868 
324 
(17.3%) 
171 
( 
9.2%) 
40 
( 
2.1%) 
504 
92 
(18.2%) 
39 
( 
7.7%) 
14 
( 
2.7%) 


In 
order 
to 
compare 
these 
ndings 
with 
A3E 
events, 
the 
distribution 
of 
short 
extensions 
was 
obtained 
and 
a 
similar, 
albeit 
distinctively 
dierent 
patterns 
identi
ed 
(Figure 
2.2, 
upper-right 
panel). 
Figure 
2.2 
shows 
that 
f(E) 
exhibits 
clear 
peak 
at 
E 
= 
3 
nucleotides, 
with 
successively 
smaller 
peaks 
at 
E 
=4, 
5, 
and 
6 
nucleotides. 
Again, 
these 
AS 
patterns 
were 
corroborated 
in 
a 
comparative 
analysis 
for 
M. 
musculus 
(Figure 
2.2, 
lower-right 
panel). 
The 
extension 
preference 
of 
alternative 
50ss 
and 
30ss 
exons 
is 
in 
accord 
with 
previous 
studies, 
where 
in 
particular 
E 
= 
3 
nucleotides 
for 
A3Es 
had 
been 
examined 
and 
found 
to 
obey 
the 
splicing 
pattern 
at 
the 
Nag/Nag/ 
motif. 



Results 
37 


2.3.2 
Tandem 
Donors 
and 
Acceptors 
Patterns 
of 
A5Es 
and 
A3E 
extensions 
with 
overlapping 
splice 
sites 
are 
interesting 
in 
their 
own 
context, 
because 
they 
are 
1) 
possibly 
dierently 
regulated 
than 
non-overlapping 
alternative 
donor 
or 
acceptor 
splice 
site 
exons 
(89, 
106); 
and 
2) 
predictive 
of 
dierent 
downstream 
eects 
of 
AS, 
resulting 
in 
dierentially 
preferred 
modes 
of 
alternative 
splicing 
at 
the 
50ss 
(predominantly 
out-of-frame 
splicing) 
and 
the 
30ss 
(predominantly 
in-frame 
splicing). 
For 
overlapping 
50ss 
and 
30ss 
are 
mainly 
represented 
by 
extensions 
of 
four 
and 
three 
nucleotides, 
respectively, 
hereafter 
they 
are 
denoted 
by 
A5E4“ 
tandem 
donors 
with 
E 
= 
4 
and 


” 
similarly 
by 
A3E3“ 
tandem 
acceptors 
with 
E 
= 
3 
nucleotides. 
This 
study 


” 
focuses 
on 
tandem 
donors 
with 
respect 
to 
sequence 
features 
that 
are 
known 
to 
be 
involved 
in 
the 
recognition 
of 
50ss, 
and 
compares 
them 
to 
30ss 
of 
alternative 
exons 
as 
well 
as 
constitutive 
exons 
including 
potential 
pseudo 
splice 
sites. 
Generally, 
the 
basic 
recognition 
and 
binding 
to 
50ss 
incorporates 
intronic 
(involving 
positions 
from 
1 
to 
6) 
and 
exonic 
nucleotides 
(positions 
from 
-3 
to 
-1). 
The 
consensus 
motif 
for 
50ss 
of 
mammalian 
genes 
is 
known 
as 
CAG/GTRAGT 
(at 
positions 
P..3P..2P..1/P1P2P3P4P5P6) 
with 
R 
standing 
for 
purine 
bases. 
This 
nine 
nucleotide-long 
motif 
is 
highly 
degenerated 
and, 
in 
fact, 
in 
the 
present 
data 
set 
of 
human 
exons 
only 
proportions 
of 
0.9% 
(966/113,386) 
and 
1.3% 
(1,431/113,386) 
of 
inferred 
constitutive 
exons 
exhibited 
exact 
matches 
to 
the 
motifs 
CAG/GTAAGT 
or 
CAG/GTGAGT, 
respectively. 
Figure 
2.4 
illustrates 
splice 
sites 
and 
utilization 
of 
tandem 
donors 
for 
three 
selected 
human 
genes: 


A. 
The 
gene 
RAD9A 
(Ensembl 
gene-identier 
ENSG00000172613) 
is 
a 
homolog 
conserved 
from 
yeast 
(S.pombe) 
to 
human, 
which 
encodes 
a 
cell 
cyclecheck 
point 
control 
protein 
that 
is 
required 
for 
cell-cycle 
arrest 
and 
DNA 
damage 
repair. 
The 
primary 
transcript 
sequence 
of 
RAD9A 
exhibited 
two 
alternative, 
overlapping 
50ss 
at 
exon 
E8, 
identied 
as 
CAG/GCAG/GT 
at 
the 
distal 
50ss 
and 
CAG/GTAGTT 
at 
the 
proximal 
50ss 
that 
extends 
E8 
(non-consensus 
nucleotides 
are 
underlined; 
exon 
extension 
bolded). 
The 
distal 
and 
proximal 
50ss 
gave 
rise 
to 
three 
and 
17 
mRNAs, 
respectively, 
which 
aligned 
to 
the 
primary 
transcript 
structure 
of 
RAD9A. 
In 
addition 
to 
the 
tandem 
donor 
pattern, 
Figure 
2.4 
shows 
the 
splice 
site 
strength, 
quanti
ed 
by 
the 
MaxEntScan 
score 
(see 
Methods 
2.2.4), 
and 
the 
conservation 
prole 
across 
exons 
and 
intron, 
quantied 
by 
the 
Phastcon 
score 
(107) 
computed 
across 
several 
genomes 
(from 
P. 
troglodytes 
to 
T. 
rubripes). 
Lo

Tandem 
Donors 
and 
Acceptors 


(a)
RAD9A_NM_004584_EIE_cons.txt_ds_intron:chr11:66939093-66939231RAD9A_NM_004584_EIE_cons.txt_ds_intron:chr11:66939007-66939093RAD9HomologA(RAD9A)
gtagttgtagttCTCCAGgcag3E9AL038954E92.53bitCTCCAGGCAGE8E8ctgcccagctgcccagGCCCGCCGCCCGCCRAD9A_NM_004584_EIE_cons.txt_cand_exon:chr11:66938941-6693900600.51176.30bitP4
d4
CF146039%Identity
(b) 
ACAD9_NM_0140049_EIE_cons.txt_ds_intron:chr3:129943813..129943884Acyl-CoenzymeAdehydrogenasefamily,member9(ACAD9)
gtaagtgtaagtTGATTGgtag138E10AF327351E107.03bitD4
TGATTGE9E9ttcctcagttcctcagAAATGACAAATGACACAD9_NM_0140049_EIE_cons.txt_cand_exon:chr3:129942304..12994238000.51BC041572p4
9.66bitGTAG%Identity
(c) 
SFRS16_NM_007056_EIE_cons.txt_ds_intron:chr19:50263517..50263578SFRS16_NM_007056_EIE_cons.txt_ds_intron:chr19:50263152..50263517Splicingfactor,arginine/serine-rich16()SFRS16gtaagagtaagaGCCAAAgtca9E16AF042800E161.98bitGCCAAAGTCAE15E15ctccccagCCCAAGCctccccagCCCAAGCSFRS16_NM_007056_EIE_cons.txt_cand_exon:chr19:50263112..5026315200.517AY358944D4
p4
5.52bit%Identity
Figure 
2.4: 
Illustrative 
examples 
of 
inferred 
tandem 
donors. 
White 
boxes 
denote 
exon 
and 
lines 
intron 
nucleotides; 
exon 
numbers 
(E#) 
corresponded 
to 
50-to-3. 
enumerated 
Ref-
Seq 
annotations, 
the 
splice 
site 
score 
as 
measured 
by 
MaxEntScan, 
and 
the 
transcript 
coverage 
of 
the 
proximal 
and 
distal 
donor 
site 
corresponded 
to 
the 
number 
of 
aligned 
seq
uences. 
In 
(a), 
E8 
of 
the 
RAD9A 
gene 
shows 
a 
tandem 
donor 
with 
extension 
/GCAG/; 
in 
(b) 
E9 
of 
the 
ACAD9 
gene 
shows 
a 
tandem 
donor 
with 
extension 
/GTAG/; 
in 
(c), 
E15 
of 
the 
SFRS16 
gene 
shows 
a 
tandem 
donor 
with 
extension 
/GTCA/. 
Tandem 
donors 
in 


(a) 
and 
(c) 
were 
preferentially 
included 
in 
transcripts. 
The 
conservation 
plot 
(Phastcon 
scores, 
not 
in 
scale 
with 
the 
stated 
exon 
and 
intron 
nucleotides) 
covers 
A5E4 
splicing 
exons, 
as 
well 
as 
adjacent 
introns 
and 
downstream 
exons, 
and 
shows 
alternating 
patterns 
of 
high/low 
levels 
across 
all 
three 
examples. 

Results 
39 


cal 
regions 
of 
high 
levels 
of 
sequence 
conservation 
for 
exons 
compared 
with 
the 
intron 
are 
apparent. 


B. 
A 
tandem 
donor 
was 
detected 
for 
E9 
(TTG/GTAG/GT 
and 
TAG/GTAAGT) 
of 
the 
ACAD9 
gene 
(ENSG00000177646), 
which 
encodes 
a 
member 
of 
the 
acyl-CoA 
dehydrogenase 
gene 
family 
and 
plays 
a 
role 
in 
lipid 
catabolism. 
The 
distal 
and 
proximal 
50ss 
gave 
rise 
to 
13 
and 
eight 
mRNAs, 
respectively. 
Figure 
2.4 
shows 
for 
E9 
consistently 
elevated 
levels 
of 
sequence 
conservation. 
C. 
The 
arginine/serine-rich 
splicing 
factor 
16 
(ENSG00000104859) 
showed 
a 
tandem 
donor 
at 
E15 
(AAA/GTCA/GT 
and 
TCA/GTAAGA). 
Distal 
and 
proximal 
50ss 
choice 
gave 
rise 
to 
nine 
and 
six 
mRNAs 
of 
SFRS16, 
respectively. 
Figure 
2.4 
shows 
that 
the 
level 
of 
sequence 
conservation 
of 
E15 
steadily 
rises 
toward 
the 
30-terminus 
and 
extends 
well 
across 
the 
exon-intron 
junction 
to 
I16, 
before 
it 
rapidly 
decays, 
which 
is 
indicative 
of 
conservation 
with 
splicing-regulatory 
function 
(102). 
2.3.3 
Experimental 
Validation 
of 
Tandem 
Donors 
Having 
obtained 
sucient 
evidence 
from 
stringent 
transcript 
alignments, 
validating 
the 
functional 
utilization 
of 
tandem 
splice 
sites 
from 
independent 
lines 
of 
evidence 
was 
pursued. 
To 
this 
end, 
rst 
publicly 
available 
literature12 
was 
searched 
for 
AS 
events 
involving 
short 
50ss 
extensions. 
Yet 
only 
a 
very 
limited 
number 
of 
reported 
cases 
of 
splice 
variants 
with 
short 
extensions 
that 
could 
be 
traced 
back 
to 
tandem 
donors 
was 
found. 
The 
human 
Clasp 
gene 
(known 
synonyms 
are 
SFRS16, 
SWAP2 
), 
for 
instance, 
encodes 
the 
Clk4-associating 
arginine/serine-rich 
(SR)-related 
protein 
that 
binds 
to 
the 
family 
of 
CDC2-like 
kinases 
(108, 
109). 
The 
50ss 
of 
E15 
of 
the 
Clasp/SFSR16 
is 
an 
alternative 
tandem 
donor, 
which 
gives 
rise 
to 
the 
splice 
forms 
ClaspS 
(with 
the 
extension 
GTCA) 
and 
ClaspL 
(without). 
Both 
isoforms 
dier 
by 
246 
nucleotides, 
where 
ClaspS 
carries 
a 
PTC 
due 
to 
out-of-frame 
splicing 
and 
thereby 
omits 
a 
third 
RS-domain 
encoded 
by 
Clasp/SFSR16. 
Both 
isoforms 
were 
tissue-enriched 
in 
the 
mice 
brain 
and 
testis, 
and 
displayed 
dierent 
intra-nuclear 
locations, 
possibly 
controlled 
by 
the 
third 
RS-domain 
(108). 
Another 
AS 
event 
involving 
tandem 
splice 
sites 
has 
been 
detected 
in 
the 
human 
growth 
hormone 
(GH) 
gene 
cluster, 
whose 
expression 
is 


1http://www.pubmed.org 
2http://apps.isiknowledge.com/ 



Experimental 
validation 
of 
tandem 
donors 


developmentally 
controlled. 
The 
gene 
GH-V 
dierentially 
expressed 
three 
isoforms 
in 
the 
placenta 
and 
testis, 
one 
of 
which 
is 
due 
to 
a 
tandem 
donor 
splice 
site 
(/GTGG/GT) 
of 
exon 
E4; 
the 
tandem 
site 
was 
not 
sequence-conserved 
in 
the 
remaining 
four 
family 
members 
(GGGG/GT). 
The 
use 
of 
the 
distal 
out-of-frame 
splice 
site 
caused 
a 
reading-frame 
shift 
of 
E5 
downstream, 
which, 
in 
turn, 
overread 
the 
original 
termination 
codon 
and 
utilized 
a 
new 
(delayed\) 
termination 


"
codon 
further 
downstream. 
Overall, 
the 
original 
splice 
variant 
and 
GH-V/4 
shared 
124/219 
and 
diered 
by 
95/219 
amino 
acids. 
Clearly, 
the 
detection 
of 
alternative 
tandem 
splice 
site 
exons 
is 
hampered 
due 
to 
the 
high 
similarity 
of 
isoforms 
and 
often 
only 
detectable 
by 
direct 
sequencing 
and 
protein 
sequence 
analysis. 
Consequently, 
an 
experimental 
assay 
was 
performed 
by 
cooperation 
partner 
at 
the 
FLI 
Jena 
to 
explore 
directly 
the 
splicing 
patterns 
of 
computationally 
identied 
alternative 
tandem 
donors. 
Table 
2.2 
list 
the 
names 
of 
a 
set 
of 
14 
genes 
with 
tandem 
acceptors 
( 
8% 
of 
total), 
which 
were 
manually 
selected 
from 
known 
genes 
exhibiting 
a 
varying 
degree 
of 
transcript 
coverage 
(ranging 
from 
one 
to 
35 
transcripts 
for 
tandem 
splice 
site 
usage) 
and 
tested 
in 
a 
battery 
of 
human 
organ 
systems 
and 
cell 
types 
by 
RT-PCR 
primers 
targeted 
to 
the 

anking 
exons; 
panels 
of 
ten 
normal 
tissue 
samples 
(from 
the 
testis, 
brain, 
colon, 
heart, 
kidney, 
small 
intestine, 
spleen, 
thymus, 
ovary, 
and 
leukocytes) 
were 
assayed. 
The 
products 
of 
these 
45 
RT-PCRs 
were 
used 
to 
verify 
the 
identity 
of 
these 
PCR 
products 
by 
sequencing 
(see 
Figure 
2.4, 
as 
well 
as 
Methods 
2.2.7). 
For 
instance, 
Figure 
2.5 
shows 
for 
E15 
of 
SFRS16 
schematically 
the 
gene 
structure, 
proximal 
and 
distal 
sites 
of 
the 
tandem 
donor, 
and 
the 
sequence 
electropherogram 
interrogated 
in 
samples 
derived 
from 
the 
human 
spleen 
and 
blood. 
Upstream 
of 
the 
E15 
tandem 
donor, 
both 
transcript 
sequences 
identically 
overlap 
and 
thus, 
cannot 
be 
distinguished 
in 
the 
electropherogram; 
downstream, 
two 
nucleotide 
signals 
appear 
above 
the 
base 
line, 
indicating 
the 
presence 
of 
two 
isoforms. 



Results 
41 


Table 
2.2: 
Summary 
of 
the 
experimental 
assay 
for 
validating 
computationally 
inferred 
human 
tandem 
donors. 
A5E4 
splicing 
exons 
were 
selected 
according 
to 
both 
transcript 
coverage, 
concordance 
of 
tissues 
inferred 
from 
cDNA-libraries 
of 
A5E4 
genes, 
and 
comm
ercially 
available 
samples. 
RT-PCR 
primers 
were 
targeted 
to 

anking 
exons, 
assayed, 
and 
sequenced. 
In 
the 
last 
column, 
+“ 
indicates 
that 
the 
tested 
A5E4 
splicing 
exon 
was 


"

detected 
to 
be 
present 
in 
both 
splice 
variants 
of 
the 
corresponding 
samples, 
separately 
for 
each 
tested 
tissue 
(a 
bolded 
+“ 
indicates 
the 
major 
form). 
In 
all, 
7/14 
A5E4 
splicing 


"

exons 
were 
veried 
in 
panels 
of 
nine 
normal 
tissues. 
In 
the 
fourth 
column 
(PTC), 
+“ 


"

indicates 
the 
presence 
of 
a 
premature 
termination 
codon. 


Ensembl 
Gene 
name 
Region 
PTC 
Transcript 
Analyzed 
Conrmed 
gene 
coverage 
tissues 
donors 
(ENSG00000#) 
(distal/ 
prox(
distal/ 
proximal) 
imal) 
172613 
RAD9A; 
RAD9 
ho-CDS 
+ 
3/17 
Kidney; 
(+/:); 
molog 
Leukocytes 
(+/:) 
175605 
ZNF32, 
zinc 
nger 
pro-CDS 
+ 
14/2 
Heart; 
(:/+); 
tein 
32 
Leukocytes 
(:/+) 
104859 
SFRS16, 
arginine/ 
CDS 
+ 
9/7 
Leukocytes; 
(+/:); 
serine-rich 
splicing 
Spleen 
(+/:) 
factor 
16 
161574 
CCL15, 
small 
in-CDS 
+ 
35/6 
Colon 
(:/+) 
ducible 
cytokine 
A15 
precursor 
177646 
ACAD-9, 
Acyl-CoA 
CDS 
+ 
13/8 
Brain; 
(:/+); 
(+/-) 
Dehydrogenase 
Fam-Heart 
ily, 
mitochondrial 
Precursor 
148459 
PDSS1, 
Trans-CDS 
+ 
6/2 
Small 
intes(+/+); 
Prenyltransferase 
tine 
(:/+) 
180198 
RCC1, 
regulator 
of 
5UTR 
+ 
4/2 
Small 
intes(
:/+); 
(-/+) 
chromosome 
condensatine; 
Testis 
tion 
170581 
STAT2, 
signal 
trans-CDS 
+ 
8/1 
Brain; 
Thy(+/-); 
(+/-) 
ducer 
and 
activator 
of 
mus 
transcription 
2 
102878 
HSF4, 
heat 
shock 
tran-CDS 
+ 
6/1 
Colona, 
(-/+); 
(-/+) 
scription 
factor 
4 
Braina 
090061 
CCNK, 
cyclin 
K 
CDS 
+ 
17/1 
Leukocytes 
(+/-) 
137502 
RAB30, 
Ras-related 
CDS 
+ 
1/7 
Leukocytes 
(-/+) 
Protein 
RAB-30 
134987 
WDR36, 
WD-Repeat 
CDS 
+ 
1/4 
Leukocytes 
(-/+) 
Prtoeine 
36 
157911 
PEX10, 
peroxisome 
as-CDS 
+ 
3/18 
Brain 
(-/+) 
sembly 
protein 
10 
049656 
CLPTM1L, 
cisplatin 
CDS 
+ 
2/32 
Ovary; 
(-/+); 
(-/+) 
resistance 
related 
Small 
Intesprotein 
CRR9p 
tine 


Table 
2.2 
lists 
the 
outcome 
for 
all 
14 
genes. 
In 
all, 
50% 
(7 
of 
14 
total) 
of 
selected 
A5E4 
splicing 
exons 
showed 
PCR-products 
displaying 
E 
= 
4 
nucleotides 
for 
the 
sets 
of 
interrogated 
alternative 
exons, 
and 
the 
experimentally 
observed 
splice 



Two 
Distinct 
Levels 
of 
A5E 
Proximal 
and 
Distal 
Splicing 


AF042800...ACAGGAGCTGCCAAAGTCACCCAAGCTGACGCCT...gtaagaatttg...ctcccccctccccagSplicingfactor,arginine/serine-rich16()SFRS16121161514...ACAGGAGCTGCCAAACCCAAGCTGACGCCT......ctcccccctccccaggtcagtaagaatttgAY358944...
Figure 
2.5: 
Experimental 
validation 
of 
a 
tandem 
donor 
activated 
in 
E15 
of 
the 
SFRS16 
gene 
using 
RT-PCR 
and 
direct 
sequencing. 
The 
top 
shows 
the 
gene 
structure 
of 
SFRS16 
; 
in 
the 
middle 
and 
bottom, 
E14-16 
are 
schematically 
extracted 
and 
the 
30-end 
core 
and 
full 
extension 
sequence 
of 
E15 
for 
proximal 
(TCA/gtaaga) 
and 
distal 
(AAA/gtcagt) 
splicing 
are 
shown. 
Prior 
to 
reaching 
the 
50ss 
of 
E15, 
both 
mRNA 
isoforms 
cannot 
be 
distinguished 
and 
consequently 
the 
electropherogram 
displays, 
for 
each 
position, 
one 
nucleotide 
signal 
peak 
above 
the 
base 
line. 
After 
the 
tandem 
donor 
site, 
two 
nucleotide 
signals 
above 
the 
base 
line 
become 
visible, 
indicating 
the 
presence 
of 
two 
isoforms. 


ratio 
between 
minor 
and 
major 
form 
was 
in 
agreement 
with 
the 
ratio 
suggested 
by 
EST 
data. 
Six 
of 
seven 
A5E4 
splicing 
exons 
could 
be 
mapped 
to 
protein 
coding 
gene 
sequences 
and 
all 
six 
CDS 
aecting 
alternative 
exons 
create 
a 
PTC. 
For 
human 
tissues 
samples 
were 
tried 
to 
match 
EST-associated 
cDNA 
libraries, 
using 
a 
larger 
battery 
of 
dierent 
organ 
systems 
and 
cell 
types 
might 
validate 
additional 
A5E4 
splicing 
exons 
and, 
therefore, 
this 
approach 
is 
rather 
delivering 
a 
lower 
boundary 
of 
the 
presence 
of 
AS 
events 
involving 
tandem 
donors. 


2.3.4 
Two 
Distinct 
Levels 
of 
A5E 
Proximal 
and 
Distal 
Splicing 
Studies 
of 
the 
inclusion 
and 
exclusion 
of 
skipped 
exons 
of 
the 
human 
and 
mouse 
genomes 
have 
shown 
that 
SEs 
can 
be 
broadly 
subdivided 
into 
two 
types: 
SEs 
that 
are 
included 
in 
the 
majority 
of 
transcripts 
(termed 
major-form\), 
and 


"
those 
that 
are 
predominantly 
excluded 
(minor-form\). 
Interestingly, 
such 


"
SEs 
posses 
dierent 
splicing 
and 
phylogenetic 
properties 
(110). 
Here, 
it 
was 
examined 
whether 
this 
property 
is 
more 
generally 
related 
to 
alternative 
exons, 
by 
analyzing 
the 
transcript 
coverage 
of 
1,816 
A5Es 
with 
one 
proximal/one 
distal 
50ss. 
Figure 
2.6 
A 
shows 
a 
scatter 
plot 
of 
the 
distal 
against 
proximal 
50ss 



Results 
43 


transcript 
coverage 
for 
both 
tandem 
and 
non-overlapping 
donors; 
the 
individual 
transcript 
coverage 
of 
the 
distal 
(proximal) 
splice 
site 
is 
placed 
above 
(on 
the 
right-hand 
side). 
The 
scatter 
plot 
shows 
that 
the 
number 
of 
aligned 
transcripts 
ranges 
from 
a 
single 
transcripts 
up 
to 
more 
than 
one 
hundred, 
with 
the 
average 
centering 
on 
13, 
and 
is 
biased 
toward 
lower 
coverage 
(median 
value 
of 
2). 
The 
ratio 
of 
alternative 
50ss 
usage 
was 
dened 
as 
R 
and 
computed 
for 
human, 
as 
well 
as 
mouse, 
A5Es. 
The 
inset 
of 
Figure 
2.6 
A 
shows 
that 
the 
histogram 
of 
the 
log(R) 
displays 
a 
bimodal 
distribution, 
which 
is 
indicative 
of 
the 
presence 
of 
two 
types 
(or 
subpopulations) 
of 
alternative 
50ss 
exons 
-one, 
which 
is 
characterized 
by 
the 
utilization 
of 
the 
proximal 
over 
the 
distal 
50ss 
(type-I), 
and 
another 
by 
the 
utilization 
of 
the 
distal 
over 
the 
proximal 
50ss 
(type-II). 
This 
is 
reminiscent 
of 
the 
major/minor 
form“ 
denition 
of 
SEs, 
albeit 
here 
it 


"
applies 
to 
both 
A5E 
proximal 
and 
distal 
splice 
sites. 
A 
threshold 
of 
Rc 
=2 
was 
used 
to 
group 
all 
A5Es 
into 
type 
I 
and 
II, 
or 
a 
remaining 
type, 
based 
on 
the 
behavior 
of 
R 
(indicated 
by 
lines 
in 
the 
scatter 
plot 
of 
Figure 
2.6 
A, 
see 
also 
Methods). 
Having 
two 
subpopulations 
of 
tandem 
donors, 
the 
major 
site 
within 
a 
tandem 
is 
denoted 
as 
P4“ 
(proximal) 
or 
D4“ 
(distal), 
whereas 


"” 
the 
notation 
p4“ 
(proximal) 
or 
d4“ 
(distal) 
adresses 
the 
minor 
donor. 


"” 
Similarly, 
for 
non-overlapping 
alternative 
50ss, 
the 
major 
donor 
is 
denoted 
as 


P“ 
or 
D“ 
and 
the 
minor 
donor 
as 
p“ 
or 
d\, 
respectively 
(cf. 
Table 
2.3). 


"” 
""

Figure 
2.6 
B 
shows 
the 
scatter 
plot 
of 
the 
distal 
against 
proximal 
30ss 
transcript 
coverage. 
Here, 
the 
points 
are 
comparatively 
larger 
scattered 
than 
in 
Figure 
2.6 
A 
and 
display 
an 
arrow 
head“ 
like 
structure. 
Using 
the 
same 
threshold 
as 
above, 


” 
no 
clear 
distinction 
between 
splice 
sites 
for 
A3Es 
is 
found. 
Rather, 
the 
data 
are 
consistent 
with 
a 
single 
population 
of 
A3Es, 
and 
the 
inset 
shows 
the 
histogram 
of 
R 
as 
an 
approximately 
unimodal 
shape 
with 
values 
of 
R 
in 
a 
similar 
range 
as 
observed 
for 
A5Es. 
In 
all, 
tandem 
and 
non-overlapping 
A5Es 
comprise 
a 
set 
of 
1,641 
out 
of 
1,868 
(88%) 
exons, 
remaining 
12% 
that 
either 
exceeded 
the 
threshold 
or 
were 
covered 
by 
a 
single 
transcript. 
The 
density 
of 
P. 
and 
D. 
splicing 
exons 
was 
59% 
(type-I) 
and 
41% 
(type-II), 
which 
was 
in 
some 
contrast 
to 
P4 
and 
D4 
of 
type-I 
with 
26% 
(44/171) 
and 
type-II 
with 
69% 
(118/171) 
exons, 
respectively 
(P< 
0:0001; 
Fisher's 
exact 
test). 
Scatter 
plots, 
populations, 
and 
histograms 
were 
corroborated 
in 
a 
comparative 
analysis 
of 
the 
transcript 
coverage 
for 
A5Es 
in 
M. 
musculus 
(data 
not 
shown). 



Two 
Distinct 
Levels 
of 
A5E 
Proximal 
and 
Distal 
Splicing 


120406080100logR1100200300110002000110002000AB120406080100-6-4-20246logR150100150010001500120406080100150010001500#Transcripts#Transcripts#Transcripts#TranscriptsDistal5´ss#TranscriptsDistal5´ss#TranscriptsProximal5´ssProximal5´ssDp
A5EdP
A5E-6-4-20246120406080100

A3EA3E
Figure 
2.6: 
Scatter 
plot 
of 
the 
transcript 
coverage 
of 
non-overlapping 
and 
4 
tandem 
donors 
(A) 
and 
acceptors 
(B). 
Vertical 
and 
horizontal 
axes 
refer 
to 
the 
coverage 
of 
distal 
and 
proximal 
splice 
sites; 
solid 
and 
dotted 
lines 
mark 
the 
transcript 
means; 
A5E4 
and 
A3E3 
splicing 
exons 
are 
bolded, 
green 
and 
blue 
mark 
the 
P 
and 
D 
(major) 
splicing 
exons, 
respectively. 
The 
inset 
shows 
the 
histogram 
of 
the 
log-ratio 
(R) 
of 
the 
coverage 
of 
the 
distal 
over 
the 
proximal 
50ss 
(30ss); 
curves 
marked 
in 
black 
show 
the 
smoothed 
distribution 
(splines, 
R 
package). 
In 
(A) 
the 
coverage 
scatters 
mainly 
along 
the 
vertical 
or 
horizontal 
axis, 
which 
is 
indicative 
of 
preferentially 
including 
or 
excluding 
the 
exon 
extension 
from 
the 
core 
sequence. 
The 
coverage 
pattern 
was 
used 
to 
partition 
all 
A5Es 
into 
two 
main 
types, 
I 
and 
II, 
and 
a 
remaining 
type. 
The 
inset 
shows 
for 
the 
histogram 
of 
R 
a 
bimodal 
shape, 
which 
is 
indicative 
of 
two 
subpopulations 
of 
A5Es 
with 
predominant 
proximal 
or 
distal 
splice 
site 
usage. 
In 
(B) 
the 
overlap 
between 
distal 
and 
proximal 
tandem 
acceptor 
coverage 
is 
comparatively 
broader, 
and 
consequently 
the 
histogram 
of 
R 
exhibits 
a 
unimodal 
shape 
consistent 
with 
a 
single 
population 
of 
A3Es. 



Results 
45 


Table 
2.3: 
Summary 
of 
selected 
features 
analyzed 
for 
A5Es 
with 
non-overlapping 
donors 


(a) 
and 
A5E4 
splicing 
exons 
with 
tandem 
donors 
(b), 
separated 
into 
major 
(P4, 
D4) 
and 
minor 
(d4, 
p4) 
splice 
forms. 
Transcript 
coverage 
denotes 
the 
number 
of 
transcripts 
(full 
length 
cDNAs 
or 
ESTs) 
that 
support 
either 
the 
minor 
or 
the 
major 
form 
donor. 
(a) 
Features 
of 
A5Es 
P. 
(major-form) 
d. 
(minor-form) 
D. 
(major-form) 
p. 
(minor-form) 
Number 
of 
occurrences 
872 
598 
in-frame 
out-of-frame 
Mean 
core 
length 
(nucleotides) 
Mean 
extension 
length 
(nucleotides) 
410 
(47%) 
462 
(53%) 
107 
82 
257 
(43%) 
341 
(57%) 
126 
119 
Trancript 
coverage 
(mRNA/EST) 
Average 
Maxent 
score 
3,603 
/ 
19,709 
7.5 
324 
/ 
924 
-0.5 
2,186 
/ 
13,126 
6.8 
330 
/ 
556 
4.6 


(b) 
Features 
of 
A5E4 
exoms 
P4 
(major-form) 
d4 
(minor-form) 
D4 
(major-form) 
p4 
(minor-form) 
Number 
of 
occurrences 
Mean 
core 
length 
(nucleotides) 
Mean 
extension 
length 
(nucleotides) 
44 
122 
4 
118 
119 
4 
Trancript 
coverage 
(mRNA/EST) 
Average 
Maxent 
score 
159 
/ 
619 
7.5 
20 
/ 
46 
2.8 
531 
/ 
7,000 
7.9 
15 
/ 
144 
-3.9 


2.3.5 
Splice 
Sites 
of 
A5Es 
Score 
Dierently 
between 
Type 
I 
and 
II 
The 
relationship 
between 
dierent 
types 
of 
transcript 
coverage 
and 
sequencecomplementarity 
of 
base 
pairing 
to 
U1 
snRNA 
was 
analysed 
by 
computing 
the 
50ss 
score 
distribution. 
To 
this 
end, 
a 
maximum-entropy 
or 
Markov-random 
eld, 
based 
model 
was 
applied, 
which 
has 
been 
shown 
to 
capture 
additional 
statistical 
signicant 
dependencies 
of 
splicing 
signals 
than 
standard 
position-weight 
matrix 
representations 
(111, 
94), 
to 
score 
the 
50ss 
of 
all 
A5Es 
(see 
also 
Methods 
2.2.4). 
Figure 
2.7A 
shows 
for 
all 
P. 
and 
P4 
splicing 
exons 
of 
type-I 
the 
score 
distribution, 
f(S), 
of 
the 
distal 
against 
proximal 
50ss. 
The 
score 
is 
large 
(S> 
0) 
when 
the 
splice 
site 
is 
`close’ 
to 
the 
consensus 
sequence, 
and 
small 
(S< 
0) 
when 
the 
splice 
site 
shows 
marked 
deviations 
from 
the 
consensus. 
For 
type-I, 
the 
scores 
of 
most 
P. 
and 
P4 
splicing 
exons 
were 
positive, 
ranged 
up 
to 
S 
= 
12 
(unit 
of 
bits), 
and 
clustered 
narrowly 
around 
a 
mean 
value 
of 
SP 
. 
˜ 
SP 
4 
where 
SP 
4 
=7:5 
bits 
(marked 
by 
horizontal 
lines 
in 
Figure 
2.7A). 
In 
contrast, 
scores 
of 
the 
corresponding 
d. 
and 
d4 
(the 
minor-forms) 

uctuated 
more 
broadly, 
and 
mean 
values 
were 
between 
S 
=4:5 
and 
8 
bits 
weaker 
than 
the 
corresponding 



Splice 
Sites 
of 
A5Es 
Score 
Dierently 
between 
Type 
I 
and 
II 


major-form 
splice 
site. 
Interestingly, 
this 
trend 
was 
reversed 
for 
exons 
of 
type-II 
(D, 
D4), 
where 
the 
score 
clustered 
for 
SD. 
and 
SD4 
between 
7 
to 
8, 
yet 
for 
minor-forms 
was 
again 
broadly 
distributed 
and 
clustered 
around 
Sp. 
=4:6 
and 
Sp4 
= 
..3:9, 
respectively. 
The 
dierent 
patterns 
of 
narrow/broad 
scattering 
of 
A5E4 
splice 
site 
strengths 
in 
dependence 
of 
their 
type 
was 
corroborated 
in 
a 
comparative 
analysis 
of 
f(S) 
in 
M. 
musculus 
(see 
Figure 
B.1). 


Observed 
motifs 
(/GTNN/GT) 
of 
proximal 
(P4) 
and 
distal 
(D4) 
tandem 
splice 
sites 
occurred 
with 
markedly 
dierent 
proportions 
(see 
Table 
2.4). 
To 
what 
extent 
were 
the 
observed 
P4 
and 
D4 
splicing 
exons 
dierent 
from 
constitutive 
splicing 
exons 
(CEs) 
with 
pseudo 
donors 
having 
a 
genomic 
predisposition“ 
for 


"
tandem 
splicing 
(but 
not 
observed)? 
This 
was 
examined 
by 
looking 
for 
constitutive 
50ss 
(/GT) 
that 
were 

anked 
by 
another 
GT 
dinucleotide 
at 
a 
distance 
of 
four 
nucleotides 
either 
upstream 
of 
the 
authentic 
50ss 
(denoted 
as 
d	4\) 
or 


"
downstream 
of 
the 
authentic 
50ss 
(p	4\). 
A 
set 
of 
63,008 
constitutive 
splic


"
ing 
exons 
(out 
of 
113,386) 
were 
searched, 
that 
exhibited 
proximal 
and/or 
distal 
pseudo 
tandem 
donors. 
Assuming 
position-independent 
nucleotide 
concentrations, 
the 
expected 
proportions 
would 
be 
10% 
(d	4) 
and 
48% 
(p	4), 
where 
the 
latter 
re
ects 
the 
GT 
motif 
at 
positions 
P5 
and 
P6 
of 
the 
50ss 
consensus. 
It 
was 
found 
that 
the 
frequency 
of 
d	4 
was 
lower 
than 
its 
expected 
occurrence 
and 
was 
present 
only 
in 
4% 
of 
CEs 
(P< 
0:001; 
z-test), 
whereas 
p	4 
was 
similar, 
albeit 
still 
signicantly 
dierent, 
to 
the 
expected 
occurrence 
and 
present 
in 
47% 
of 
CEs 
(P< 
0:001; 
z-test); 
a 
substantial 
proportion 
of 
5% 
(5,211) 
was 
comprised 
by 
GYNN/GYNNGY, 
but 
was 
excluded 
from 
further 
analysis 
to 
avoid 
any 
ambiguity. 
The 
distribution 
f(S) 
for 
the 
above 
sets 
showed 
related 
dierences. 
The 
mean 
scores 
of 
P4 
and 
constitutive 
50ss 
(downstream 
of 
d	4), 
SP 
4 
=7:5 
and 
S50ss 
=7:9, 
were 
about 
equally 
large 
(P< 
0:13, 
Mann-Whitney 
test), 
yet 
Sd	4 
= 
..3:6 
was 
signicantly 
lower 
as 
compared 
with 
Sd4 
= 
2:8 
(P< 
2:2e 
- 
16). 
Similarly, 
the 
mean 
scores 
of 
D4 
and 
constitutive 
50ss 
(upstream 
of 
p	4),SD4 
=7:9 
and 
S50ss 
=8:7, 
were 
found 
to 
be 
similar, 
but 
still 
signicantly 
dierent 
(P< 
0:003), 
whereas 
Sp	4 
= 
..10:2 
was 
signicantly 
lower 
than 
Sp4 
= 
..3:9(P< 
1:9e 
- 
13). 
In 
words, 
minor 
splice 
variants 
of 
tandem 
donors 
(p4, 
d4) 
scored 
larger 
than 
pseudo 
tandem 
donors 
(p	4, 
d	4), 
while 
lower 
than 
50ss 
of 
constitutive 
splicing 
exons, 
and 
were 
consequently 
suciently 
dierent 
from 
pseudo 
splice 
sites, 
despite 
the 
same 
genomic 
motif. 



Results 
47 


Proximal5´ss(c)(d)
Distal5´ssScoreDistributionDp
A5EdP
A5E-40-30-20-10010-30-20-10010(a)(b)
-30-20-10010-40-30-20-10010Distal5´ssScore1.010.20.40.60.8-20151050-5-10-151.010.20.40.60.8-20151050-5-10-15
(e) 
P4 
(f) 
d	4 
29 
6 
35 
4,637 
121 
4,758 
38 
15 
53 
4,789 
273 
5,062 
67 
21 
88 
9,426 
394 
9,920 
(g) 
D4 
(h) 
p	4 
96 
10 
106 
50,939 
1,256 
52,195 
108 
22 
130 
51,503 
1,915 
53,418 


204 


32 


236 
102,422 


3,171 


105,613 


Figure 
2.7: 
(a,b) 
Scatter 
plots 
of 
50ss 
scores 
of 
non-overlapping 
and 
4 
tandem 
donors 
(cf. 
notation 
of 
Figure 
2.6). 
Shown 
are 
the 
individual 
and 
mean 
scores 
for 
(a) 
type-I 
and 


(b) 
type-II 
donors 
respectively, 
marked 
by 
solid/dashed 
lines 
for 
non-overlapping/tandem 
donors; 
(c,d) 
Cumulative 
score 
distributions. 
Shown 
are 
comparisons 
of 
(c) 
P4 
and 
d4 
splice 
sites 
with 
constitutive 
50ss 
and 
d	4 
(pseudo 
distal 
50ss, 
in 
black) 
and 
(d) 
p4 
and 
D4 
splice 
variants 
with 
p	4 
and 
50ss 
(pseudo 
proximal 
50ss, 
in 
black). 
The 
threshold 
at 
which 
the 
curves 
intersect 
(S) 
marks 
the 
accuracy 
at 
which 
sets 
can 
be 
distinguished 
with 
equal 
classication 
errors 
on 
major 
and 
minor 
splice 
variants. 
S* 
˜ 
78% 
for 
P4 
versus 
d4 
(P4/d4) 
and 
S* 
˜ 
92% 
for 
p4/D4, 
and 
S* 
˜ 
95% 
for 
d	4/50ss 
and 
S* 
˜ 
99% 
for 
50ss/p	4. 
(e-h) 
Tables 
of 
exon 
counts 
for 
each 
splice 
type 
above 
and 
below 
S; 
1st 
row 
from 
left 
right: 
TP, 
FP, 
TP+FP; 
2nd 
row: 
TN, 
FN, 
TN+FN; 
3rd 
row: 
TP+TN, 
FP+FN, 
P+N 
(T=True, 
F=False, 
P=Positive, 
N=Negative) 

Splice 
Sites 
of 
A5Es 
Score 
Dierently 
between 
Type 
I 
and 
II 


Table 
2.4: 
Summary 
of 
the 
transcript 
coverage 
for 
all 
possible 
dierent 
motifs 
of 
A5E4 
splicing 
exons. 
The 
coverage 
is 
shown 
for 
major-form 
distal 
(D4) 
and 
proximal 
(P4) 
tandem 
donors. 
Genes 
with 
inferred 
D4 
splicing 
exons 
outnumber 
genes 
with 
P4 
splici
ng 
exons 
about 
2.5-fold, 
which 
is 
re
ected 
in 
their 
overall 
cDNA 
(about 
three-fold) 
and 
EST 
(about 
ten-fold) 
coverage. 
In 
addition 
to 
/GT, 
the 
/GY 
motif 
is 
shown, 
if 
the 
presence 
/GC 
was 
statistically 
signicant. 


Splice 
site 
Distal 
50-splice 
site 
(D4) 
Distal 
50-splice 
site 
(D4) 
motif 
Occurrence 
EST 
mRNA 
Occurrence 
EST 
mRNA 
/GYGA/GT 
36 
2,555 
170 
3 
16 
8 
/GYAA/GT 
32 
2,603 
140 
4 
23 
19 
/GYAG/GT 
27 
922 
118 
19 
372 
75 
/GYAT/GT 
7 
174 
38 
1 
2 
1 
/GYGG/GT 
6 
94 
18 
11 
91 
31 
/GYAC/GT 
2 
50 
8 
2 
50 
8 
/GYCA/GT 
2 
5 
10 
---
/GYGC/GT 
2 
390 
5 
1 
5 
2 
/GYGT/GT 
2 
25 
13 
---
/GYTA/GT 
2 
182 
9 
---
/GYTG/GT 
1 
-2 
3 
60 
15 
/GYCC/GT 
------
/GYCG/GT 
------
/GYCT/GT 
------
/GTTC/GT 
------
/GTTT/GT 
------
118 
7,000 
531 
44 
619 
159 



Results 
49 


2.3.6 
Discriminating 
A5E4 
versus 
Constitutively 
Spliced 
Exons 
The 
dierence 
between 
the 
50ss 
score 
distribution 
f(S) 
of 
major 
and 
minor 
A5E4 
splicing 
exons 
of 
tandem 
donors 
were 
used 
to 
test, 
based 
on 
the 
behavior 
of 
f(S) 
alone, 
how 
accurate 
P4 
can 
be 
distinguished 
from 
d4, 
and 
D4 
from 
p4 
splicing 
exons. 
To 
this 
end, 
for 
type-I 
the 
cumulative 
distribution 
F 
(S(n)), 
with 
n 
=1, 
2, 
:::N, 
was 
computed 
for 
the 
set 
fSP 
4g, 
by 
i) 
rank-ordering 
all 
scores 


S(n) 
from 
the 
smallest 
to 
the 
largest 
score, 
ii) 
calculating 
sN 
= 
S(n), 
and 


n=1::N 


iii) 
normalizing 
F 
(S(n))= 
sn=sN 
. 
By 
construction, 
F 
(S(n)) 
is 
a 
monotonically 
increasing 
function 
of 
S 
and 
takes 
on 
its 
largest 
value 
at 
F 
(S(N)) 
= 
1. 
Similarly, 
G(S)=1 
- 
F 
(S) 
for 
the 
set 
fSd4} 
was 
computed, 
which 
is 
a 
monotonically 
decreasing 
function 
of 
S 
that 
takes 
on 
its 
largest 
value 
at 
G(S(1)) 
= 
1. 
The 
intersection 
of 
F 
(S) 
and 
G(S) 
yields 
for 
each 
set 
the 
accuracy 
at 
which 
fSP 
4} 
and 
fSd4} 
can 
be 
distinguished, 
with 
smallest 
probability 
of 
error 
on 
the 
classi
cation 
of 
both 
sets 
(112, 
113). 
Figure 
2.7 
C 
shows 
for 
P4/d4 
splicing 
exons 
the 
cumulative 
distributions 
F 
(S) 
and 
G(S) 
in 
the 
range 
between 
-20 
and 
15 
units, 
together 
with 
F 
(S) 
and 
G(S) 
for 
constitutive 
50ss 
and 
d	4 
splicing 
exons 
for 
comparison. 
On 
the 
one 
hand, 
it 
was 
found 
for 
P4 
and 
constitutive 
50ss 
that 
F 
(S) 
collapses 
to 
approximately 
one 
curve 
for 
S> 
0, 
and 
that 
constitutive 
50ss 
exhibit 
a 
long 
range 
of 
negative 
scores, 
which 
was 
not 
seen 
for 
tandem 
donors. 
G(S) 
for 
d4 
decays 
similarly 
to 
d	4, 
albeit 
overall 
shifted 
by 
about 
ten 
units 
toward 
larger 
scores, 
and 
hence, 
leads 
to 
a 
greater 
overlap 
between 
the 
F 
(SP 
4) 
and 
G(Sd4) 
as 
compared 
with 
F 
(S50ss) 
and 
G(Sd	4) 
for 
constitutive 
splicing 
exons. 
Consequently, 
the 
accuracy 
A(S* 
=3:5) 
> 
95% 
at 
which 
one 
can 
distinguish 
constitutive 
50ss 
from 
d	4 
is 
larger 
than 
A(7:3) 
= 
78% 
for 
P4/d4. 
On 
the 
other 
hand, 
in 
Figure 
2.7 
D 
for 
D4 
and 
constitutive 
50ss 
with 
p	4 
similar 
relationships 
for 
F 
(S) 
and 
G(S) 
were 
found, 
with 
G(Sp4) 
overall 
shifted 
by 
about 
ve 
bits 
toward 
G(Sp	4). 
Both 
distributions 
are 
wider 
gapped 
than 
observed 
in 
Figure 
2.7 
C, 
and 
thus, 
the 
accuracy 
reached 
A(6) 
= 
92% 
for 
alternative 
and 
A(4:6) 
= 
99% 
for 
constitutive 
splice 
sites, 
respectively. 
Note 
that 
distinguishing 
the 
sets 
above 
by 
means 
of 
a 
50ss 
score 
dierence 
and 
the 
log-likelihood 
dierence 
(LLD), 
presented 
in 
(114), 
are 
closely 
related. 
This 
can 
most 
easily 
be 
seen, 
by 
considering 
splice 
site 
scores 
derived 
from 
a 
standard 
position 
specic 
weight-matrix 
(PSWM) 
model 
with 
independent 
nucleotide 
fre



Nucleotide 
Conservation 
around 
Major 
and 
Minor 
A5E4 
Splice 
Sites 


quencies: 
provided 
the 
PSWM 
background 
model 
remains 
unchanged, 
the 
slice 
site 
score 
dierence 
is 
equal 
to 
the 
LLD. 
For 
the 
Maxent 
splice 
site 
model 
incorporates 
higher-order 
statistical 
dependencies 
between 
nucleotides, 
this 
exact 
relationship 
is 
replaced 
by 
correlated 
values. 
For 
this 
data, 
the 
subsets 
of 
p	4 
and 
d	4 
splice 
sites 
hold 
an 
upper 
limit 
on 
the 
overall 
number 
of 
human 
tandem 
donors, 
where 
the 
pseudo 
splice 
site 
remained 
unobserved 
or 
unutilized. 
Using 
the 
threshold 
scores 
suggested 
from 
discriminating 
P4 
against 
d4 
(S* 
=7:3), 
as 
well 
as 
D4 
against 
p4 
(S* 
=6:0), 
one 
nds 
that 
23 
(0.5%) 
of 
the 
d	4 
set 
and 
530 
(1.0%) 
exons 
of 
the 
p	4 
set 
exceed 
these 
thresholds 
and 
were 
putatively 
classied 
as 
unobserved 
tandem 
donors. 


2.3.7 
Nucleotide 
Conservation 
around 
Major 
and 
Minor 
A5E4 
Splice 
Sites 
Given 
existing 
dierences 
between 
tandem 
donors 
and 
constitutive 
splicing 
exons 
with 
either 
d	4 
or 
p	4 
splice 
sites, 
the 
nucleotide 
conservation 
around 
splice 
sites 
was 
compared 
and 
contrasted 
(cf. 
Table 
2.5). 
To 
this 
end, 
for 
each 
splice 
site 
position 
(Pi) 
the 
nucleotide 
frequencies 
of 
proximal 
and 
distal 
tandem 
donors 
in 
type-I 
and 
II 
was 
computed, 
and 
their 
information 
score 
I 
represented 
by 
individual 
sequence 
logos 
(95) 
(see 
Methods 
2.2.4). 
I 
is 
close 
to 
zero 
in 
the 
absence 
of 
nucleotide 
conservation 
with 
respect 
to 
the 
background, 
and 
increases 
with 
increasing 
conservation 
up 
to 
around 
two 
bit 
per 
sequence 
position. 


Figure 
2.8 
shows 
in 
part 
A) 
pictograms 
for 
constitutive 
50ss 
and 
30ss, 
proximal 
(P4) 
and 
distal 
(D4) 
tandem 
donors, 
as 
well 
as 
A3E3 
splicing 
exons; 
in 
B) 
the 
information 
score 
dierence 
between 
P4 
and 
D4 
tandem 
donors 
to 
constitutive 
50ss, 
respectively; 
and 
in 
C) 
a 
species 
comparison 
of 
splice 
site 
positions 
of 
human 
A5E4 
splicing 
exons 
that 
were 
sequence 
conserved 
at 
positions 
P..4P..3 
or 
P3P4 
in 
exon 
of 
the 
orthologous 
mouse 
gene. 
Base 
frequencies 
of 
P4/d4 
were 
compared 
to 
constitutive 
50ss/pseudo 
d	4 
splice 
sites, 
as 
well 
as 
D4/p4 
to 
50ss/p	4 
splice 
sites, 
in 
order 
to 
identify 
dierences 
in 
the 
base 
composition 
between 
these 
classes. 


On 
the 
one 
hand, 
clear 
statistical 
dierences 
were 
found 
for 
d4/P4 
splicing 
exons 
with, 
e.g., 
signicantly 
lower 
levels 
of 
C 
but 
higher 
levels 
of 
T 
at 
P..3 
(P< 
10..4 
, 
2-test) 
compared 
to 
d	4/50ss 
splicing 
exons. 
Together 
with 
P..2 
and 
P..1, 
which 
show 
a 
signicant 
enrichment 
of 
G 
and 
A 
(P< 
10..4 
, 
2-test) 
of 



Results 
51 


-14-12-10-8-6-4-2123456789101214A5EP4
A5ED4
A3E3
-0.6-0.4-0.20.00.20.40.6ConstitutiveExon(CE)
A5EP4
A5ED4
Alternative5´ssExond4
...GGACGGTCTA-Gppp32,2,7CATTCAABp4proximal3´ssdistal3´ssAlternative3´ssExon(A3E)
-14-7+1+7+14
I[bit]
-14-13-12-11-10-9-8-7-6-5-4-3-2-112345678910111213145´sspositionD4P4
C5´ssposition5´sspositionI[bit]
2.01.002.01.00d4
p4
28nt28nt
Figure 
2.8: 
Splice 
site 
signals 
and 
sequence 
conservation 
around 
splice 
sites. 
(A) 
Pict
ograms 
of 
50ss 
and 
30ss 
of 
constitutive, 
P4 
and 
D4, 
and 
A3E3 
splicing 
exons. 
The 
height 
of 
a 
nucleotide 
represents 
the 
frequency 
of 
occurrence 
at 
a 
given 
position, 
repres
ented 
in 
the 
range 
of 
14 
nucleotides 
around 
the 
splice 
junctions. 
Above 
the 
constitutive 
50ss, 
the 
30-end 
of 
the 
U1 
snRNA 
is 
indicated. 
(B) 
Information 
score 
dierence 
(I) 
bet
ween 
P4 
and 
D4 
splicing 
exons, 
respectively, 
and 
constitutive 
splice 
sites, 
and 
A3E3 
and 
constitutive 
exons. 
For 
each 
position, 
I> 
0(I< 
0), 
indicates 
more 
(lack 
of) 
informat
ion 
of 
an 
alternative 
compared 
to 
a 
constitutive 
splice 
site. 
(C) 
Sequence 
conservation 
of 
human 
P4 
and 
D4 
splice 
sites 
and 
splice 
sites 
of 
exons 
of 
orthologous 
mouse 
genes, 


anchored“ 
at 
major 
splice 
sites 
and 
with 
> 
80% 
exon 
sequence 
identity. 


"


Nucleotide 
Conservation 
around 
Major 
and 
Minor 
A5E4 
Splice 
Sites 


Table 
2.5: 
Pseudo 
tandem 
donors 
occurring 
upstream 
(d	4, 
distal) 
or 
downstream 
(p	4, 
proximal) 
of 
constitutive 
50ss. 
For 
constitutive 
exons, 
possible 
d	4 
motifs 
are 
shown 
in 
rows 
1 
and 
2, 
and 
possible 
p	4 
motifs 
are 
shown 
in 
rows 
3 
and 
4. 
For 
alternative 
A5E4 
exons, 
tandem 
donors 
are 
shown 
in 
the 
last 
two 
rows. 
H=not 
G, 
i.e., 
only 
A,C,T 
(R=not 
C 
or 
T, 
i.e. 
only 
A 
or 
G) 
permitted 
at 
this 
motif 
position. 


Constitutive 
exons 
P4 
proximal 
major 
P. 
proximal 
major 
D4 
distal 
major 
D. 
distal 
major 
GYNN/GYNNYH 
4,910 
(4%) 
----
HNNN/GYNNGY 
GRNN/GYNNGY 
52,887 
(47%) 
----
GYNN/GYNNGY 
5,211 
(5%) 
----
HNNN/GYNNHY 
GRNN/GYNNHY 
50,348 
(44%) 
----
/GYNN/GYNNGY 
/GYNN/GYNNHY 
-
-
22 
(50%) 
22 
(50%) 
419 
(48%) 
453 
(52%) 
26 
(22%) 
92 
(78%) 
235 
(39%) 
363 
(61%) 
113,356 
44 
872 
118 
598 


d4/P4 
over 
50ss/d	4 
splicing 
exons, 
respectively, 
P..2 
possibly 
mismatches 
to 
U1 
snRNA 
upon 
binding 
to 
P4, 
while 
P..3 
and 
P..1 
possibly 
support 
splicing 
upon 
binding 
to 
d4 
due 
to 
sequence-complementarity 
of 
base 
pairing 
with 
U1 
snRNA. 
Other 
elevated 
levels 
of 
d4/P4 
splicing 
exons 
were 
found 
for 
T 
at 
P..12 
(P< 
10..4), 
A 
at 
P..6 
(P< 
0:05), 
G 
at 
both 
P..5 
and 
P5 
(P< 
0:05), 
and 
C 
or 
T 
at 
P6 
(P< 
10..4 
, 
2-tests). 
On 
the 
other 
hand, 
D4/p4 
splicing 
exons 
showed 
a 
signicant 
decrease 
(increase) 
of 
A 
(T) 
at 
P..2 
(P< 
0:02) 
worsening 
the 
match 
with 
U1 
snRNA 
for 
both 
D4 
and 
p4, 
while 
an 
increase 
of 
A 
at 
P8 
(P< 
0:01) 
and 
T 
at 
P10 
(P< 
0:02, 
2-tests) 
improved 
the 
U1 
snRNA 
sequence-complementarity 
of 
p4 
over 
p	4. 
In 
all, 
several 
splice 
site 
positions 
were 
dierently 
depleted 
or 
elevated, 
often 
with 
the 
possibility 
to 
enhance 
the 
sequence-complementarity 
to 
U1 
snRNA 
(115, 
116, 
117, 
118). 
In 
particular, 
G 
at 
position 
P..1 
has 
been 
attributed 
as 
crucial 
for 
U1 
but 
not 
U5 
snRNA 
base 
pairing, 
creating 
stacking 
eects 
to 
G 
at 
P1 
(119). 
The 
conservation 
of 
P..1 
and 
P5 
observed 
for 
A5E4 
major-forms, 
as 
well 
as 
A5Es 
and 
CEs 
but 
also 
for 
d4 
splicing 
exons 
(type-I), 
is 
in 
agreement 
with 
an 
association 
of 
those 
positions 
reported 
by 
Carmel 
et 
al. 
(119). 
Additionally, 
P..7 
and 
P..6 
of 
d4/P4 
splicing 
exons 
showed 
elevated 
levels 
of 
A 
over 
d	4/50ss 
and 
could 
promote 
U5 
snRNA-dependent 
base 
pairing 
via 
uridines 
in 
the 
U5 
invariant 
loop, 
suggested 
to 
compensate 
for 
weaker 
U1 
snRNA 
anity 
(119) 
(neither 
d	4/50ss 
nor 
p4 
splicing 
exons 
showed 
here 
elevated 
levels). 
The 
dierent 
conservation 
levels 
were 
in 
accord 
with 
the 
average 
information 



Results 
53 


score 
that 
takes 
into 
account 
the 
frequency 
of 
all 
nucleotides, 
at 
a 
given 
position, 
against 
a 
background 
level. 
Figure 
2.8 
B 
shows 
the 
dierence 
I 
between 
tan-
dem 
and 
constitutive 
50ss, 
which 
is 
positive 
(negative) 
for 
higher 
(lower) 
scores 
of 
tandem 
against 
constitutive 
50ss. 
It 
was 
found 
that 
d4/P4 
splicing 
exons 
carried 
overall 
more 
information 
at 
P..12,P..6{P..2, 
and 
P..3, 
but 
as 
well 
at 
P..5, 
whereas 
it 
was 
found 
that 
D4/p4 
carried 
less 
information 
at 
P..2 
and 
P..1, 
but 
more 
at 
P5 
and 
P6. 
Interestingly, 
Figure 
2.8 
B 
shows 
no 
marked 

uctuations 
of 
I 
between 
tandem 
and 
constitutive 
30ss. 
Figure 
2.8 
C 
supports 
the 
above 
mentioned 
positional 
constraints 
detected 
for 
type-I 
and 
type-II 
alternative 
splicing. 
It 
shows 
the 
conservation 
around 
major 
(P4, 
D4) 
splice 
sites 
between 
human 
A5E4 
splicing 
exons 
and 
mouse 
exons 
of 
orthologous 
genes, 
`anchored’ 
at 
/GT 
or 
/GC 
splice 
sites, 
respectively 
(the 
major 
site, 
but 
not 
the 
minor 
site, 
is 
conserved 
by 
dataset 
construction). 
D4/p4 
splicing 
exons 
only 
conserved 
positions 
P5 
and 
P6, 
whereas 
d4/P4 
showed 
two 
recognizable 
overlapping 
50ss 
(positions 
P..4{P..2 
and 
P1{P6) 
with 
nucleotides 
within 
the 
alternative 
part 
being 
complementary 
to 
U1 
snRNA 
(119). 


2.3.8 
Higher 
Levels 
of 
Intron 
Conservation 
near 
Type 
I 
Tandem 
Donors 
Exon 
and 

anking 
sequences 
of 
alternative 
conserved 
exons, 
or 
ACEs, 
of 
orthologous 
human 
and 
mouse 
genes 
exhibit 
signicant 
higher 
levels 
of 
sequence 
conservation. 
This 
has 
most 
clearly 
been 
demonstrated 
for 
ACEs 
that 
undergo 
exon-skipping 
(14, 
37, 
120), 
and 
has 
also 
been 
shown 
for 
smaller 
sets 
of 
A5Es 
and 
A3Es, 
including 
A3E3 
tandem 
acceptors 
(14, 
82). 
Such 
conservation 
could 
imply 
the 
utilization 
of 
splicing 
regulatory 
signals 
that 
are 
common 
to 
orthologous 
sets 
of 
genes. 
It 
was 
examined 
whether 
A5Es 
and 
their 

anking 
regions 
exhibited 
higher 
sequence 
conservation 
when 
compared 
with 
constitutive 
exons. 
To 
this 
end, 
the 
set 
of 
exons 
arising 
from 
tandem 
(overlapping) 
and 
non 
overlapping 
alternative 
splice 
sites 
were 
mapped 
to 
exons 
of 
orthologous 
mouse 
genes. 
Imposing 
a 
level 
of 
at 
least 
80% 
sequence 
identity 
and 
canonical 
splice 
sites, 
matches 
for 
about 
75% 
of 
P4 
and 
90% 
of 
D4 
splice 
variants 
were 
obtained. 
For 
each 
species, 
the 
sequences 
of 
exons 
and 
up 
to 
200 
nucleotides 
of 
their 

anking 
sequences 
downstream 
of 
the 
donor 
splice 
sites 
were 
extracted, 
and 
the 
conservation 
levels 
for 
exon 
and 
intron 
regions 
assessed 
(see 
Methods 
2.2.6). 
As 
control 
sets, 
536/653 



Occurrence 
of 
Splicing 
Signals 
in 
Exon 
Flanking 
Sequences 


A3E3 
splicing 
exons; 
a 
randomly 
selected 
subset 
of 
CEs 
with 
4,145/4,910 
and 
4,082/4,910 
up-(d	4) 
and 
downstream 
(p	4) 
pseudo 
splice 
sites, 
respectively; 
and 
a 
randomly 
selected 
subset 
of 
2,705/4,910 
SEs 
were 
mapped. 
Note 
that 
exons 
of 
orthologous 
mouse 
genes 
can 
be 
constitutive 
or 
alternative 
and, 
if 
so, 
of 
the 
same 
or 
a 
dierent 
AS 
type. 
Figure 
2.9 
A 
shows 
for 
P4 
test 
and 
control 
sets 
the 
exon 
conservation 
as 
a 
combined 
score, 
and 
the 
intron 
conservation 
in 
the 
range 
between 
one 
and 
100 
nucleotides. 
Similarly, 
Figure 
2.9 
B 
shows 
for 
D4 
test 
and 
control 
sets 
the 
exon 
and 
intron 
conservation. 
Test 
sets 
have 
smaller 
overall 
sizes 
than 
the 
controls, 
and 
therefore 
possess 
larger 
statistical 

uctuations. 
For 
both 
exons 
and 
introns, 
the 
highest 
level 
of 
conservation 
was 
observed 
for 
the 
control 
set 
of 
human 
SEs, 
which 
exhibit 
a 
clear 
enrichment 
over 
tandem 
donor 
A5Es 
and 
the 
remaining 
controls, 
in 
accord 
with 
previous 
analyses 
(37; 
120; 
15). 
On 
the 
one 
hand, 
for 
intron 

anking 
regions 
of 
P4 
splicing 
exons 
a 
markedly 
higher 
level 
of 
conservation 
was 
found 
as 
compared 
with 
CEs, 
ranging 
up 
to 
80 
nucleotides 
(Figure 
2.9 
A), 
while 
for 
intron 

anking 
regions 
of 
D4 
splicing 
exons 
a 
conservation 
level 
similar 
to 
CEs 
was 
observed 
(Figure 
2.9 
B). 
On 
the 
other 
hand, 
Figure 
2.9 
A 
and 


2.9 
B 
show 
no 
marked 
dierences 
of 
exon 
conservation 
levels 
between 
sequences 
of 
A5E4 
and 
the 
control 
sets 
(except 
SEs), 
and 
for 
all 
investigated 
exon 
types 
the 
average 
conservation 
level 
was 
found 
between 
80% 
and 
85%. 
Previous 
analyses 
used 
datasets 
enriched 
by 
AS 
events 
that 
were 
specically 
conserved 
between 
exons 
of 
orthologous 
human 
and 
mouse 
genes 
(also 
being 
smaller 
sized 
(14)), 
and 
a 
follow-up 
study 
incorporating 
such 
data 
did 
not 
distinguish 
between 
P4 
and 
D4 
splicing 
exons 
(121). 
2.3.9 
Occurrence 
of 
Splicing 
Signals 
in 
Exon 
Flanking 
Sequences 
The 
above 
analyses 
suggested 
a 
higher 
downstream 
intron 
conservation 
of 
P4 
as 
compared 
to 
D4 
and 
constitutive 
splicing 
exons, 
in 
conjunction 
with 
a 
different 
splice 
site 
score 
between 
the 
major 
and 
minor 
splice 
variants. 
It 
was 
examined 
whether 
the 
occurrence 
of 
splicing-regulatory 
elements 
could 
explain 
the 
observed 
dierences 
(see 
Methods 
2.2.8). 
To 
this 
end, 
it 
was 
searched 
for 
over-representations 
of 
known 
oligonucleotides 
(six 
to 
seven-mers) 
implicated 
in 
splicing 
regulation, 
which 
were 
enriched 
in 
A5E4 
over 
constitutive 
exon 

anking 
regions 
from 
one 
to 
100 
nucleotides. 
Four 
sets 
of 
previously 
computationally 



Results 
55 


P4
0.40.60.81.0HYNNgyag5´ssCAGYGYNNgyagp4
GYNNgynnhyagd4
gynn-10.28.77.9-3.6Intronposition120406080100P4
d4
7.52.8HYNNgyagD4
p4
-3.97.9GYNNCEA3E3SEA5ESequenceidentity[%]
0.40.60.81.0D4
CEA3E3SEA5EIntronposition1204060801005´ssSequenceidentity[%]
20406080100Elements/20nt0123204060801000123A5EP4CE5´ss204060801000123A5ED4CE5´ss204060801000123FAS2-ESSFAS2-ESSPESSPESS20406080100Intronposition012345620406080100Intronposition0123456IREIREESEESE-100-80-60-40-20Exonposition0123456-100-80-60-40-20Exonposition0123456A5ECEElements/20ntElements/20ntElements/20ntIntronpositionIntronpositionIntronpositionIntronpositionABCD
Figure 
2.9: 
Sequence 
conservation 
and 
splicing 
regulatory 
elements 
of 
A5E4, 
A3E3, 
and 
SEs 
of 
orthologous 
human 
and 
mouse 
genes. 
(A) 
and 
(B) 
show 
for 
dierent 
AS 
types 
graphs 
of 
the 
mean 
exon 
conservation 
and 
of 
the 
mean 
conservation 
of 
exon 

anking 
sequences 
up 
to 
100 
nucleotides 
downstream, 
respectively. 
The 
conservation 
is 
shown 
individually 
for 
P4 
(panel 
A, 
green) 
and 
D4 
(panel 
B, 
blue) 
splicing 
exons; 
extension 
regions 
of 
A5E4 
splicing 
exons 
were 
excluded. 
(C) 
and 
(D) 
show 
plots 
of 
occurrences 
of 
dierent 
splicing 
regulatory 
elements, 
located 
within 
the 
rst 
100 
nucleotides 
of 
exon 

anking 
sequences 
that 
share 
> 
80% 
exon 
identity 
and 
splice 
site 
signals 
with 
mouse 
exons. 



A5E4 
Splicing 
Exons 
often 
Produce 
NMD 
Target 
Substrates 


and/or 
experimentally 
identied 
cis-elements 
were 
utilized: 
FAS2-ESS 
(A) 
and 
PESS 
elements 
(B), 
IREs 
(C), 
as 
well 
as 
ESE 
elements 
(D). 
Figure 
2.9 
C 
compares 
for 
P4 
splicing 
exons 
the 
frequency 
of 
occurrences 
of 
all 
four 
sets 
of 
sequence 
elements, 
binned 
to 
non-overlapping 
20 
nucleotide 
windows 
and 
separated 
for 
type-I 
and 
-II, 
against 
the 
control. 
Similarly, 
Figure 
2.9 
D 
shows 
for 
D4 
splicing 
exons 
the 
frequency 
of 
occurrences 
of 
all 
four 
sets 
of 
sequence 
elements. 
For 
introns, 
and 
both 
P4 
and 
D4 
splicing 
exons, 
a 
generally 
higher 
frequency 
of 
sequence 
elements 
from 
sets 
A 
and 
C 
was 
found, 
particularly 
from 
the 
start 
of 
the 
splice 
junction 
to 
about 
40 
nucleotides 
downstream, 
while 
elements 
of 
set 
B 
are 
dierentially 
enriched 
in 
P4 
and 
suppressed 
in 
D4 
splicing 
exons. 
Sequence 
elements 
in 
exons 
(set 
D) 
were 
indicative 
of 
a 
general 
enrichment 
of 
ESEs 
in 
P4 
splicing 
exons, 
particularly 
from 
about 
40 
nucleotides 
upstream 
to 
the 
splice 
junction, 
which 
was 
not 
found 
for 
D4 
splicing 
exons 
(except 
for 
a 
peak 
at 
about 
60 
nucleotides 
upstream 
the 
splice 
junction). 
Exon 
E15 
of 
the 
gene 
SFRS16, 
e.g., 
showed 
two 
purine-rich 
motifs, 
GGGGGGC 
and 
GGTGGG, 
located 
at 
65 
and 
87 
nucleotides 
downstream 
of 
the 
50ss 
(contained 
in 
sets 
A 
and 
B), 
respectively. 
Additional 
hexamers 
were 
located 
between 
the 
positions 
117 
and 
123 
nucleotides 
(GGGAGG), 
while 
other 
sequence 
elements 
(set 
C) 
occurred 
often 
closer 
to 
the 
E15 
proximal 
donor 
of 
SFRS16, 
between 
ve 
and 
30 
nucleotides. 
Poly(G)-rich 
sequence 
elements 
are 
binding 
sites 
for 
the 
family 
of 
hnRNP 
splicing 
regulators 
(122) 
and 
have 
been 
implicated 
in 
the 
control 
of 
50ss 
choice 
(123, 
124, 
125). 
Interestingly, 
a 
phylogenetically 
conserved 
poly(G)-rich 
sequence 
element 
has 
previously 
been 
reported 
as 
involved 
in 
the 
selection 
of 
tandem 
/GTNNNN/GA 
splice 
sites 
in 
the 
splicing 
of 
the 
human 
FGFR 
gene 
(126). 


2.3.10 
A5E4 
Splicing 
Exons 
often 
Produce 
NMD 
Target 
Substrates 
Inferred 
AS 
events 
of 
A5E4 
and 
A3E3 
splicing 
exons 
showed 
a 
splicing 
di


"
chotomy“ 
between 
the 
50ss 
and 
30ss 
– 
while 
AS 
events 
of 
the 
latter 
result 
in 
subtle 
but 
perhaps 
biologically 
signicant 
in-frame 
variation 
of 
a 
single 
amino-acid, 
tan-
dem 
donors 
result 
in 
out-of-frame 
shifts 
downstream 
of 
the 
tandem 
donor 
and 
could 
thus, 
lead 
to 
a 
truncated 
protein 
with 
dierent 
function 
or 
unproductive 
splicing, 
depending 
on 
position 
of 
the 
exon 
(Figure 
2.10). 
Indeed, 
regulated 
unproductive 
splicing 
and 
translation 
(RUST) 
has 
been 
proposed 
to 
be 
a 
mech



Results 
57 


5´partMiddlepart3´partA5E4
A5ELocationwithintranscript0.00.10.20.30.40.5Frequency
Figure 
2.10: 
Occurrences 
of 
A5E 
and 
A5E4 
exons 
in 
Refseq 
sequences. 
Each 
sequence 
was 
subdivided 
into 
three 
segments 
of 
equal 
size 
(50, 
center, 
and 
30), 
and 
mapped 
A5E 
and 
A5E4 
splicing 
exons 
were 
recorded 
in 
their 
respective 
segments. 


anistic 
link 
between 
AS 
and 
the 
NMD 
quality 
control 
pathway 
(96, 
127). 
What 
is 
the 
proportion 
of 
A5E4 
splicing 
exons 
in 
the 
present 
data 
set 
that 
might 
be 
subjected 
to 
NMD? 
To 
address 
this 
question 
it 
was 
i) 
the 
initially 
obtained 
A5E 
annotation 
standardized“ 
by 
matching 
it 
with 
Refseq-annotated 
sequences; 


” 
ii) 
Refseq 
sequences 
with 
complete 
exon-intron 
structures 
and 
annotated 
startstop 
codons 
of 
protein 
coding 
sequence 
(CDS) 
regions 
identied; 
and 
iii) 
proximal 
and 
distal 
splice 
sites 
imposed, 
and 
the 
altered 
reading-frame 
and 
stop 
codon 
position 
downstream 
of 
A5E4 
splicing 
exons 
recalculated. 
Possible 
compensating 
AS 
events 
were 
neglected 
at 
this 
step 
(Figure 
2.10). 
The 
detection 
of 
in-frame 
stop 
codons 
is 
schematically 
sketched 
in 
Figure 
2.11. 
In 
all, 
153/171 
(90%) 
inferred 
A5E4 
splicing 
exons 
were 
conrmed 
by 
at 
least 
one 
Refseq 
sequence, 
which 
broke 
down 
to 
111/153 
and 
44/153 
for 
distal 
and 
proximal 
donors, 
respectively. 
A 
large 
majority 
of 
A5E4 
splicing 
exons 
(94%) 
was 
located 
in 
CDS 
regions, 
with 
only 
marginal 
proportions 
in 
the 
50-untranslated 
region 
(50-UTR) 
or 
30-UTR. 
During 
splicing, 
choice 
of 
the 
out-of-frame 
tandem 
donor 
will 
create 
an 
mRNA 
isoform 
with 
an 
in-frame 
stop 
codon 
that 
introduces 
a 
premature 
termination 
codon 
(PTC) 
and 
shortens 
the 
C-terminus 
in 
97% 
of 
all 
considered 
cases. 
Tandem 
splicing 
of 
exon 
E8 
of 
the 
human 
RAD9 
gene 
at 
the 
minor 
distal 
donor 
d4, 
e.g., 
truncates 
the 
RAD9 
domain 
by 
52 
amino 
acids 
(15% 
of 
total 
length). 
While 
possibly 
still 
maintaining 
the 
domain 
functionality, 
the 
loss 
of 
four 
C-terminal 
phosphoserines 
could 
prevent 
the 
interaction 
with 
the 
(9-1-1) 
cell-cycle 
checkpoint 
response 
complex 
(128). 
In 
all, 
about 
threequarters 
(78%) 
of 
PTCs 
were 
located 
more 
than 
50 
nucleotides 
upstream 
of 
the 
last 
exon-exon 
junction, 
and 
thus, 
predicted 
to 
produce 
a 
marked 
proportion 
of 



A5E4 
Splicing 
Exons 
often 
Produce 
NMD 
Target 
Substrates 


5´UTR3´UTRCDSSSSSS50nt78%
4%94%2.0%
97%3%DTCPTCA5E4
HetereogeneousnuclearribonucleoproteinU(HNRPU)
HighmobilitygroupAT-hook1protein(HMGA1)
Transcriptionassociatedrecombinationprotein(PCID2)
ADP-ribosylationfactor-like2bindingprotein(ARL2BP)
Basalcelladhesionmolecule(BCAM)
Integrinalpha1(ITGA1)
Sortingnexin14(SNX14)
CellsurfaceglycoproteinCD44(CD44)
Zincfingerprotein259(ZNF259CyclinK(CCNK)
Karyopherinbeta1(KPNB1)
SignalTransducerandActivatoroftranscription2(STAT2)
DNAdamagecheckpointprotein(RAD9)
DNArecombinationandrepairprotein(MRE11A)
ATP-dependentRNAhelicase(DDX1)
ATP-dependentRNAhelicase(DDX24)
HeterogeneousnuclearribonucleoproteinA1(HNRPA1)
HeterogeneousnuclearrnpU-likeprotein1(HNRPUL1)
pre-mRNAprocessingfactor3(PRPF3)
...
22%
NMD
Figure 
2.11: 
Annotation 
of 
A5E4 
splicing 
exons 
in 
Refseq 
genes. 
Percentages 
refer 
to 
fractions 
of 
A5E4 
splicing 
exons 
located 
in 
the 
50-UTR, 
coding 
sequence 
(CDS) 
region, 
or 
30-UTR. 
A 
black-colored 
s“ 
indicates 
the 
position 
of 
the 
stop 
codon 
relative 
to 
the 


"

Refseq 
transcript 
structure, 
whereas 
the 
red-colored 
version 
indicates 
the 
altered 
stop 
codon 
due 
to 
tandem 
donor 
splicing. 
A5E4 
splicing 
exons 
embedded 
within 
CDS 
regions 
are 
broken 
down 
into 
two 
categories, 
depending 
on 
the 
creation 
of 
a 
premature 
(PTC) 
or 
delayed 
termination 
codon 
(DTC). 
PTCs 
can 
signal 
mRNAs 
as 
substrates 
for 
non-sense 
mediated 
decay. 


NMD 
substrates 
(10). 
In 
contexts 
of 
type-I 
and 
-II, 
more 
than 
twice 
(69%) 
NMD 
candidates 
were 
produced 
by 
D4 
splicing 
exons 
(where 
splicing 
of 
p4 
produced 
PTCs), 
as 
compared 
with 
26% 
P4 
splicing 
exons 
(where 
splicing 
of 
d4 
produced 
PTCs). 
The 
reminder 
of 
about 
5% 
of 
NMD 
candidates 
did 
not 
stem 
from 
type-I 
or 
II. 


Interestingly, 
a 
small 
number 
of 
A5E4 
splicing 
exons 
( 
3%) 
was 
going 
to 
avoid 
the 
truncation 
of 
the 
transcript 
due 
to 
the 
out-of-frame 
shift 
but 
instead 
extended 
the 
message. 
In 
close 
relation 
to 
premature 
termination 
codons 
(PTCs), 
these 
were 
termed 
delayed“ 
termination 
codons 
(DTCs), 
and 
all 
detected 
DTCs 


"
were 
produced 
from 
utilization 
of 
the 
minor 
donor 
(p4). 
For 
instance, 
tandem 
splicing 
at 
the 
p4 
donor 
of 
exon 
E13 
of 
the 
HNRPU 
gene 
(ENSG00000153187), 
which 
encodes 
the 
heterogeneous 
nuclear 
ribonucleoprotein 
(hnRNP) 
U, 
extended 



Results 
59 


the 
CDS 
regions 
by 
27 
amino 
acids. 
Due 
to 
the 
frame 
shift 
and 
the 
occurrence 
of 
synonymous 
and 
non-synonymous 
codons, 
the 
amino-acid 
sequence 
is 
changed 
such 
that 
the 
complexity 
at 
the 
protein 
level 
(determined 
by 
the 
software 
Smart 
(129)) 
increases 
at 
the 
C-terminal 
end. 



Discussion 


2.4 
Discussion 
This 
comparative 
study 
distinguishes 
tandem 
50ss 
and 
30ss, 
with 
three 
to 
six 
nucleotides 
long 
extensions, 
as 
having 
unusually 
high 
proportions 
of 
alternative 
splicing. 
The 
study 
further 
analyzed 
dierences 
and 
similarities 
between 
sets 
of 
A5Es, 
A3Es, 
and 
CEs, 
and 
focused 
on 
a 
particular 
type 
of 
a 
pair 
of 
alternative 
donors 
(A5E4) 
that 
are 
tandemly 
arrayed 
and 
overlapping. 
These 
tan-
dem 
donors 
were 
experimentally 
validated 
in 
a 
panel 
of 
dierent 
human 
tissues, 
and 
a 
dichotomy 
in 
the 
splicing 
frequency 
at 
these 
alternative 
tandem 
splice 
sites 
is 
highlighted. 
The 
results 
indicate 
that 
human 
alternative 
exons 
spliced 
at 
overlapping 
50ss 
possess 
features 
of 
typical 
splice 
variants 
and 
could 
well 
be 
benecial 
for 
the 
cell. 
Alternative 
splicing 
is 
essential 
for 
protein 
diversication 
and 
has 
recently 
been 
suggested 
as 
mechanistically 
linked 
to 
post-transcriptional 
gene 
regulation 
via 
nonsense 
mediated 
mRNA 
decay 
(NMD) 
(130). 
The 
consequences 
for 
protein 
sequence 
and 
function 
alteration, 
as 
well 
as 
triggering 
of 
the 
NMD 
pathway, 
have 
been 
demonstrated 
for 
exon-skipping 
events 
in 
several 
studies 
(131, 
132, 
133). 
While 
there 
is 
further 
evidence 
for 
the 
functioning 
and 
regulation 
of 
the 
remaining 
types 
of 
alternative 
exons 
(121), 
present 
understanding 
of 
their 
sequence 
evolution, 
produced 
AS 
patterns, 
regulation, 
and 
functioning 
still 
remains 
vague 
(134). 
Alternative 
50ss 
exons 
(A5Es) 
were 
computationally 
inferred 
from 
a 
collection 
of 
stringently 
aligned 
cDNA 
and 
EST 
sequences 
to 
the 
human 
genome, 
and 
their 
sequence 
features 
were 
compared 
to 
known 
features 
involved 
in 
RNA 
splicing. 
Spliced-alignments 
were 
obtained 
from 
the 
three 
independent 
algorithms 
(Sim4, 
Blat, 
and 
Exalin). 
Exalin 
detected 
the 
smallest 
number 
of 
subtle 
AS 
patterns, 
which 
are 
characteristic 
of 
tandem 
donors 
(involving 
just 
a 
few 
nucleotides 
long 
extensions), 
most 
of 
which 
were 
also 
identied 
by 
Sim4 
and 
Blat. 
For 
there 
is 
no 
unique 
true“ 
method 
of 
inferring 
AS 
events, 
all 
analyses 
were 
based 


” 
on 
the 
subset 
dened 
by 
the 
intersection 
of 
the 
predictions 
of 
all 
three 
algorithms. 
While 
one 
cannot 
rule 
out 
misalignments 
still 
arising 
from 
three 
methods 
in 
some 
instances, 
such 
rigor 
was 
taken 
to 
produce 
a 
condence-enriched 
set. 
In 
addition, 
other 
independent 
lines 
of 
evidence 
were 
pursued 
as 
for 
example 
the 
experimentally 
validatation 
of 
a 
subset 
of 
14 
human 
genes 
with 
tandem 
donors 
across 
dierent 
tissues. 
The 
outcome 
conrmed 
about 
50% 
A5E4 
splicing 
exons 
and 
provided 
evidence 
that 
a 
substantial 
fraction 
of 
tandem 
donors 
detectable 
in 
public 
sequence 
repositories 
are 
not 
explained 
by 
sequence 
alignment 
ambi



Discussion 


guities. 
Almost 
one 
tenth 
of 
all 
human 
A5Es 
with 
exactly 
one 
shorter 
and 
one 
longer 
splice 
variant, 
and 
no 
other 
inferred 
splice 
type 
(SE, 
A3E, 
or 
RI), 
were 
found 
as 
A5E4 
splicing 
exons. 
Interestingly, 
Figure 
2.2 
also 
shows 
a 
small 
but 
persistent 
pattern 
of 
higher 
frequencies 
at 
E 
=6, 
9, 
12, 
15 
and 
18 
nucleotides, 
which 
is 
indicative 
that 
non-overlapping 
splice 
sites 
had 
biased 
extensions 
that 
preserve 
the 
reading-frame. 
The 
central 
outcome 
of 
this 
study 
points 
to 
a 
splicing 
dichotomy 
between 
human 
alternative 
50ss 
and 
30ss 
exons 
in 
that 
exon 
extensions 
were 
markedly 
biased 
toward 
overlapping 
splice 
sites, 
with 
A5Es 
biased 
for 
E 
= 
4 
nucleotides 
(tandem 
donors, 
A5E4), 
in 
contrast 
to 
A3Es 
biased 
for 
E 
= 
3 
nucleotides 
(tandem 
acceptors, 
A3E3). 
Both, 
A3E 
and 
A5E 
biases 
in 
exon 
length 
variation 
have 
been 
previously 
reported 
(2, 
114, 
135), 
but 
their 
pertinent 
features 
have 
largely 
remained 
hidden. 
It 
is 
important 
to 
note 
that 
AS 
at 
both 
the 
50ss 
and 
30ss 
gives 
rise 
to 
splicing 
variations 
with 
very 
subtle 
changes 
to 
the 
encoded 
protein 
sequence, 
but 
further 
downstream 
A5E4 
and 
A3E3 
splicing 
exons 
lead 
to 
very 
dierent 
consequences. 
While 
A3E3 
splicing 
exons 
of 
the 
form 
of 
NAG/NAG/ 
have 
been 
analyzed 
in 
some 
detail, 
in 
part 
with 
several 
controversial 
interpretations 
(2, 
114), 
A5E4 
splicing 
exons 
had 
not 
previously 
been 
conrmed 
experimentally 
and 
only 
initially 
been 
characterized 
(135). 
In 
this 
context, 
two 
related 
questions 
are 
whether 
i) 
such 
frequently 
observed 
subtle 
changes 
simply 
arise 
by 
noise“ 
of 
the 
spliceosome 
action, 
and 
ii) 
the 
splicing 


” 
cell 
has 
found 
a 
way 
to 
benet 
or 
neutralize 
the 
downstream 
consequences 
that 
arise 
from 
such 
AS 
events 
. 
Provided 
their 
biological 
authenticity, 
what 
is 
the 
nature 
of 
overlapping 
splice 
site 
choice? 
Several 
models 
for 
splice 
site 
choice 
have 
been 
proposed, 
including 
i) 
competition 
between 
antagonistic 
splicing 
factors 
(for 
example 
ASF/SF2 
and 
hnRNP 
A1) 
and 
U1-snRNP 
(86, 
136, 
87); 
ii) 
a 
scanning 
mechanism 
(90); 
or 
iii) 
cis-acting 
motifs 
with 
dierent 
free-energy 
for 
binding 
U1-snRNP 
and 
splice 
factors 
between 
competing 
splice 
sites 
(89). 
These 
models 
take 
into 
account 
the 
binding 
property 
of 
the 
U1-snRNP 
and 
additional 
factors. 
Consequently, 
known 
features 
involved 
in 
splice 
site 
choice 
were 
investigated, 
as 
well 
as 
consequences 
to 
the 
post-transcriptional 
regulation 
of 
A5E4-carrying 
genes. 
A5E4 
splicing 
exons 
were 
also 
compared 
with 
A3E3 
and 
constitutive 
splicing 
exons 
in 
the 
light 
of 
existing 
models 
for 
50ss 
selection. 
Examined 
features 
showed 
pertinent 
dierences 
that 
individually 
came 
out 
subtle, 
yet 
taken 
in 
concert 
they 
were 
indicative 
of 
a 
spliceosomal 
distinction 
of 
overlapping 
50ss. 
The 
data 
supports 
that 
overlapping 
donors, 
but 
not 
acceptors, 
can 
be 



Discussion 


distinguished 
into 
major-form 
(D4, 
P4) 
and 
minor-form 
(d4, 
p4) 
splicing 
exons 
for 
both 
distal 
(D4, 
p4 
= 
type 
I) 
and 
proximal 
(d4, 
P4 
= 
type 
II) 
splice 
sites, 
respectively. 
This 
is 
further 
corroborated 
by 
their 
splice 
site 
scores, 
which 
within 
the 
tandem 
splice 
sites 
correlated 
with 
their 
respective 
utilization 
as 
major 
or 
minor 
splice 
variant. 
On 
the 
one 
hand, 
splice 
sites 
deviated 
most 
from 
the 
consensus 
for 
P4 
splicing 
exons 
at 
positions 
P..4,P..3, 
and 
P3 
(information 
score 
dierence 
I> 
0) 
as 
well 
as 
P4,P5 
(I< 
0), 
thus, 
overlapping 
positions 
of 
the 
U1-snRNA, 
which 
are 
involved 
in 
50ss 
selection 
(89, 
123). 
Additionally, 
some 
of 
these 
positions 
have 
also 
been 
related 
to 
codon 
preference 
(135). 
Interestingly, 
P..12 
deviated 
too 
from 
the 
consensus. 
Because 
of 
its 
close 
proximity 
to 
the 
edge 
of 
the 
U1 
snRNA 
stem-loop 
it 
could 
possibly 
contribute 
to 
U1 
binding 
when 
donor 
d4 
is 
spliced. 
On 
the 
other 
hand, 
D4 
splicing 
exons 
deviated 
dierently 
from 
the 
consensus 
at 
the 
positions 
P..2,P..1,P2 
(I< 
0) 
as 
well 
as 
P5,P6 
(I< 
0). 
Based 
on 
previous 
experiments 
on 
position-specic 
stabilizing 
and 
advancing 
spliceosomal 
interactions 
with 
the 
50ss, 
these 
dierences 
between 
types 
I 
and 
II 
are 
indicative 
that 
P4 
improve 
compatibility 
with 
U1-snRNA 
above 
D4 
splicing 
exons. 
Previous 
computational 
studies 
showed 
that 
the 
conservation 
of 
sequences 

anking 
ACEs 
is 
higher 
compared 
to 
sequences 
around 
species-specic 
or 
constitutively 
spliced 
exons 
(120, 
137). 
Here, 
higher 
levels 
of 
conservation 
around 
P4, 
but 
similar 
levels 
for 
D4 
splicing 
exons 
were 
observed, 
when 
compared 
with 
constitutive 
exons 
or 
the 
50ss 
of 
A3E3 
splicing 
exons. 
Interestingly, 
the 
higher 
level 
is 
in 
accord 
with 
a 
larger 
number 
of 
detected 
silencing 
splicing-regulatory 
elements, 
often 
positioned 
in 
proximity 
to 
A5E 
tandem 
donors. 
In 
contrast 
to 
typical 
AS 
events, 
however, 
tandem 
donors 
are 
hindered 
to 
place 
regulatory 
elements 
between 
alternative 
donors. 
The 
data 
show 
an 
elevation 
of 
ESE 
elements 
near 
d4, 
in 
conjunction 
with 
an 
enrichment 
of 
ESS 
elements 
of 

anking 
introns. 
This 
could 
be 
interpreted 
in 
a 
model, 
in 
which 
tandem 
donors 
restrictively 
exploit 
elements 
in 
proximal 
location, 
i.e., 
near 
d4, 
to 
attract 
the 
U1-snRNP 
to 
this 
site 
of 
the 
tandem 
donor, 
or 
in 
distal 
location 
to 
d4, 
to 
impair 
U1-snRNP 
binding 
to 
P4 
(87). 
For 
the 
majority 
of 
tandem 
donors 
was 
embedded 
in 
coding 
regions, 
the 
downstream 
eects 
of 
4 
splicing 
was 
predictive 
of 
producing 
PTCs. 
Splicing 
at 
p4 
produced 
putative 
NMD 
substrates 
in 
more 
than 
two-thirds 
of 
all 
cases, 
whereas 
d4 
splicing 
exons 
showed 
about 
one-quarter, 
suggesting 
that 
p4 
and 
d4 
(the 
minor-forms) 
were 
more 
likely 
to 
serve 
as 
the 
corresponding 
NMD 
candidates. 
In



Discussion 


terestingly, 
a 
small 
set 
of 
A5E4-carrying 
genes 
avoided 
PTCs, 
yet 
instead 
was 
inferred 
to 
use 
DTCs 
(delayed 
termination 
codons) 
positioned 
downstream 
of 
the 
original 
signal. 
Utilization 
of 
the 
E15 
proximal 
tandem 
donor 
of 
the 
human 
SFRS16 
gene, 
e.g., 
with 
signicantly 
high 
levels 
of 
E15 

anking 
sequence 
conservation 
well 
over 
120 
nucleotides 
in 
I16 
(typical 
of 
RNA 
splicing 
conservation 
across 
species 
(120)), 
produced 
a 
PTC 
that 
apparently 
avoided 
NMD 
(138). 
Using 
dierentially 
binding 
antibodies, 
a 
previous 
study 
(108) 
showed 
that 
SFRS16 
produced 
two 
detectable 
isoforms, 
which 
correspond 
to 
E15 
tandem 
splicing. 
A 
survey 
of 
gene 
ontology 
(GO) 
functions 
of 
the 
categories 
molecular 
function“ 


” 
and 
biological 
process“ 
for 
genes 
with 
P4 
and 
D4 
splicing 
exons 
showed 
a 
sig


"
nicant 
enrichment 
in 
several 
proteins, 
while 
after 
corrections 
for 
multiple 
testing 


only 
the 
single 
GO-term 
RNA 
binding\(P< 
0:005, 
nonparametric 
t-test) 
was 


"
signicantly 
enriched, 
when 
compared 
between 
P4/d	4 
and 
D4/p	4 
splicing 
exons, 
respectively 
(see 
Methods 
2.2.9). 
This 
study 
substantially 
arms 
the 
utilization 
of 
tandem 
donors. 
While 
in 
principle 
complementing 
earlier 
ndings 
of 
previously 
undetected 
tandem 
donor 
AS 
events 
(121, 
135), 
dierent 
approaches 
to 
the 
characterization 
of 
A5E4 
events 
impede 
to 
some 
extend 
the 
comparison 
of 
results. 
For 
example, 
Dou 
et 
al. 
2006 
nd 
type-I 
tandem 
donor 
splice 
events 
more 
frequently 
than 
type-II, 
which 
stands 
in 
contrast 
to 
results 
of 
this 
study. 
However, 
they 
do 
not 
provide 
the 
exact 
rules 
based 
on 
which 
the 
dominant“ 
(major) 
spliced 
site 
in 
a 
tandem 
is 
dened. 
This 


” 
makes 
their 
class 
boundaries 
not 
reproducable 
and 
hence 
dierent 
class 
occupation 
between 
the 
studies 
could 
be 
a 
consequence 
of 
dierent 
methods 
in 
the 
type-I 
and 
II 
tandem 
donor 
classication. 
Furthermore, 
examples 
of 
cryptic 
4-type 
50ss 
have 
been 
reported 
in 
the 
literature 
(111, 
139). 
In 
contrast, 
here 
it 
is 
demonstrated 
that 
such 
splice 
variations 
are 
potentially 
enriched 
in 
authentic 
AS 
events, 
and 
also 
supported 
by 
experimental 
studies 
(108, 
140). 
Critically, 
pertinent 
data 
are 
not 
yet 
at 
hand 
to 
make 
conclusive 
inference 
about 
the 
specic 
regulation 
of 
A5E4 
splicing 
exons 
(e.g. 
controlled 
expression 
of 
species-specic 
minor/major 
isoforms), 
here 
transcript 
data 
acquisition 
and 
careful 
spliced-alignments 
have 
added 
to 
a 
higher 
condence 
of 
tandem 
donor 
(and 
acceptor) 
utilization, 
and 
deeper 
insight 
will 
require 
dierent 
types 
of 
data, 
e.g., 
from 
mini-genes 
in 
dierent 
organ 
systems 
and 
cell 
types, 
U1 
snRNP 
mutants, 
or 
variations 
of 
splicing 
factor 
dosages. 
In 
one 
extreme 
view, 
incorporating 
a 
mechanistic 
and 
dosage-dependent 
model 
(89, 
87), 
the 
selection 
of 
AS 
sites 
depends 
on 
the 
properties 
of 
U1 
and/or 
U6 



Discussion 


snRNPs 
binding 
interrelated 
with 
antagonistic 
eects 
mediated 
by 
splicing 
enhancing 
and 
suppressing 
factors. 
Although, 
type-II 
tandem 
donors 
show 
a 
reduced 
dierence 
between 
distal 
and 
proximal 
splice 
sites, 
an 
initially 
equal 
chance 
of 
U1 
snRNP 
binding 
to 
either 
the 
proximal 
or 
distal 
donor, 
could 
later 
be 
compensated 
by 
the 
higher 
complementarity 
of 
the 
minor 
distal 
donor 
to 
U6 
snRNA 
positions, 
complemented 
by 
silencing 
elements 
upstream 
of 
the 
major 
proximal 
donor. 
Similar 
it 
was 
shown 
that 
the 
choice 
of 
a 
tandem 
splice 
site 
of 
E10 
of 
the 
FGFR 
gene 
can 
be 
determined 
by 
a 
higher 
sequence-compatibility 
of 
the 
E10 
proximal 
splice 
site 
(p6) 
to 
U6 
snRNA 
(126). 
In 
addition, 
constraints 
set 
by 
secondary 
mRNA 
structures 
(75, 
141) 
have 
been 
shown 
to 
in
uence 
splice 
site 
choice. 
In 
the 
opposite 
extreme, 
suggested 
by 
subsets 
of 
tandem 
donors 
with 
strong 
dierence 
between 
distal 
and 
proximal 
splice 
site, 
splicing 
at 
such 
tandem 
donors 
could 
regularly 
involve 
stochastic 
binding 
of 
the 
splicing 
machinery 
without 
an 
implicit 
functional 
relevance 
(114), 
which 
seems 
to 
be 
supported 
by 
type-I 
isoforms. 
Either 
view 
largely 
requires 
the 
NMD 
pathway 
to 
control 
deliberatively 
or 
aberrantly 
produced 
truncated 
messages. 
Earlier 
works 
investigating 
spliceosomal 
components 
in 
50ss 
recognition, 
suggested 
co-recognition 
of 
5. 
ss 
by 
U1 
snRNP 
and 
tri-snRNP 
as 
a 
mechanism 
to 
explain 
activation 
of 
cryptic 
5. 
ss 
and 
U1 
independent 
splicing. 
Cryptic 
sites 
were 
proposed 
to 
substitute 
for 
mutationally 
silenced 
authentic 
splice 
sites, 
still 
binding 
low 
levels 
of 
U1 
binding. 
Those 
bound 
U1 
snRNPs 
in 
turn 
could 
recruit 
tri-snRNPs, 
which 
themselves 
can 
not 
bind 
authentic 
splice 
sites 
but 
activate 
nearby 
sequences 
via 
Prp8 
or 
other 
U5 
snRNP 
components 
(142). 
It 
is 
interesting 
to 
note, 
that 
U1-dependent 
and 
U1-independent 
splicing 
share 
the 
same 
5. 
ss 
sequence 
requirements, 
leaving 
room 
for 
the 
possibility 
that 
especially 
4 
AS 
events 
of 
similar 
splice 
site 
plasticity 
could 
be 
dierentially 
regulated 
by 
dierently 
composed 
spliceosomal 
complexes 
(143, 
144). 
An 
interesting 
question 
regarding 
the 
nding 
of 
deliberately 
or 
aberrantly 
produced 
AS 
variants 
with 
out-of-frame 
shifts 
and 
PTCs 
(either 
due 
to 
A5E4 
or 
other 
types 
of 
AS), 
and 
their 
functional 
utilization 
on 
the 
transcriptional 
or 
translational 
level, 
is 
whether 
there 
is 
a 
possible 
benet 
of 
generating 

awed 
mRNA 
isoforms. 
If 
such 
splice 
variants 
would 
be 
generally 
produced 
across 
organ 
systems 
and 
cell 
types, 
in 
addition 
to 
their 
normal 
splice 
variants, 
cells 
would 
have 
means 
of 
producing 
low 
levels 
of 
imperfect 
proteins. 
Depending 
on 
the 
eciency 
of 
mRNA 
quality 
control, 
a 
fraction 
of 
which 
is 
subjected 
to 
the 
NMD 
pathway 
during 
the 
rst 
pioneer 
round 
of 
translation 
and 
degraded, 
while 
a 
remaining 



Discussion 


fraction 
could 
still 
misfold 
and 
-depending 
on 
the 
quality 
control 
of 
protein 
synthesis 
-form 
defective 
ribosomal 
products 
(DRiPs). 
Ubiquitin-tagged 
peptide 
fragments 
that 
originate 
from 
DRiPs 
have 
recently 
been 
identied 
as 
a 
potent 
source 
of 
antigens 
for 
display 
by 
the 
MHC 
class 
I 
molecules 
on 
the 
cell 
surface 
to 
cognate 
CD8+ 
T-cells, 
in 
agreement 
with 
a 
recently 
suggested 
mechanism 
of 
im


” 
mune 
surveillance“ 
(145, 
146, 
147). 
A 
motivating 
example 
is 
given 
by 
the 
human 
Tyrosinase-related 
protein 
1 
(TYRP1), 
which 
utilizes 
two 
dierent 
reading-frames 
to 
produce 
the 
protein 
gp75 
(recognized 
by 
IgG) 
and 
a 
truncated 
24 
amino-acids 
long 
peptide. 
The 
latter 
was 
shown 
to 
be 
the 
source 
of 
an 
antigenic 
peptide 
specically 
recognized 
by 
T-cells 
as 
a 
tumor 
rejection 
antigen 
(148). 
It 
remains 
to 
be 
substantiated 
whether 
such 
antigenic 
peptides 
are 
linked 
to 
AS 
events 
that 
produce 
variants 
with 
out-of-frame 
shifts, 
such 
as 
produced 
by 
tandem 
donors. 



Discussion 



Chapter 
3 


Petrinet 
Analysis 
of 
Spliceosome 
Assembly 


3.1 
Introduction 
3.1.1 
The 
Spliceosome 
Alternative 
splicing 
depends 
on 
the 
fundamental 
processes 
of 
the 
spliceosomal 
protein 
complex. 
The 
spliceosome 
is 
a 
nuclear 
megadalton 
complex 
which 
is 
organized 
in 
ve 
major 
subcomplexes 
U1, 
U2, 
U4, 
U5 
and 
U6 
that 
are 
formed 
by 
core 
proteins 
and 
additional 
splicing 
factors. 
The 
presently 
known 
number 
of 
splicesosomal 
factors 
is 
estimated 
to 
150-300 
proteins 
(149, 
150, 
151, 
152, 
153), 
thus 
providing 
the 
cell 
with 
a 
broad 
repertoire 
of 
splicing 
regulatory 
proteins. 
The 
spliceosomal 
subunits 
are 
ribonucleoprotein 
complexes 
of 
approximately 
1-2 
m3 
which 
form 
a 
structured 
environment 
around 
target 
splice 
sites. 
Only 
recently 
it 
was 
conrmed 
that 
the 
snRNAs 
of 
the 
U2 
and 
U6 
subunits 
are 
sucient 
for 
RNA 
cleavage 
and 
re-ligation, 
rendering 
the 
spliceosome 
eectively 
a 
ribozyme 
(154). 
Though 
this 
suggests 
that 
the 
splicing 
reaction 
is 
a 
relic 
from 
the 
RNA 
world, 
a 
complex 
protein 
machine 
is 
wrapped 
around 
this 
mechanism 
with 
many 
proteins 
being 
homologous 
between 
such 
dierent 
organisms 
as 
human 
and 
yeast. 
The 
outcome 
of 
dierent 
splicing 
events 
shows 
that 
the 
maturation 
of 
RNA 
is 
a 
complicated 
process 
that 
can 
be 
in
uenced 
by 
dierent 
events 
such 
as 
mutations 
in 
splicing 
signals 
or 
signal 
cascades, 
induced 
by 
stimuli 
outside 
of 
the 
cell 
(155, 
11). 
Splicing 
decisions 
are 
controlled 
by 
two 
major 
determinants 
-i) 
the 
pre-mRNA 
sequence 
and 
its 
inherent 
signals 
and 
ii) 
the 
protein-complement 
of 
the 
spliceosome 
where 
signal 
transduction 
is 
frequently 
maintained 
via 
arginine-serine 
rich 


67 



The 
Basic 
Steps 
of 
Spliceosome 
Assembly 


domains 
of 
the 
participating 
proteins 
(RS 
domains) 
(20). 
Hence, 
the 
dependence 
of 
gene 
expression 
on 
developmental 
stage 
and/or 
tissue 
type 
is 
modulated 
to 
a 
major 
extent 
by 
a 
network 
of 
protein-protein 
and 
protein-RNA 
interactions 
that 
in
uence 
the 
assembly 
and 
localization 
of 
active 
spliceosomes. 
Noteworthy, 
the 
spliceosome 
is 
thought 
to 
serve 
not 
only 
as 
the 
catalyst 
of 
the 
splicing 
reaction 
steps, 
but 
also 
to 
translate 
the 
limited 
information 
presented 
by 
pre-mRNA 
into 
splicing 
specicity 
and 
orderliness 
(156). 


3.1.2 
The 
Basic 
Steps 
of 
Spliceosome 
Assembly 
Several 
hypotheses 
about 
the 
dynamics 
of 
spliceosome 
assembly 
exist: 
one 
model 
suggests 
the 
co-transcriptional 
assembly 
of 
spliceosomes 
at 
the 
nascent 
pre-mRNA, 
consistent 
with 
the 
fact 
that 
in 
eukaryotes 
splicing 
factors 
have 
been 
found 
in 
association 
with 
the 
c-terminal 
end 
of 
the 
RNA 
polymerase 
II 
(157, 
158, 
159). 
In 
opposite, 
alternative 
splicing 
due 
to 
exon 
skipping 
can 
require 
the 
presence 
of 
an 
at 
least 
partially 
transcribed 
pre-mRNA, 
as 
the 
remarkable 
example 
of 
the 
heteronuclear 
ribonucleoprotein 
(hnRNP) 
A1 
shows 
(79). 
There, 
hnRNP 
A1 
binds 
at 
intronic 
sites 
adjacent 
to 
an 
exon 
of 
its 
own 
pre-mRNA, 
and 
by 
interacting 
hnRNP 
molecules 
across 
the 
exon, 
the 
exon 
is 
looped 
out 
from 
the 
splicing 
plane, 
thus 
implying 
the 
occurrence 
of 
a 
post-transcriptional 
splicing 
mechanism. 
Additionally, 
the 
commonly 
accepted 
model 
of 
exon 
denition 
in 
higher 
eukaryotes 
(85), 
favors 
a 
post-transcriptional 
splicing 
mechanism. 
This 
is 
reasonable 
since 
the 
5. 
splice 
site 
(50ss), 
branchpoint 
(BP) 
and 
3. 
splice 
site 
(30ss) 
have 
to 
be 
present 
and 
recognized 
by 
an 
initial 
protein 
complement 
for 
subsequent 
bridging 
across 
the 
exon. 
Consistently 
in 
well 
studied 
yeast 
splicing 
systems 
it 
has 
been 
found 
that 
due 
to 
the 
transcription 
kinetics, 
spliceosomes 
preferentially 
assemble 
post-transcriptionally 
on 
short 
second-exon 
genes, 
aecting 
approximately 
more 
than 
90% 
of 
yeast 
splicing 
(160). 
However, 
the 
same 
study 
and 
others 
(161) 
observed 
that 
U1 
snRNP 
recruitment 
and 
splicing 
of 
long 
second 
exon 
genes 
(>1 
kilobase) 
occurs 
mainly 
co-transcriptionally. 


Opposing 
the 
stepwise 
assembly 
model, 
a 
holospliceosome“ 
model 
has 
been 
pro


"

posed, 
where 
a 
tetra-or 
penta-snRNP 
complex 
binds 
to 
the 
introns 
and 
modulates 
RNP-RNP 
or 
RNP-pre-mRNA 
contacts 
within 
the 
complex 
(162, 
163, 
164). 
Spliceosome 
assembly 
can 
be 
described 
as 
intracellular 
allosteric 
cascade, 
where 
every 
interaction 
depends 
on 
a 
previous 
interaction. 
Besides 
the 
snRNP 
sub



Introduction 


complexes 
and 
their 
core 
components, 
additional 
proteins 
(splicing 
factors) 
and 
enzymes 
(DExD/H-box 
proteins) 
are 
required 
to 
shape 
the 
active 
spliceosome. 
While 
the 
rst 
step 
-the 
E-complex 
assembly 
-requires 
no 
energy 
for 
structural 
rearrangements, 
subsequent 
steps 
of 
snRNP 
addition 
and 
interaction 
involve 
helicase 
like 
ATPases 
that 
catalyze 
structural 
rearrangements 
in 
snRNA-pre-mRNA 
contacts 
(163). 
The 
major 
events 
in 
the 
stepwise 
assembly 
prior 
to 
the 
activated 
spliceosome 
include 
U1 
snRNP 
binding 
to 
the 
5. 
ss, 
U2 
snRNP 
binding 
to 
the 
branchpoint, 
addition 
of 
a 
preformed 
U4/U6U5 
triple 
snRNP, 
interaction 
of 
U2and 
U6 
snRNA 
and 
concomitant 
destabilization 
U1 
and 
U4 
snRNP 
(165). 
At 
this 
point, 
the 
spliceosome 
is 
reshaped 
such 
that 
the 
rst 
transesterication 
reaction 
can 
take 
place. 
The 
snRNP 
and 
splicing 
factors 
arrange 
the 
20-OH 
of 
the 
branchpoint 
adenosine 
in 
such 
a 
close 
proximity 
to 
the 
50ss 
that 
it 
can 
perform 
a 
nucleophilic 
attack 
to 
the 
50ss 
phosphodiester 
bond, 
disrupting 
the 
pre-mRNA 
and 
forming 
an 
intermediate 
loop 
(lariate 
) 
within 
the 
intron. 
Further, 
internal 
rearrangements 
of 
snRNA 
contacts 
and 
interaction 
of 
snRNP 
components 
prepare 
the 
second 
step 
of 
catalysis 
whereupon 
the 
30-OH 
end 
of 
the 
upstream 
exon 
attacks 
the 
3. 
ss 
phosphodiester 
bond, 
nally 
tethering 
both 
exons 
together 
and 
releasing 
the 
lariat 
intron 
(149). 
These 
basic 
steps 
are 
conserved 
from 
yeast 
to 
mammals 
and 
also 
similar 
for 
the 
minor 
U12 
type 
spliceosome 
assembly 
(166; 
167). 


Description 
of 
the 
Four 
Major 
Stages 
of 
Spliceosomal 
Assembly 


Splicecosome 
assembly 
is 
a 
hierarchical 
process 
that 
progresses 
through 
several 
main 
stages 
designated 
(P!)E 
. 
A 
. 
B 
. 
C(. 
P). 
Each 
stage 
(E-C) 
describes 
an 
intermediate 
complex 
that 
is 
build 
from 
the 
pool 
(P) 
of 
proteins 
available 
in 
the 
nucleus 
and 
which 
are 
recycled 
for 
repeated 
rounds 
of 
spliceosome 
assembly. 
The 
biochemical 
processes, 
which 
result 
in 
the 
mature 
spliceosome 
that 
is 
able 
to 
catalyze 
the 
splicing 
reaction 
in 
eukaryotic 
messenger 
RNAs 
can 
be 
summarized 
as 
following: 


I. 
E-complex 
(commitment 
complex): 
The 
recognition 
of 
the 
5. 
splice 
site 
is 
initiated 
by 
early 
interaction 
of 
the 
U1 
specic 
protein 
U1C 
with 
the 
50ss 
sequence 
(168). 
Additionally, 
interactions 
via 
phosphorylated 
RS 
domains 
of 
U1-70K 
with 
splicing 
enhancing 
factors, 
which 
bind 
to 
nearby 
located 
specic 
enhancer 
motifs, 
can 
direct 
the 
U1 
snRNP 
to 
a 
specic 
50ss 
(169). 
It 
was 
shown 
that 
50ss, 
which 
are 
less 
complementary 
to 
the 
canonical 
eukaryotic 
50ss, 
are 
still 
selected 

The 
Basic 
Steps 
of 
Spliceosome 
Assembly 


by 
U1 
snRNP 
due 
to 
interactions 
between 
U1 
snRNP 
proteins 
and 
splice 
enhancing 
proteins, 
such 
as 
ASF/SF2 
or 
TIA1 
(169, 
170). 
This 
is 
in 
agreement 
with 
the 
earlier 
observation 
that 
donor 
site 
selection 
by 
U1 
snRNP 
can 
be 
rescued 
by 
the 
SR 
protein, 
SC35, 
when 
the 
donor 
recognition 
site 
within 
U1 
snRNA 
is 
impaired 
(171). 
In 
contrast, 
a 
weak 
5. 
ss 
might 
also 
be 
selected 
by 
variants 
of 
U1 
snRNA 
(172). 
This 
led 
to 
the 
interesting 
hypothesis 
that 
50ss 
selection 
within 
the 
E-complex 
might 
be 
possible 
even 
without 
the 
binding 
of 
U1 
snRNP. 
Experiments 
indicate 
that 
the 
presence 
of 
higher 
concentrations 
of 
SR 
proteins 
enable 
such 
a 
spliceosomal 
pathway 
(173). 
At 
the 
3. 
end 
of 
the 
intron, 
E-complex 
assembly 
involves 
the 
U2 
auxilary 
factor, 
U2AF, 
which 
is 
a 
heterodimer 
of 
a 
65 
and 
35 
kDa 
subunit. 
Together 
with 
SF1 
(in 
mammals 
designated 
branch 
point 
binding 
protein, 
mBBP), 
these 
factors 
recognize 
via 
their 
RNA 
recognition 
motifs 
(RRM) 
the 
polypyrimidine 
tract, 
30ss 
and 
branchpoint, 
respectively. 
This 
step 
was 
shown 
to 
occur 
in 
a 
coordinated 
way 
involving 
cooperativity 
between 
SF1 
and 
U2AF65 
(174), 
which 
can 
be 
mediated 
by 
interactions 
between 
the 
RS 
domains 
of 
these 
proteins 
(20). 
Additional 
interactions 
reported 
during 
E-complex 
assembly 
include 
the 
bridging 
of 
U1 
snRNP 
at 
the 
50ss 
and 
factors 
bound 
to 
the 
branchpoint-30ss, 
which 
is 
mediated 
by 
SR 
protein 
FBP11 
(175). 
U2 
snRNP 
is 
present 
in 
close 
proximity 
to 
the 
U1 
snRNP 
and 
formation 
of 
the 
E-complex 
depends 
on 
U2 
snRNP 
(176, 
177). 


II. 
A-complex 
(pre-spliceosome): 
The 
A-complex 
contains 
the 
stably 
integrated 
U2 
snRNP, 
which 
is 
assembled 
out 
of 
two 
heteromeric 
subcomplexes 
SF3a 
and 
SF3b 
via 
an 
intermediate 
17S 
complex. 
Prior 
to 
their 
contacts 
to 
the 
U2 
snRNA, 
SF3a 
and 
SF3b 
are 
formed 
by 
several 
interactions 
between 
U2 
core 
components 
(e.g., 
SF3b155 
with 
SF3b14 
or 
SF3a120 
with 
SF3a60) 
(178). 
In 
this 
stage, 
U2 
snRNP 
interacts 
with 
the 
branchpoint 
site 
via 
base 
pairing 
between 
U2 
snRNA 
and 
the 
pre-mRNA 
under 
consumption 
of 
ATP 
(177). 
Additionally, 
the 
transition 
from 
E 
to 
A-complex 
is 
supported 
by 
the 
SF3b 
protein, 
SF3b155, 
which 
binds 
at 
both 
sides 
of 
the 
branchpoint 
and 
interacts 
simultaneously 
with 
the 
U2AF65 
subunit 
and 
the 
SF3b14 
protein 
(179, 
180). 
The 
ATP 
requirement 
during 
A-complex 
formation 
has 
been 
reasoned 
by 
two 
other 
U2 
snRNP 
proteins, 
SF3b125 
and 
hPrp5, 
which 
are 
members 
of 
the 
DExD/H 
family 
and 
may 
function 
either 
as 
helicases 
or 
RNPases 
(181). 
Such 
auxiliary 
enzymes 
could 
be 
recruited 
from 
proximal 
cajal 
bodies 
or 

Introduction 


speckles 
(182). 


These 
helicase 
like 
proteins 
have 
been 
shown 
to 
function 
as 
generic 
ATPases 
that 
bind 
and 
hydrolyze 
NTP 
to 
unwind 
double-stranded 
RNAs 
(dsRNAs) 
(165). 
In 
contrast, 
the 
ADP-bound 
form 
can 
modulate 
the 
annealing 
of 
complementary 
RNA 
strands. 
In 
context 
of 
the 
spliceosome 
DEAxD/H 
box 
helicases 
are 
governing 
structural 
rearrangements 
to 
shape 
the 
active 
complex. 
However, 
many 
of 
these 
ATPases 
function 
in 
a 
generic 
way 
hydrolyzing 
also 
other 
RNA 
species. 
Thus, 
they 
have 
to 
be 
specically 
activated 
for 
catalyzing 
the 
correct 
structural 
rearrangements, 
not 
least 
to 
ensure 
delity 
of 
the 
splicing 
reaction. 
It 
was 
proposed 
that 
additional 
splicing 
factors 
interact 
with 
DExD/H 
box 
helicases 
to 
direct 
their 
activity 
to 
specic 
substrates 
during 
the 
assembly 
process. 


SF3b125 
was 
detected 
only 
in 
low 
amounts 
in 
the 
SF3b 
subcomplex, 
but 
not 
associated 
with 
the 
17S 
U2 
snRNP 
and 
hPrp5 
was 
present 
in 
the 
17S 
U2 
snRNP, 
but 
not 
in 
the 
SF3b 
subcomplex 
(182). 
The 
protein 
hPrp5 
exhibits 
an 
ATP 
independent 
function, 
which 
stabilizes 
U2 
snRNP 
to 
the 
BP 


(183) 
and 
was 
proposed 
to 
function 
as 
bridge 
between 
U1 
and 
U2 
snRNPs 
at 
the 
time 
of 
A-complex 
formation 
(181). 
Accompanying 
the 
U2 
snRNP 
rearrangements, 
which 
are 
catalyzed 
by 
hPrp5 
under 
ATP 
hydrolization, 
the 
SF3a60 
contacts 
to 
the 
U2 
snRNA 
are 
signicantly 
reduced 
upon 
association 
of 
U2 
with 
the 
BP 
in 
the 
A-complex 
(178). 
Another 
DExD/H 
box 
helicase, 
which 
is 
required 
for 
U2 
snRNP 
/ 
BP 
interaction, 
is 
UAP56, 
whose 
recruitment 
also 
depends 
on 
U2AF65 
(184). 
The 
reactions 
of 
the 
individual 
assembly 
stages 
are 
described 
and 
formalized 
in 
Table 
C.1. 
III. 
B-complex 
(active 
spliceosome): 
In 
this 
stage, 
U4, 
U5, 
and 
U6 
snRNPs 
enter 
the 
assembly 
pathway 
as 
trisnRNP 
complex. 
This 
subcomplex 
is 
formed 
in 
a 
separate 
way, 
where 
U4 
and 
U5 
snRNP 
assemble, 
similar 
as 
U1 
snRNP, 
from 
a 
family 
of 
seven 
RNA 
binding 
proteins, 
termed 
Sm 
proteins 
(185, 
186), 
whereas 
the 
U6 
snRNA 
is 
bound 
by 
a 
dierent 
set 
of 
proteins 
termed 
Sm-like 
proteins 
(187). 
Subsequently, 
U4 
and 
U6 
snRNP 
form 
a 
duplex 
via 
base 
pairing 
of 
their 
snRNAs, 
resulting 
in 
a 
structural 
conformation, 
which 
is 
stabilized 
by 
several 
additional 
proteins, 
for 
example 
hSnu13, 
Prp3, 
Prp4, 
CypH 
and 
Prp31 
(186). 
The 
U5 
snRNP 
contains 
several 
proteins, 
important 
for 
structural 
rearrangements 
prior 
to 
the 
rst 
catalytic 
step 
of 
splicing, 
most 
important 
the 

The 
Basic 
Steps 
of 
Spliceosome 
Assembly 


DExD/H 
box 
helicases 
Brr2 
and 
Prp28 
and 
the 
two 
proteins 
Snu114 
and 
Prp8 
(188, 
186). 
An 
interaction 
between 
Prp6 
and 
Prp31 
was 
proposed 
to 
serve 
as 
bridging 
step 
between 
U4/U6 
and 
U5 
snRNP, 
preparing 
the 
formation 
of 
the 
U4/U6.U5 
tri-snRNP 
complex 
(189). 
The 
additional 
proteins, 
Snu66, 
Sad1 
and 
27K, 
stabilize 
the 
intermediate 
tri-snRNP 
complex. 
The 
latter 
is 
recruited 
to 
the 
spliceosome 
which, 
however, 
is 
still 
catalytically 
inactive. 
An 
intermediate 
state, 
designated 
pre-catalytic 
B. 
complex, 
shows 
a 
more 

exible 
tri-snRNP 
structure, 
possibly 
for 
integrating 
other 
components, 
for 
example 
Prp19 
(190). 


Prior 
to 
the 
conformational 
rearrangement 
required 
for 
spliceosome 
activation, 
U1 
snRNP 
is 
dissociating 
from 
the 
50ss 
enabling 
U6 
snRNP 
to 
contact 
donor 
splice 
site. 
This 
step 
involves 
the 
ATPase, 
Prp28, 
which 
was 
found 
to 
counteract 
the 
stabilizing 
eect 
of 
the 
U1 
component 
U1C 
with 
the 
U1 
snRNA 
(191). 
Prp5 
can 
leave 
the 
spliceosome 
at 
this 
stage 
as 
it 
was 
demonstrated 
to 
function 
mainly 
before 
or 
during 
A-complex 
assembly 
(182). 
The 
U5 
snRNP 
components 
Brr2, 
Prp8 
and 
Snu114 
are 
critical 
for 
unwinding 
the 
U4/U6 
snRNA 
stemloop. 
This 
step 
resembles 
a 
G-protein 
activating 
mechanism, 
where 
Snu114 
enters 
a 
GTP 
dependent 
state, 
and 
subsequently 
activates 
the 
helicase 
Brr2 
(192, 
193). 
This 
conformational 
change 
also 
involves 
Prp19, 
a 
part 
of 
the 
Ninteenth 
complex 
(NTC) 
(194). 
Brr2 
is 
further 
involved 
in 
interactions 
with 
Prp16 
and 
U1-70K 
(195, 
186). 
After 
unwinding 
the 
U4/U6 
duplex, 
U2 
and 
U6 
snRNP 
establish 
several 
interactions 
via 
their 
snRNAs 
and 
the 
pre-mRNA, 
whereas 
a 
binding 
motif 
within 
the 
U6 
snRNA 
directly 
contacts 
the 
50ss. 
Subsequent 
release 
of 
U1 
and 
U4 
snRNP 
results 
in 
the 
catalytically 
competent 
B* 
complex 
which 
forms 
the 
catalytic 
core 
of 
the 
spliceosome 
(165, 
186). 
The 
B-complex, 
composed 
of 
U4/U6.U5 
tris-nRNP 
in 
close 
contact 
with 
the 
U2 
snRNP, 
performs 
the 
rst 
catalytic 
step 
of 
splicing 
by 
nucleophilic 
attack 
of 
the 
branchpoint 
adenosine 
to 
the 
phosphate 
ester 
bond 
of 
the 
5. 
ss, 
and 
is 
subsequently 
converted 
into 
Ccomplex. 


IV. 
C-complex: 
The 
C-complex 
contains 
U2, 
U5 
and 
U6 
snRNA 
at 
a 
stage 
subsequent 
to 
the 
rst 
chemical 
step, 
because 
splicing 
intermediates 
can 
be 
found 
in 
this 
complex. 
The 
conformation 
is 
centered 
around 
Prp8 
which 
is 
thought 
to 
serve 
as 
a 
surgery 
table\, 
connecting 
the 
already 
free 
5. 
and 
xating 
the 
3. 
"


Introduction 


ss 
such 
that 
the 
second 
transesterication 
can 
open 
the 
intron-downstream 
exon 
connection 
(192). 
With 
the 
ligation 
of 
the 
free 
exon 
end, 
the 
intron 
and 
bound 
snRNPs 
are 
released 
as 
a 
post-spliceosomal 
lariat 
complex. 
Subsequently, 
bonds 
between 
U2, 
U6 
and 
U5 
snRNA 
are 
broken, 
involving 
the 
helicase, 
Prp43, 
and 
U5 
dissociates 
into 
smaller 
subcomplexes 
to 
nally 
join 
another 
round 
of 
spliceosome 
assembly 
(151). 
Since 
Prp43 
can 
be 
found 
in 
the 
17S 
U2 
complex 
(182), 
which 
forms 
during 
early 
A-complex 
assembly, 
it 
is 
conceivable 
that 
this 
protein 
remains 
present 
over 
several 
stages 
of 
the 
spliceosome 
assembly 
pathway. 


Additional 
factors 
support 
the 
recycling 
process, 
for 
example 
Prp24, 
which 
reanneals 
U4 
and 
U6 
snRNAs 
and 
allows 
regeneration 
of 
the 
U4/U6 
snRNP 
duplex 
(196). 
Two 
important 
helicases, 
Prp16 
and 
Prp22, 
impose 
kinetic 
proofreading 
activity 
and 
can 
subject 
suboptimal 
splicing 
substrates 
into 
a 
discard 
pathway 
(197, 
198). 
It 
is 
important 
to 
note, 
that 
the 
catalyzing 
function 
of 
the 
spliceosome 
can 
experimentally 
be 
reduced 
to 
its 
RNA 
parts, 
making 
it 
eectively 
a 
ribozyme 
(154). 
However, 
the 
protein 
scaffold 
is 
necessary 
to 
form 
the 
structural 
environment 
(RNA 
conformations) 
necessary 
to 
enable 
the 
splicing 
reaction. 
Moreover 
the 
participating 
proteins 
establish 
important 
links 
to 
other 
cellular 
processes 
as 
for 
example 
transcription 
or 
nuclear 
export. 


3.1.3 
Modeling 
the 
Spliceosome 
One 
of 
the 
major 
diculties 
in 
the 
functional 
characterization 
of 
the 
spliceosome 
arises 
from 
the 
dynamical 
interactions 
between 
its 
subcomplexes 
and 
the 
proteins 
organized 
within 
those. 
Although 
extensive 
knowledge 
has 
been 
gathered 
about 
the 
factors 
involved 
in 
spliceosomal 
function, 
their 
functional 
interplay 
and 
regulatory 
impact 
is 
not 
comprehensively 
understood 
and 
even 
discussed 
controversially. 
For 
example, 
it 
is 
still 
not 
fully 
understood 
whether 
the 
assembly 
process 
occurs 
mainly 
co-or 
posttranscriptional 
and 
whether 
a 
stepwise 
assembly 
(158, 
164) 
rules 
out 
the 
possibility 
of 
a 
pre-assembled 
holospliceosome 
(162, 
199). 
in 
this 
context 
the 
term 
transcription 
factory 
model“ 
has 
been 
coined 
denoting 


"

the 
concerted 
action 
of 
transcription 
and 
splicing 
machinery 
at 
the 
places 
of 
active 
gene 
transcription 
(200). 
The 
connection 
and 
coupling 
of 
dierent 
protein 
machines 
in 
the 
cell 
emphasizes 
the 
complex 
environment 
in 
which 
spliceosomal 



Modeling 
the 
Spliceosome 


assembly 
is 
embedded. 


The 
vast 
amount 
of 
experimental 
data 
makes 
it 
necessary 
to 
structure 
the 
wealth 
of 
information 
to 
derive 
new 
hypotheses 
on 
the 
underlying 
signal 
transduction 
processes. 
This 
task 
requires 
the 
development 
of 
in 
silico 
models 
which 
are 
able 
to 
integrate 
much 
of 
the 
existing 
data 
while 
being 
suited 
for 
rigorous 
validation 
and 
stepwise 
extension. 
Further 
modeling 
goals 
can 
be 
summarized 
in 
i) 
visualization, 
ii) 
comprehensible 
data 
annotation 
and 
iii) 
data 
abstraction, 
allowing 
an 
exact 
mathematical 
description 
of 
the 
model. 


In 
particular, 
alternative 
splicing 
often 
involves 
dierent 
sets 
of 
splicing 
factors 
in 
addition 
to 
the 
spliceosomal 
core 
components, 
hence 
variations 
of 
the 
model 
network 
should 
provide 
a 
sound 
base 
for 
testing 
hypotheses 
on 
the 
regulation 
of 
splicing 
and 
AS 
events. 
Several 
structural 
and 
kinetic 
factors 
were 
proposed 
to 
in

uence 
splicing 
patterns 
of 
a 
gene 
such 
as 
i) 
precise 
balance 
and 
concentrations 
of 
regulatory 
proteins 
, 
ii) 
nature 
of 
interaction 
like 
inhibiting 
or 
cooperative 
eects, 
iii) 
number 
of 
interactions 
which 
dene 
the 
connectivity 
of 
a 
network, 
iv) 
speed 
of 
transcriptional 
elongation 
or 
recruitment 
of 
splicing 
factors 
(201, 
202). 
Additionally, 
a 
temporal 
model 
component 
can 
be 
considered 
as 
the 
assembly 
pathway 
bears 
a 
number 
of 
timed 
dependencies. 


These 
keypoints 
pose 
an 
essential 
base 
for 
modeling 
spliceosomal 
processes, 
but, 
most 
of 
them 
can 
be 
not 
comprehensively 
applied 
at 
present. 
Incompleteness 
of 
experimental 
data 
as 
for 
example 
the 
lack 
of 
knowledge 
about 
interaction 
kinetics, 
concentrations 
or 
the 
exact 
temporal 
order 
of 
reactions 
is 
accompanied 
by 
heterogeneity 
of 
proposed 
mechanisms 
for 
dierent 
stages 
of 
spliceosome 
assembly. 


Protein 
interaction 
databases 
such 
as 
String 
(203), 
cross-referencing 
to 
Mint, 
Hprd, 
BioGrid, 
Dip 
and 
Reactome, 
Apid 
(204) 
or 
IntAct(205), 
already 
provide 
an 
extensive 
organization 
and 
integration 
of 
experimental 
and 
predicted 
protein 
interaction 
data, 
but, 
most 
of 
them 
represent 
static 
interactions 
without 
providing 
information 
about 
the 
temporal 
order 
and 
direction 
of 
interactions 
within 
hierarchical 
networks. 
This 
underscores 
the 
need 
to 
nd 
ways 
to 
access 
this 
additional 
information 
from 
literature, 
incorporating 
knowledge 
that 
can 
be 
used 
to 
extend 
the 
capabilities 
of 
network 
exploration 
for 
hierarchical 
processes, 
such 
as 
spliceosome 
assembly. 
This 
should 
extensively 
support 
the 



Introduction 


generation 
of 
hypotheses 
on 
the 
progression 
of 
signals 
and 
protein 
interactions. 
For 
comparison, 
Figure 
3.1 
depicts 
an 
unordered-undirected 
protein-protein 
interaction 
network 
as 
reviewed 
in 
(206), 
and 
a 
directed 
ordered 
interaction 
network 
presented 
in 
this 
work. 
Due 
to 
lack 
of 
kinetic 
properties, 
composing 


(a) 
(b) 
ABCDEFGH1.2.3.
DABACCACDB
Figure 
3.1: 
Two 
types 
of 
protein 
interaction 
networks: 
(a) 
An 
undirected 
network 
of 
binary 
interactions, 
e.g., 
modeled 
in 
(207) 
(b) 
Directed 
hierarchical 
molecular 
interaction 
network 
as 
modeled 
here 
by 
Petri 
net 
(PN) 
theory 


a 
discrete 
structural 
model 
was 
considered 
as 
a 
good 
initial 
choice 
for 
the 
computational 
analysis 
of 
the 
spliceosome. 
Here, 
Petri 
net 
(PN) 
theory 
was 
chosen, 
because 
it 
oers 
the 
advantage 
to 
combine 
visualization, 
with 
the 
possibility 
of 
mathematical 
description 
and 
graph-theoretical 
analyses. 
PN 
theory 
provides 
techniques 
and 
software 
tools 
to 
model, 
analyze, 
and 
simulate 
biochemical 
networks 
(208, 
209, 
210, 
211). 
Meanwhile, 
metabolic 
networks 
(212, 
213, 
214, 
210) 
as 
well 
as 
signal 
transduction 
pathways 
(215, 
216) 
and 
gene 
regulatory 
pathways 
(217, 
218, 
219) 
were 
successfully 
modeled 
using 
PN. 
There 
are 
parallel 
approaches 
to 
structural 
modeling 
such 
as 
those 
based 
on 
elementary 

ux 
modes 
of 
(220, 
221) 
and 
on 
extreme 
pathways 
(222). 
They 
have 
primarily 
been 
applied 
to 
metabolic 
pathways 
(for 
reviews, 
see 
(223, 
224)) 
but 
also 
to 
signaling 
networks 
(225). 
An 
interesting 
question 
is, 
whether 
the 
model 
of 
stoichiometrically 
quantiable 
mass 

ow 
inherent 
to 
metabolic 
networks, 
can 
be 
transferred 
to 
models 
of 
information 

ow 
as 
occurring 
in 
signaling 
networks. 
Recently, 
a 
model 
of 
the 
U1 
snRNP 
subcomplex 
assembly 
demonstrated 
the 
applicability 
of 
PN 
theory 
(226). 
The 
present 
work 
is 
the 
rst 
attempt 
to 
merge 
current 
knowledge 
about 
the 
entire 
spliceosomal 
assembly 
into 
a 
network 
of 
directed 
reactions. 
In 
doing 
so, 
most 
challenging 
is 
the 
proper 
translation 
of 
a 
huge 
body 
of 
experimental 
evidence 
from 
the 
literature 
into 
single 
reactions 
steps, 
which 
can 
be 
integrated 
in 
a 
PN. 
Sorting 
through 
these 
reactions 
served 
as 
a 
base 
for 
gradual 
renement, 
converging 
in 
a 
validated 
model 
of 
spliceosome 



Modeling 
the 
Spliceosome 


maturation. 


Box 
3.1.1 
Allosteric 
interactions 


Allosteric 
interactions 
describe 
interactions 
between 
spatially 
separated 
regions 
of 
a 
protein 
and 
were 
rst 
des
cribed 
for 
oxygen 
binding 
within 
hemoglobin. 
In 
enzymes, 
allostery 
describes 
the 
binding 
of 
an 
eector 
molecule 
at 
a 
site 
dierent 
from 
the 
active 
center, 
aecting 
the 
subs
trate 
anity 
of 
the 
enzyme 
by 
conformational 
changes 
(227). 
As 
a 
special 
case 
of 
allostery, 
cooperative 
intera
ctions 
describe 
the 
alleviated 
binding 
of 
a 
molecule 
A 
to 
its 
target, 
after 
a 
rst 
molecule 
B 
has 
already 
bound 
(174). 
Cooperativity 
is 
further 
distinguished 
in 
homotropic 
and 
heterotropic 
cooperativity, 
depending 
on 
whether 
eect
or 
and 
substrate 
are 
the 
same 
molecule 
or 
not, 
respect
ively. 
This 
concept 
can 
be 
generalized 
and 
transferred 
from 
enyzmes 
to 
protein 
complexes 
where 
interactions 
bet
ween 
protein(domains) 
dictate 
conformations 
which 
in
ue
nce 
the 
signaling 
activity 
of 
the 
whole 
complex 
(228). 
Kin
etically 
this 
means 
that 
the 
equilibrium 
constant 
for 
assoc
iation 
of, 
for 
example, 
two 
dimers 
BA 
(multimerizing 
to 
BAAB) 
is 
greater 
than 
that 
for 
A 
+ 
A 
. 
AA 
alone 
see 
Figure 
3.2 
(229). 


ABq(AA)<q(BAAB)
BBBAAAAAAA
Figure 
3.2: 
In 
case 
of 
cooperativity 
the 
equilibrium 
con


ka

stant 
q 
= 
for 
association 
of 
two 
proteins 
A 
is 
greater 


kd 


after 
binding 
of 
an 
allosteric 
eector 
B. 
ka 
and 
kd 
denote 
the 
rate 
constants 
of 
association 
and 
dissociation 
respect
ively. 


The 
model 
was 
designed 
as 
signal 
regulatory 
network 
which 
consists 
of 
protein 
and 
RNA 
species 
relaying 
a 
signal 
rather 
than 
a 
mass 

ow. 
As 
such, 
this 
structural 
model 
shows 
dependencies 
of 
reactions 
rather 
than 
their 
kinetics, 
which 
for 
many 
parts 
of 
the 
network 
is 
still 
unknown. 
However, 
spliceosome 
assembly 
has 
been 
shown 
to 
form 
a 
complex 
interacting 
network 
of 
subcomplex 
formation 


(151) 
best 
described 
as 
an 
allosteric 
cascade 
(163) 
that 
frequently 
involves 
cooperativity 
(174) 
(see 
Box 
3.1.1). 
However, 
spliceosome 
assembly 
has 
been 
shown 
to 
form 
a 
complex 
interacting 
network 
of 
subcomplex 
formation 
(151) 
best 
described 
as 
an 
allosteric 
cascade 
(163), 
that 
frequently 
involves 
cooperativity(174) 
(see 
Box 
3.1.1). 
Already 
a 
decade 
ago, 
it 
was 
proposed 
that 
the 
composition 
of 
spliceosomal 
snRNPs 
could 
be 
dierent 
on 
dierent 
splicing 
substrates 
in 
a 
context-dependent 
manner 
(tissue 
type, 
developmental 
stage). 
This 
has 
the 
consequence 
that 
many 
dierent 
combinations 
of 
factors 
could 
potentially 
give 



Introduction 


rise 
to 
dierent 
types 
of 
active 
spliceosomes, 
thus, 
increasing 
the 
potential 
for 
splicing 
regulation 
(230). 


In 
light 
of 
the 
wealth 
of 
proteins 
that 
up 
to 
now 
have 
been 
identied 
around 
the 
spliceosome 
, 
it 
is 
necessary 
to 
go 
beyond 
cataloging 
the 
factors. 
Putting 
protein 
and 
mRNA 
factors 
together 
into 
the 
context 
of 
a 
larger 
network 
poses 
a 
dicult 
task 
and 
requires 
the 
integration 
of 
a 
standardized 
vocabulary 
to 
unambiguously 
describe 
all 
possible 
reactions. 
While 
this 
task 
will 
remain 
a 
challenge 
for 
ontology 
and 
text 
mining 
specialists, 
here, 
it 
was 
started 
to 
summarize 
reactions, 
such 
that 
they 
can 
be 
utilized 
for 
a 
PN 
model 
and 
subsequent 
analyses, 
e.g. 
in 
silico 
testing 
of 
hypotheses. 
A 
number 
of 
analysis 
strategies 
accustomed 
to 
PN 
representation 
of 
signaling 
networks 
is 
presented 
in 
the 
following 
sections. 



Denition 
of 
Petri 
Nets 


3.2 
Methods 
3.2.1 
Denition 
of 
Petri 
Nets 
The 
spliceosomal 
assembly 
network 
was 
modeled 
as 
a 
P/T 
net 
(see 
Box 
3.2.1). 
Places 
correspond 
to 
biological 
objects 
(e.g. 
RNA 
regions, 
protein 
factors, 
protein 
complexes 
etc.), 
whereas 
transitions 
correspond 
to 
processes, 
which 
act 
upon 
objects 
(e.g. 
protein 
interaction, 
phosphorylation 
reactions, 
protein-mRNA 
binding 
etc.). 
The 
direction 
of 
arcs 
denes 
pre-places 
(pre-transitions) 
and 
post-places 
(post-transitions). 
Tokens 
represent 
movable 
objects. 
They 
are 
used 
to 
model 
the 
equivalent 
of 
signal 
or 
mass 

ow 
units 
and 
are 
symbolized 
by 
black 
dots 
on 
places. 
The 
maximum 
number 
of 
tokens 
that 
a 
place 
can 
hold 
is 
dened 
by 
its 
capacity. 
The 
distribution 
of 
tokens 
over 
all 
places 
is 
called 
a 
marking. 
Each 
marking 
denes 
a 
certain 
state 
of 
the 
system. 


Box 
3.2.1 
Petri 
net 
denitions: 
A 
Petri 
net 
(PN) 
is 
a 
directed, 
labeled, 
bipartite 
graph 
consisting 
of 
places 
(circles), 
transitions 
(rectangles) 
and 
arcs 
(arr
ows), 
such 
that 
arcs 
connect 
only 
nodes 
of 
dierent 
type. 
A 
PN 
is 
a 
six-tuple 
Y 
=(P, 
T, 
F, 
K, 
W, 
M0) 
and 
denotes 
a 
Place 
/ 
Transition 
-net 
if 
the 
following 
denitions 
hold 
(231): 


i. 
The 
tuple 
(P, 
T, 
F 
) 
is 
a 
net 
graph 
N 
with 
(a) 
P 
= 
fp1;p2;:::;pn} 
: 
a 
nite 
set 
of 
places 
(b) 
T 
= 
ft1;t2;:::;tm} 
: 
a 
nite 
set 
of 
transitions 
(c) 
F 
. 
(P 
× 
T 
) 
. 
(T 
× 
P 
)=6
Ø 
: 
the 
set 
of 
arcs 
(
ux 
relations 
of 
N) 
(d) 
P 
. 
T 
=6
Ø 
: 
union 
of 
P 
and 
T 
is 
never 
disjunctive 
(e) 
P 
n 
T 
= 
Ø 
: 
sets 
P 
and 
T 
are 
disjunctive 
ii. 
K 
: 
P 
. 
N. 
f1g: 
the 
capacity 
of 
places 
iii. 
W 
: 
F 
. 
N: 
the 
arc 
weights 
iv. 
M 
: 
P 
. 
N. 
f1g: 
the 
marking 
of 
Y 
if 
8p 
. 
P 
: 
M(p) 
= 
K(p) 
with: 
• 
M0 
: 
P 
. 
N0 
: 
the 
initial 
marking 
w

• 
M0 
. 
M0: 
the 
consecutive 
marking 
given 
a 
ring 
sequence 
w 
w

• 
M0 
. 
denes 
a 
ring 
sequence, 
which 
is 
activated 
under 
M0 
for 
which 
holds: 
w 
= 
;. 
M. 
= 
M0 
or 
t1:::tk

9M. 
. 
M 
(Y 
): 
M0 
. 
M. 



Methods 
79 


Places 
and 
transitions 
are 
connected 
via 
directed 
arcs 
such 
that 
arcs 
connect 
only 
nodes 
of 
dierent 
type. 
Tokens 
represent 
movable 
objects. 
They 
are 
used 
to 
model 
the 
equivalent 
of 
signal 
and/or 
mass 

ow 
units 
and 
are 
symbolized 
by 
black 
dots 
on 
places. 
The 
maximum 
number 
of 
tokens 
that 
a 
place 
can 
hold 
is 
dened 
by 
its 
capacity. 
Adjacency 
relations 
between 
nodes 
are 
dened 
by 
incoming 
and 
outgoing 
arcs. 
Hence, 
the 
set 


x 
:= 
fy 
| 
(y, 
x) 
. 
F 
} 
(3.1) 
denes 
pre-places, 
which 
feed 
a 
transition 
y 
. 
T 
with 
tokens. 
Transitions 
without 
pre-places 
are 
called 
input 
transitions 
and 
represent 
external 
sources. 


Accordingly, 
the 
set 
x• 
:= 
fy 
| 
(x, 
y) 
. 
F 
} 
(3.2) 


denes 
post-places, 
which 
draw 
tokens 
from 
a 
transition 
y 
. 
T 
. 
Transitions 
without 
post-places 
are 
called 
output 
transitions 
represent 
external 
sinks. 
PN 
are 
commonly 
modeled 
as 
transition 
bounded 
net, 
where 
each 
node 
x 
. 
P 
n 
T 
is 
a 
boundary 
node 
if 
x 
= 
;. 
x• 
= 
Ø 
(232). 


The 
dynamic 
behavior 
of 
the 
network 
is 
realized 
through 
the 
ring 
of 
transitions 
which 
model 
the 
activity 
of 
biochemical 
reactions. 
A 
transition 
can 
re 
(is 
enabled) 
if 
all 
pre-places 
are 
covered 
by 
at 
least 
one 
token 
(see 
Box 
3.2.2). 
Tokens 
represent 
molecules 
or 
moles. 
During 
ring 
of 
a 
transition 
according 
to 
the 
corresponding 
arc 
weights 
and 
ring 
rules, 
the 
number 
of 
tokens 
is 
decreased 
on 
the 
pre-places 
and 
increased 
on 
the 
post-places. 
Consequently, 
the 
marking 
of 
the 
net 
is 
changing, 
resulting 
in 
a 
new 
state 
of 
the 
PN. 
Note 
that 
in 
signal 
transduction 
networks 
without 
mass 

ow 
and 
reaction 
stoichiometry, 
it 
is 
reasonable 
to 
interpret 
arc 
weights 
rather 
as 
information 
units“ 
with 
a 
default 
value 
of 
one 


” 
than 
as 
reaction 
quantities 
(see 
Box 
3.2.3). 


w

Starting 
from 
an 
initial 
marking 
M0 
one 
can 
dene 
a 
ring 
sequence 
M0 
!;w 
= 
t1 
:::tk 
2T 
(cf. 
Box 
3.2.1) 
as 
a 
subset 
of 
transitions 
within 
the 
PN, 
which 
corresponds 
to 
a 
specic 
signal 
propagation 
through 
the 
biological 
assembly 
network. 
For 
each 
ring 
sequence 
w 
a 
frequency 
vector 
w= 
(#(t1;w) 
::. 
#(tn;w)) 
(also 
called 
Parikh-vector) 
can 
be 
assigned, 
which 
indicates 
how 
often 
each 
transition 
res. 
The 
change 
of 
the 
net 
marking 
can 
be 
determined 
by: 


w

M0 
!M. 
= 
M0 
+ 
C· 
w(3.3) 



Denition 
of 
Petri 
Nets 


whereat 
C 
is 
the 
incidence 
matrix, 
in 
which 
rows 
and 
columns 
correspond 
to 
P 
and 
T 
, 
respectively, 
and 
the 
matrix 
elements 
describe 
the 
change 
of 
token 
number 
on 
a 
place, 
when 
a 
transition 
res. 
For 
metabolic 
networks, 
the 
incidence 
matrix 
corresponds 
to 
the 
stoichiometric 
matrix. 


Box 
3.2.2 
PN 
ring 
rules 


The 
activity 
of 
reactions 
in 
a 
PN 
system 
(dened 
in 
Box 
3.2.1) 
is 
simulated 
by 
ring 
of 
transitions, 
which 
is 
symb
olized 
by 
a 
token 
change. 
A 
transition 
t 
can 
only 
re, 
if 
it 
is 
enabled 
by 
satisfying 
following 
two 
conditions: 


i. 
M(t) 
= 
W 
(pi;t) 
: 
all 
its 
pre-places 
are 
occupied 
by 
at 
least 
as 
many 
tokens 
as 
the 
weights 
of 
the 
incoming 
arcs 
prescribe. 
ii. 
K(t) 
= 
W 
(t, 
pi) 
: 
all 
its 
post-places 
must 
have 
at 
least 
a 
capacity 
as 
the 
weights 
of 
the 
outgoing 
arcs 
dictate 
p1p2p3t123p3p2p1t132
The 
ring 
sequence 
w 
can 
be 
determined 
from 
the 
solution 
of 
the 
homogenous 
equation 
system 
C· 
w= 
0 
which 
is 
valid 
if 
the 
signal 
or 
information 

ow 
within 
the 
network 
is 
assumed 
to 
be 
conserved. 
This 
forms 
the 
base 
to 
compute 
systems 
invariants 
from 
the 
incidence 
matrix, 
which 
can 
be 
divided 
in 
two 
dierent 
vectors 
w= 
xor 
w= 
y
depending 
on 
the 
orientation 
of 
C. 


Figure 
3.3: 
Transition 
ring 
behavior: 
a) 
Transition 
t1 
is 
enabled 
because 
pre-places 
p1 
and 
p2 
are 
occupied 
by 
as 
many 
tokens 
as 
their 
arc 
weights 
prescribe. 
No 
indicated 
arc 
weight 
means 
per 
default 
an 
arc 
weight 
of 
one, 
b) 
After 
ring 
of 
t1 
two 
tokens 
of 
p1 
and 
one 
token 
of 
p2 
are 
consumed 
and 
three 
tokens 
of 
p3 
are 
produced. 
Note 
that 
in 
signaling 
networks, 
the 
number 
of 
consumed 
tokens 
must 
not 
necessarily 
be 
equal 
to 
the 
number 
of 
produced 
tokens. 


The 
vector 
xis 
called 
nonn
egative 
place 
invariant 
(P-
invariant) 
if 
it 
solves 
the 
homogeneous 
equation 
system 


CT 
· 
x=0: 
x1 
:::xn 
. 
N0 


(3.4) 
The 
elements 
of 
a 
P-
invariant 
can 
be 
interpreted 
as 
conservation 
relation 
for 
tokens. 
For 
an 
initial 
marking 
M0 
holds: 


8M. 
. 
[M0. 
: 
M. 
· 
x 
= 
M0 
· 
x 
(3.5) 


whereat 
M. 
denes 
a 
consecutive 
marking 
(see 
Box 
3.2.1), 
that 
is, 
a 
subset 
of 
the 
reachability 
set 
[M0i, 
which 
denes 
all 
possible 
states 
in 
the 
net 
that 
can 
be 
reached 
from 
M0 
by 
ring 
of 
w. 
The 
solution 
vector 
y 
is 
called 
non-negative 
transition 
invariant 
(T-invariant) 
if 
it 
is 
a 
solution 
of 
the 
homogeneous 
equation 



Methods 
81 


system 
C· 
y=0: 
y1 
:::yn 
. 
N0 
(3.6) 


A 
T-invariant 
is 
a 
transition 
sequence 
that 
after 
ring 
reproduces 
a 
certain 
marking 
(state) 
of 
the 
network 
(Equation 
3.5). 
A 
T-invariants 
Parikh-vector, 
indicates 
how 
often 
each 
transition 
has 
to 
re 
in 
order 
to 
reach 
the 
same 
state 
(marking) 
again. 
T-invariants 
are 
minimal 
if 
there 
exists 
no 
smaller 
positive 
T-invariant 
such 
that 
y. 
: 
(y 
..y0) 
> 
0 
(231). 
Hence, 
minimal 
T-invariants 
are 
not 
further 
decomposable 
into 
smaller 
T-invariants. 
The 
same 
holds 
for 
minimal 
P-invariants. 
In 
the 
following 
the 
term 
T-invariant“ 
(P-invariant\) 
stands 
as 
abbreviation 
for 


"” 


minimal 
non-negative 
T-invariant 
(P-invariant). 


T-invariants 
can 
be 
interpreted 
as 

ux 
vectors. 
In 
biochemical 
terms, 
it 
was 
shown, 
that 
under 
steady 
state 
conditions 
a 
metabolic 
network 
can 
be 
decomposed 
into 
sets 
of 
minimal 
meaningful 
reaction 
sequences 
(elementary 

ux 
modes\), 


"
which 
may 
form 
a 
variety 
of 

ux 
patterns 
when 
expressed 
as 
non-negative 
linear 
combinations 
(220, 
221). 
Elementary 

ux 
modes 
correspond 
to 
non-negative 
minimal 
T-invariants. 



Denition 
of 
Petri 
Nets 


Box 
3.2.3 
PN 
as 
signal 
relay 
networks 


Signaling 
pathways 
are 
often 
mixed 
in 
their 
types 
of 
involved 
reactions. 
Commonly 
known 
are 
phosphorylation 
events 
where 
kinases 
modify 
spec
ial 
domains 
of 
proteins, 
resulting 
in 
the 
capabili
ty 
to 
bind 
proteins. 
In 
contrast 
to 
metabolic 
netw
orks, 
where 
an 
enzymatic 
reaction 
is 
clearly 
de
ned 
by 
its 
stoichiometry, 
in 
signaling 
events 
one 
often 
observes 
only 
a 
state 
that 
coincides 
with 
a 
certain 
function, 
without 
exact 
knowledge 
about 
the 
amount 
of 
the 
participating 
signal 
molecules. 
For 
example, 
the 
proteins 
A 
and 
B 
form 
the 
dimer 
AB 
which 
may 
function 
as 
the 
necessary 
signal 
to 
recruit 
an 
important 
third 
factor 
C. 
Reducing 
the 
arc 
weight 
to 
one 
is 
a 
meaningful 
simplication 
to 
circumvent 
the 
lack 
of 
stoichiometric 
information. 
It 
can 
be 
interpreted 
as 
the 
minimal 
information 
unit 
triggered 
by 
an 
maintenance 
concentration 
of 
the 
necessary 
molecules, 
to 
achieve 
the 
minim
al 
requirement 
for 
signal 
transduction. 


a=11Bm=1a=12m=0Aa=13ABa=11Bm=1a=12m=1Aa=13ABa=11Bm=0a=12m=0Aa=13ABa=11Bm=1a=12m=0Aa=13ABa)
b)
Figure 
3.4: 
Example 
of 
signal 

ow 
in 
PN 
via 
a 
reduced 
arc 
weight 
to 
one, 
m 
= 
number 
of 
tokens 
per 
place, 
a 
= 
arc 
weights, 
A 
and 
B 
= 
interacting 
molecules 
a) 
only 
factor 
A 
is 
present, 
indicated 
by 
the 
token 
at 
place 
B, 
hence 
the 
transition, 
which 
models 
the 
reaction 
is 
not 
enabled 
b) 
both 
required 
factors 
are 
present, 
indicated 
by 
the 
token 
on 
each 
place 
of 
A 
and 
B 
respectively, 
hence 
the 
transition 
can 
re 
and 
generates 
the 
signaling 
complex 
AB 


The 
spliceosomal 
assembly 
net 
was 
modeled 
as 
transition-bounded 
net, 
such 
that 
no 
places 
without 
pre-or 
post 
transitions 
exist, 
but 
transitions 
without 
pre-and 
post 
places. 
The 
boundary 
transitions 
describe 
reactions 
that 
connect 
to 
external 
sources 
(input 
transitions) 
and 
external 
sinks 
(output 
transitions) 
of 
the 
assembly 
network, 
whose 
reactants 
can 
be 
dened 
as 
buered 
substances 
at 
xed 
concentrations 
(220). 
In 
biological 
interpretation 
that 
means, 
all 
reactants 
feeding 
input 
transitions 
or 
leaving 
output 
transitions 
are 
considered 
to 
be 
external. 


All 
reactants 
(places) 
are 
modeled 
in 
non-limited 
amounts 
with 
a 
capacity 
of 
K(p) 
!8 
and 
an 
inital 
marking 
of 
one 
token 
per 
place 
to 
enable 
each 
transition. 
The 
model 
has 
been 
validated 
using 
PN 
analysis 
techniques. 
First, 
by 
determining 
the 
static 
and 
dynamic 
properties, 
using 
the 
programs 
Ina 
(233) 
and 
Pina 
(234), 
and 
second 
by 
computing 
substructures 
as 
maximal 
common 
transition 
sets 
(MCTS). 
The 
PN 
model 
was 
designed, 
using 
the 
PN 
editor 
Snoopy 
(235). 


The 
initial 
XML 
format, 
was 
converted 
into 
a 
pnt 
le 
format 
which 
served 
as 
input 
le 
for 
the 
structural 
analysis 
by 
Ina 
using 
the 
command 
sequence 
given 
in 
Table 
C.2. 
With 
increasing 
network 
size 
and 
complexity, 
the 
number 
of 
T-invariants 
can 
ex



Methods 
83 


ponentially 
grow. 
Two 
approaches 
were 
employed 
to 
facilitate 
the 
validation 
of 
the 
model: 
i) 
decomposition 
into 
disjunctive 
sub 
networks 
(MCTS) 
and 
ii) 
decomposition 
into 
overlapping 
sub 
networks 
(T-clusters) 


Partitioning 
of 
T-invariants 
into 
MCTS 


MCTS 
are 
based 
on 
a 
matrix 
D 
in 
which 
rows 
and 
columns 
correspond 
to 
T 
and 
X, 
respectively, 
with 
T 
dening 
the 
set 
of 
transitions 
and 
X 
dening 
the 
set 
of 
T-invariants. 
Each 
row 
constitutes 
a 
subset 
I 
. 
X 
of 
T-invariants 
that 
share 
a 
considered 
transition 
t. 
Biologically, 
this 
means 
that 
a 
specic 
reaction 
is 
part 
of 
a 
certain 
number 
of 
all 
possible 
and 
minimal 
steady 
state 
signaling 
pathways 
within 
the 
network. 
All 
transitions, 
which 
in 
this 
way 
are 
shared 
exclusively 
by 
the 
same 
set 
of 
T-invariants, 
form 
an 
MCTS 
A 
. 
T 
for 
which 
holds: 


8ti;tj 
. 
T 
: 
ti;tj 
. 
A 
(. 
I(ti)= 
I(tj). 
(3.7) 


The 
set 
of 
all 
transitions 
of 
T-invariant 
x 
. 
X 
is 
called 
support 
of 
x 
and 
denoted 
supp(x). 
Given 
Equation 
3.7 
it 
follows 
that 
each 
MCTS 
is 
either 
a 
subset 
of 
the 
support 
of 
a 
T-invariant 
or 
does 
not 
participate 
in 
a 
T-invariant 
at 
all: 


8~y 
= 
x 
. 
X 
: 
A 
. 
supp(x) 
. 
A 
n 
supp(x)= 
;. 
(3.8) 


Clustering 
of 
T-Invariants 


A 
clustering 
of 
the 
T-invariants 
was 
performed 
to 
nd 
similarities 
imposed 
by 
transitions 
that 
are 
shared 
between 
dierent 
T-invariants. 
T-clusters 
dene 
sub 
networks 
that 
can 
overlap 
(234). 
They 
facilitate 
the 
identication 
of 
frequently 
traversed 
routes, 
which 
are 
formed 
by 
common 
subsets 
of 
reactions 
and 
are 
thought 
to 
highlight 
more 
important 
structures 
within 
the 
net. 
The 
cluster 
analysis 
was 
done 
by 
computing 
a 
distance 
measure 
according 
to 
the 
transformed 
Tanimoto 
coecient 
(234), 
which 
is 
also 
known 
as 
binary 
distance 
or 
Jaccard 
index. 
This 
measure 
is 
well 
suited 
for 
comparing 
signaling 
pathways 
in 
vector 
representation, 
because 
it 
is 
robust 
against 
variations 
in 
the 
total 
number 
of 
reactions 
in 
a 
network 
by 
considering 
reactions, 
missing 
in 
both 
of 
the 
compared 
T-Invariants, 
as 
additional 
evidence 
of 
similarity. 
Hence, 
three 
binary 
states 
can 
be 
specied: 
M11, 
the 
number 
of 
reactions 
present 
in 
both 
compared 
T-Invariants, 
M10, 
the 
number 
of 
reactions 
present 
only 
in 
T-invariant 
Ti 
and 
M10 
the 
number 



Model 
Renement 


of 
reactions 
present 
only 
in 
Tj. 
The 
Jaccard-Tanimoto 
similarity 
measure 
follows 
as: 


M11

J(Ti;Tj) 
= 
(3.9)
M01 
+ 
M10 
+ 
M11 


and 
the 
Tanimoto 
distance 
as: 


M01 
+ 
M10

D(Ti;Tj)= 
=1 
- 
J 
(3.10)
M01 
+ 
M10 
+ 
M11 
The 
corresponding 
distance 
tree 
of 
related 
T-invariants 
was 
constructed 
by 
the 
UPGMA-algorithm 
(236). 
A 
threshold 
of 
80% 
was 
chosen 
to 
merge 
T-invariants 
with 
less 
than 
20% 
dierence 
into 
the 
same 
subtree. 
The 
same 
distance 
measure 
and 
clustering 
algorithm 
was 
used 
to 
create 
a 
color 
map 
(cf. 
Figure 
3.14). 
A 
color 
map 
is 
a 
graphical 
way 
of 
displaying 
matrices 
by 
using 
colors 
to 
represent 
the 
numerical 
values. 
Due 
to 
the 
binary 
(on/off 
= 
present 
/ 
non-present) 
status 
of 
transitions 
within 
T-invariants, 
a 
simplied 
two 
color 
mode 
was 
chosen, 
where 
dark 
and 
light 
blue 
tones 
indicate 
the 
presence 
and 
absence 
of 
an 
reaction, 
respectively. 
The 
color 
map 
also 
rearranges 
rows 
and 
columns 
of 
the 
distance 
matrix 
so 
that 
similar 
rows, 
and 
similar 
columns, 
are 
grouped 
together, 
according 
to 
the 
distance 
tree. 
This 
representation 
facilitates 
to 
visualize 
block 
patterns 
of 
transitions, 
shared 
by 
dierent 
T-invariants. 


3.2.2 
Model 
Renement 
Many 
biological 
signaling 
processes 
in 
the 
human 
cell 
are 
well 
documented, 
for 
example 
the 
caspase 
cascade 
of 
apoptosis 
or 
the 
communication 
network 
of 
cytokines 
between 
immune 
cells. 
Reactions 
of 
these 
signaling 
pathways 
can 
be 
found 
in 
databases 
as 
Kegg 
(237) 
or 
Transpath 
(238). 
Although 
the 
spliceosome 
is 
for 
many 
years 
under 
investigation, 
no 
consistent 
and 
wholistic 
network 
has 
been 
published 
so 
far. 
Reactions 
involved 
in 
spliceosome 
assembly 
were 
biochemically 
described, 
but 
not 
formalized. 


Because 
of 
the 
multitudity 
of 
involved 
proteins 
a 
high 
initial 
eort 
was 
necessary 
to 
manually 
review 
literature 
that 
describe 
or 
suggest 
reactions 
involving 
these 
proteins. 
Known 
experimental 
results 
were 
taken 
as 
base 
to 
extract 
reactions 
applicable 
for 
designing 
the 
PN 
model, 
but 
were 
often 
only 
vaguely 
or 
contradictory 
described. 
Therefore, 
depending 
on 
available 
data, 
reactions 
and 
its 
participating 
factors 
were 
summarized 
or 
abstracted. 
A 
naming 
convention 
for 
reactions 
was 



Methods 
85 


introduced 
to 
give 
a 
quick 
idea 
of 
the 
nature 
of 
the 
reaction 
(e.g., 
bdg“ 
for 


” 
binding 
matur“ 
for 
maturation, 
ass“ 
for 
assignment). 
All 
reactions 
used 
for 


"” 
the 
model 
are 
summarized 
in 
Table 
C.1. 
A 
processing 
scheme 
was 
developed 
(see 
Figure 
3.5), 
which 
guides 
from 
the 
extraction 
of 
reactions 
from 
literature, 
through 
their 
incorporation 
into 
the 
model 
network 
to 
the 
subsequent 
validation 
by 
T-invariant 
and 
MCTS 
computation. 
In 
average, 
each 
protein 
or 
reaction 
of 
the 
network 
was 
supported 
by 
at 
least 
one 
experimental 
observation 
or 
thoroughly 
inferred 
from 
such. 
This 
time 
intensive 
preparation 
could 
be 
more 
automatized 
in 
future, 
but 
human 
inspection 
for 
correctness 
of 
the 
pathways 
will 
remain 
an 
important 
aspect 
of 
quality 
control 
of 
the 
nal 
model. 
The 
processing 
steps 
that 
were 
iteratively 
applied 
to 
extend 
and 
revalidate 
the 
model, 
can 
be 
summarized 
as 
follows: 


i. 
Collect 
review 
articles 
about 
E-and 
A-complex 
assembly 
ii. 
Identify 
and 
extract 
reactions, 
and 
develop 
the 
rst 
PN 
model 
iii. 
Compute 
T-invariants, 
MCTS 
and 
T-clusters 
iv. 
Check 
biological 
meaning 
of 
T-invariants/MCTS: 
rst 
model 
check 
. 
describe 
and 
annotate 
T-invariants/MCTS 
repeated 
model 
check 
. 
compare 
previously 
computed 
T-invariants/MCTS 
if 
necessary, 
update 
annotation 
implausible 
result 
. 
return 
to 
literature, 
consult 
independent 
reports 
on 
reactions 
that 
appear 
not 
to 
be 
covered 
by 
T-invariants 
v. 
Check 
composition 
of 
T-clusters 
vi. 
Extend 
model 
by 
further 
reactions 
reported 
for 
B 
and 
C-complex 
formation 
This 
procedure 
allows 
the 
iterative 
and 
semi-automated 
evaluation 
of 
the 
results 
of 
the 
T-invariant 
/ 
MCTS 
computation 
to 
provide 
support 
for 
the 
PN 
model. 
However, 
presently 
the 
raw 
output 
of 
the 
PN 
editor 
tool 
(235) 
has 
to 
be 
manually 
checked 
after 
each 
processing 
round, 
because 
of 
the 
rearrangement 
of 
internal 
node 
IDs 
after 
editing 
the 
network. 
This 
is 
a 
limitation 
of 
the 
present 
PN 
editing 
software. 
After 
this 
intermediate 
check, 
all 
reactions 
(see 
Table 
C.1) 
were 
automatically 
written 
and 
formatted 
into 
tabular 
form 
by 
a 
Perl 
script. 
Nevertheless, 
consultation 
of 
experimental 
literature 
often 
involve 
an 
elaborate 
search 
for 
reports, 
which 
describe 
not 
only 
the 
immediate 
partners 
of 
the 
reacting 
molecule, 



Model 
Renement 


but 
if 
possible 
also 
surrounding 
processes 
for 
connecting 
the 
reaction 
appropriately 
to 
the 
existing 
network. 


INA.pm-inherits basic functions and 
object attributes of PNT.pm-collection 
LiteratureSNOOPY:
edit PN; add new 
reactionsSTARTINA / PInAcomputing t/p-
invariantsAnnotation 
available?YPNT.pm-provides basic functions for handling 
PN data structures; 
-reading and storing reactions 
-reading and storing reactants-creating pre-formatted TeX tables 
-outfile-documenting functionst/p-invariants / MCTS:
-compare existing with new-inherit annotation of 
unchanged 
Annotation-complete/ updatemanually 
NSNOOPY:
check (new) t/p-
invariants, MCTSt/p-invariants / MCTS:
-write TeX table version 
MCTS-computeBiol.
meaningful?
YNfinal?NENDClustering /
Colormapwith RY06.07.2008Figure3_1_flowchart.VSD
Figure 
3.5: 
Overview 
of 
methodology 
applied 
to 
model 
the 
spliceosomal 
assembly 
pathw
ay. 
The 
inner 
part 
shows 
steps 
that 
involve 
in 
house 
software 
(Perl) 
for 
reformatting 
and 
analyzing 
the 
output 
produced 
by 
external 
tools. 



Methods 
87 


3.2.3 
Data 
Preparation 
To 
handle 
the 
output 
given 
by 
the 
program 
Ina, 
several 
perl 
scripts 
were 
written, 
which 
run 
on 
top 
of 
two 
libraries, 
collecting 
functions 
for 
analysis 
of 
*.pnt 
and 
*.ina 
les. 
These 
served 
to: 


i. 
Rewrite 
summarizing 
reactions 
from 
*.pnt 
les, 
provided 
by 
Ina 
(get 
reactions.pl) 
ii. 
Find 
inconsistencies 
within 
the 
model 
by 
compiling 
the 
oending 
transitions 
in 
case 
the 
network 
is 
not 
completely 
covered 
with 
transitions 
(check 
coverage.pl) 
iii. 
Compute 
MCTS 
and 
print 
them 
in 
two 
dierent 
summaries 
(1. 
fractions 
of 
T-invariants 
sharing 
the 
MCTS; 
2. 
number 
of 
transitions 
involved 
in 
the 
MCTS) 
along 
with 
some 
descriptive 
gures 
about 
types 
and 
frequencies 
of 
T-invariants 
(compute 
mcts.pl); 
this 
script 
also 
creates 
a 
core 
le 
for 
annotating 
the 
MCTS, 
from 
which 
at 
a 
second 
run 
the 
annotation 
is 
written 
into 
a 
ready-to-use 
formatted 
table 
iv. 
Annotate 
T-invariants 
and 
print 
them 
into 
a 
formatted 
table 
(print 
tinv 
report.pl) 

Application 
of 
PN 
Modules 
in 
Splicing 
Models 


3.3 
Results 
3.3.1 
Application 
of 
PN 
Modules 
in 
Splicing 
Models 
Inspired 
by 
previous 
suggestions 
(209, 
239, 
240), 
a 
number 
of 
smaller 
net 
modules 
were 
designed 
at 
rst, 
which 
served 
as 
building 
blocks 
describing 
dierent 
reactions 
or 
interactions 
between 
spliceosomal 
components. 
To 
reach 
a 
valid 
model, 
these 
net 
modules 
are 
useful 
for 
testing 
modeling 
strategies, 
which 
appropriately 
re
ect 
observed 
biological 
behavior 
of 
parts 
of 
the 
network. 
In 
general, 
the 
modeling 
of 
biologically 
meaningful 
modules 
within 
signaling 
pathways 
strongly 
depends 
on 
the 
depth 
of 
experimentally 
veried 
knowledge 
of 
the 
described 
mechanism. 
The 
following 
net 
modules 
have 
been 
used 
for 
modeling 
the 
basic 
reactions 
of 
spliceosomal 
assembly: 


i. 
Allosteric 
interaction 
describes 
the 
process 
in 
which 
a 
protein 
binds 
to 
a 
specic 
domain 
of 
a 
target 
protein, 
induces 
a 
conformational 
change 
at 
a 
distant 
site, 
and 
hence 
rendering 
the 
target 
protein 
itself 
active 
or 
inactive. 
In 
spliceosomal 
processes 
this 
concept 
can 
be 
extended 
to 
the 
level 
of 
protein 
complex 
association, 
where 
the 
binding 
of 
special 
factors 
is 
crucial 
for 
subsequent 
progress 
through 
intermediate 
assembly 
stages 
(see 
Box 
3.1.1). 
A 
module 
for 
this 
biochemical 
process 
is 
decomposable 
into 
four 
Tinvariants, 
two 
of 
which 
being 
cycles 
hat 
describe 
the 
repeated 
association 
and 
disintegration 
of 
the 
intermediate 
complex, 
AB, 
and 
the 
nal 
complex, 
ABC 
(cf. 
Figure 
3.6). 
Note 
that 
dissociation 
is 
restricted 
to 
AB 
+ 
C 
or 
A 
+B 
+C, 
since 
AC 
or 
BC 
are 
forbidden“ 
by 
the 
allosteric 
rule 
imposed 
” 


during 
complex 
formation. 
The 
same 
model 
strongly 
reduces 
structural 
complexity 
by 
exhibiting 
one 
T-invariant, 
covering 
all 
transitions. 


Further 
special 
cases 
of 
allostery 
can 
be 
distinguished, 
and 
accordingly 
different 
net 
modules 
were 
designed. 


(a) 
Allosteric 
inhibition 
depends 
on 
the 
presence 
of 
a 
specic 
domain 
within 
a 
subcomplex, 
an 
inhibitor 
may 
bind 
to 
the 
complex, 
inducing 
either 
disassembly 
of 
the 
complex 
or 
non-functionality 
of 
the 
complex 
towards 
specic 
downstream 
interactions 
(cf. 
Figure 
3.7). 
This 
leads 
to 
an 
extension 
of 
the 
module 
depicted 
in 
Figure 
3.6b, 
where 
the 
heterotrimer 
ABC 
can 
either 
participate 
in 
further 
reactions 
(ABC 
out), 
dissociates 
or 
binds 
to 
an 
inhibitory 
factor 
I. 
After 
sequestration 
of 

Results 
89 


(a) 
Allosteric 
cascade 
with 
dissociation 
(b) 
Simplied 
allosteric 
cascade 
Figure 
3.6: 
Example 
of 
a 
module 
for 
protein 
complex 
association 
where 
two 
molecules 
A 
and 
B 
form 
a 
heterodimer, 
AB, 
which 
denes 
a 
necessary 
step 
for 
binding 
the 
factor, 
C, 
in 
progressing 
the 
complex 
assembly 
through 
complex 
ABC. 
(a) 
PN 
module 
with 
reactions, 
forming 
(solid 
lines) 
and 
decomposing 
(dashed 
lines) 
intermediate 
complexes. 
Two 
of 
four 
computed 
T-invariant 
pathways, 
completely 
covering 
this 
module 
are 
colored 
and 
dene 
the 
main 
signaling 
route 
(blue) 
and 
a 
cycle 
(red); 
green 
= 
source 
factors, 
grey 
= 
intermediate 
factors 
(complexes); 
blue 
= 
target 
complex; 
(b) 
The 
same 
model 
without 
dissociation 
reactions 
strongly 
reduces 
structural 
complexity, 
leading 
to 
one 
T-invariant. 


complex 
IAB, 
AB 
may 
either 
dissociate 
again 
or 
remain 
for 
a 
certain 
time 
non-functional 
within 
IAB. 
Thus, 
it 
is 
modeled 
as 
output 
transition 
(IAB 
out). 
The 
module 
designed 
with 
dissociation 
reactions 
again 
results 
in 
several 
T-invariants 
(data 
not 
shown), 
including 
sustained 
cycles 
of 
associations 
and 
dissociations. 
In 
contrast, 
the 
module 
reduced 
for 
dissociation 
reactions 
exhibits 
two 
minimal 
Tinvariants, 
re
ecting 
only 
the 
important 
aspects 
of 
functional 
ABC 
and 
non-functional 
IAB 
formation. 
The 
simplied 
version 
may 
suce 
in 
many 
cases, 
in 
particular 
when 
time 
points 
of 
protein 
activities 
are 
yet 
unknown, 
for 
example, 
the 
time 
when 
a 
specic 
factor 
dissociates 
from 
an 
intermediate 
complex. 
This 
net 
module 
is 
easily 
modied 
from 
the 
basic 
allosteric 
cascade 
shown 
in 
Figure 
3.6b) 
by 
adding 
the 



Application 
of 
PN 
Modules 
in 
Splicing 
Models 


inhibitor 
I 
and 
introducing 
an 
additional 
edge 
that 
purges 
AB. 



Figure 
3.7: 
Reduced 
PN 
module 
which 
describes 
the 
formation 
of 
an 
inhibiting 
interm
ediate 
complex 
(IAB). 
The 
blue 
highlighted 
pathway 
covers 
reactions 
which 
result 
in 
a 
functional 
target 
complex 
ABC, 
while 
the 
red 
colored 
pathway 
describes 
a 
T-invariant 
covered 
by 
reactions 
which 
result 
in 
a 
non-functional 
complex 
IAB. 


(b) 
Allosteric 
enhancement 
describes 
the 
presence 
of 
a 
specic 
protein 
factor, 
which 
increases 
the 
anity 
of 
other 
proteins 
to 
participate 
in 
subsequent 
reactions 
(e.g., 
subcomplex 
formation, 
RNA 
recognition 
etc.). 
The 
structural 
analysis 
results 
in 
four 
T-invariants, 
two 
of 
which 
producing 
the 
target 
complex 
AB. 
The 
interaction 
of 
factor 
A 
and 
B 
can 
result 
in 
a 
dimerized 
complex 
AB 
(3.8a, 
red 
pathway), 
however, 
the 
enhancer 
may 
be 
necessary 
for 
the 
protein 
(complex) 
to 
be 
active. 
The 
model 
accounts 
for 
the 
presence 
of 
enhancer 
E 
with 
a 
higher 
output 
of 
AB 
(Figure 
3.8a, 
blue 
pathway) 
due 
to 
an 
increased 
arc 
weight. 
Hence, 
transition 
AB 
out 
has 
to 
re 
twice 
to 
reproduce 
the 
initial 
marking. 
Biologically, 
this 
can 
be 
interpreted 
as 
an 
increased 
signal 
transduction 
as 
AB 
reaches 
a 
state 
of 
higher 
disposition 
for 
participating 
in 
downstream 
reactions. 
The 
reduction 
of 
the 
system 
for 
the 
dissociation 
reaction 
of 
the 
dimer 
AB 
decreases 
the 
number 
of 
T-invariants 
by 
one 
(Figure 
3.8b). 
Two 
T-invariants 
involve 
transition 
assoc 
AB 
but 
only 
one 
produces 
AB, 
while 
the 
other 
purges 
AB 
from 
the 
network 
(Figure 
3.8b, 
red 
pathway). 
The 
enhancer 
involving 
pathway 
via 
assoc 
ABE 
again 
produces 
an 
increased 
amount 
of 
AB, 
thus 
the 
reduced 
model 
captures 
all 
essential 
aspect 
of 
the 
enhancer 
dependent 
complex 
formation. 
ii. 
Enzymatic 
reactions 
describe 
the 
reactions 
where 
a 
catalytically 
active 
enzyme 
acts 
on 
molecular 
groups 
of 
spliceosomal 
proteins, 
e.g., 
kinases 

Results 
91 


(a) 
Allosteric 
enhancement 
with 
dissociation 
(b) 
Simplied 
allosteric 
enhancement 
Figure 
3.8: 
PN 
module 
for 
modeling 
enhanced 
protein-protein 
interaction. 
The 
presence 
of 
enhancer 
proteins 
stabilizes 
complex 
formation 
and 
results 
in 
an 
increased 
output 
of 
dimer, 
AB, 
compared 
to 
dimerization 
without 
the 
in
uence 
of 
enhancer 
protein, 
E. 
Note 
that 
in 
contrast 
to 
Figure 
3.6, 
enhancer 
E 
only 
mediates 
a 
temporary 
eect 
until 
the 
dimer, 
AB, 
has 
stabilized 
its 
interaction. 
(a) 
The 
module 
with 
a 
dissociation 
transition 
for 
dimer, 
AB, 
which 
results 
in 
four 
T-invariants, 
including 
one 
with 
a 
higher 
output 
of 
AB 
(blue 
pathway) 
and 
one 
producing 
AB 
via 
a 
possibly 
undirected 
self 
stabilized 
interaction 
(red 
pathway) 
(b) 
Replacement 
of 
the 
dissociation 
reaction 
by 
an 
output 
transition 
(red 
pathway) 
reduces 
the 
number 
of 
T-invariants, 
while 
preserving 
the 
essential 
model 
function 
of 
enhancer 
dependent 
complex 
formation. 


phosphorylate 
proteins. 
This 
behavior 
was 
modeled 
as 
loop, 
which 
preserves 
the 
marking 
of 
the 
respective 
place 
and 
results 
in 
a 
simple 
conservation 
relation 
(Figure 
3.9a). 
Also 
helicase 
like 
proteins 
with 
DExD/H 
box 
domains 
have 
been 
frequently 
found 
in 
puried 
spliceosomes 
(149) 
and 
were 
here 
considered 
as 
separate 
module. 
This 
module 
was 
extended 
for 
another 
reaction, 
representing 
the 
enhancement 
of 
substrate 
specicity 
of 
the 
helicase 
(see 
Figure 
3.9). 
In 
this 
context, 
the 
rate 
of 
NTP 
hydrolyzation 
has 



Dening 
Biological 
Reactions 
of 
the 
Spliceosome 
Assembly 
Pathway 


been 
proposed 
as 
a 
crucial 
parameter 
for 
splicing 
delity, 
since 
fast 
kinetics 
on 
weak 
or 
incorrect 
protein-substrate 
(RNA, 
protein) 
interactions 
increase 
the 
chance 
of 
dissociation 
of 
essential 
spliceosomal 
proteins. 
In 
consequence, 
such 
defective 
substrates 
could 
be 
submitted 
into 
a 
degradation 
pathway 
(165). 
While 
a 
putative 
degradation 
pathway 
was 
integrated 
as 
a 
branch 
into 
the 
PN, 
the 
hydrolyzation 
activity 
could 
not 
be 
modeled, 
because 
of 
the 
lack 
of 
kinetic 
parameters. 
Finally, 
since 
several 
DExD/H 
box 
proteins 
are 
involved 
in 
spliceosome 
assembly, 
the 
total 
accuracy 
of 
splicing 
may 
depend 
on 
the 
cumulative 
success 
of 
enzyme 
modulated 
signal 
transduction 
along 
the 
pathway, 
further 
complicating 
the 
corresponding 
kinetic 
model. 


(a)Phosphorylation(b)Helicasespecication
Figure 
3.9: 
PN 
modules, 
modeling 
enzymatic 
activities 
found 
in 
protein 
complex 
assemb
ly. 
(a) 
Model 
of 
protein 
activation 
where 
phosphorylation 
of 
a 
specic 
domain 
mediates 
interacting 
capabilities, 
thus, 
in
uencing 
subsequent 
complex 
rearrangements; 
(b) 
Binding 
of 
a 
specic 
factor 
to 
a 
generic 
helicase, 
thus, 
mediating 
substrate 
specicity. 


3.3.2 
Dening 
Biological 
Reactions 
of 
the 
Spliceosome 
Assembly 
Pathway 
In 
an 
iterative 
process 
(see 
Diagram 
3.5) 
literature 
was 
inspected 
to 
isolate 
reactions 
involved 
in 
spliceosome 
assembly. 
Reactions 
and 
proteins 
focus 
on 
knowledge 
reported 
for 
human 
spliceosomal 
processes, 
however 
some 
parts 
where 
rst 
discovered 
in 
yeast 
and 
the 
human 
homologue 
proteins 
discovered 
later. 
In 
this 
way 
the 
model 
does 
not 
discriminate 
between 
an 
explicit 
human 
or 
yeast 
spliceosomal 
assembly 
model. 
All 
reactions 
are 
ordered 
according 
to 
the 
four 
major 
stages 
of 
spliceosome 
assembly, 
E-, 
A-, 
B-and 
C-complex 
(see 
Table 
C.1). 
After 



Results 
93 


each 
major 
stage 
the 
structural 
composition 
of 
the 
assembly 
network 
was 
evaluated 
by 
T-invariant 
analysis. 
The 
net 
was 
only 
extended 
if 
each 
modeled 
reaction 
was 
part 
of 
at 
least 
one 
T-invariant. 
Reactions 
were 
only 
included 
if 
the 
factors 
involved 
could 
be 
integrated 
in 
a 
causal 
order. 
As 
consequence, 
some 
spliceosomal 
factors, 
e.g., 
SPF27, 
CypE, 
HSP73 
and 
others, 
are 
omitted 
from 
the 
model 
due 
to 
lack 
of 
evidence 
for 
a 
specic 
time 
point 
at 
which 
they 
participate 
in 
the 
spliceosomal 
assembly 
process. 
Further, 
it 
was 
assumed 
that 
all 
factors 
for 
which 
no 
evidence 
of 
leaving 
or 
becoming 
destabilized 
in 
spliceosome 
assembly 
is 
given, 
implicitly 
remain 
in 
the 
spliceosomal 
subcomplexes 
until 
nally 
a 
dissociation 
into 
substructures 
(e.g., 
snRNPs) 
and 
their 
recycling 
takes 
place. 
Hence, 
not 
all 
factors 
modeled 
with 
an 
input 
transition 
have 
an 
explicit 
output 
transition. 
This 
is 
reasonable 
since 
many 
substructures 
remain 
intact 
for 
repeated 
rounds 
of 
spliceosomal 
assembly 
(241) 
This 
results 
in 
a 
model 
with 
140 
reacting 
species 
(places) 
comprised 
of 
RNA, 
proteins 
and 
intermediate 
complexes 
was 
established, 
which 
covers 
about 
half 
of 
the 
currently 
known 
spliceosomal 
proteins. 
The 
network 
was 
modeled 
as 
transition-bounded 
network 
with 
all 
places 
connected 
to 
a 
total 
of 
161 
transitions. 
92 
(57%) 
of 
those 
are 
boundary 
transitions, 
splitting 
into 
68 
(74%) 
input 
and 
24 
(26%) 
output 
transitions. 
In 
total 
69 
(43%) 
transitions 
describe 
the 
internal 
reactions 
of 
the 
assembly 
network. 
This 
biological 
network 
was 
modeled 
by 
reactions, 
which 
re
ect 
a 
certain 
hierarchy 
characteristic 
for 
spliceosome 
assembly. 
Therefore 
and 
in 
contrast 
to 
PN 
models 
for 
technical 
processes, 
the 
degree 
of 
concurrency 
of 
reactions 
is 
reduced 
in 
the 
network. 


3.3.3 
Invariant 
Signaling 
Pathways 
in 
Spliceosome 
Assembly 
Pathways 
Corresponding 
to 
T-Invariants 
Structural 
analyses 
resulted 
in 
a 
complete 
coverage 
of 
the 
network 
by 
71 
Tinvariants 
(see 
Table 
C.3). 
All 
T-invariants 
describe 
at 
least 
one 
partial 
pathway 
within 
the 
spliceosomal 
assembly 
process 
and 
are 
biologically 
meaningful. 
12 
Tinvariants 
(17%) 
can 
be 
considered 
to 
be 
trivial 
as 
they 
describe 
solely 
the 
inand 
eux 
of 
the 
DExD/H 
box 
helicases 
Prp5, 
Prp28, 
the 
proteins 
hPrp6, 
SKIP 
and 
hDib1, 
the 
splicing 
factors 
SF1, 
ASF/SF2, 
SC35 
and 
the 
SF3b 
components 
SF3b10, 
SF3b14a, 
SF3b14b 
and 
SF3b49. 
However, 
these 
proteins 
also 
constitute 
a 
set 
of 
spliceosomal 
components 
for 
which 
it 
is 
possible 
to 
narrow 
the 
putative 
point 
of 
exit 
from 
the 
assembly 
process, 
which 
so 
far 
is 
unknown 
for 
the 
majority 



Invariant 
Signaling 
Pathways 
in 
Spliceosome 
Assembly 


of 
proteins 
functioning 
in 
spliceosome 
assembly. 
For 
example, 
the 
four 
smaller 
SF3b 
proteins 
are 
known 
to 
be 
required 
for 
SF3b 
formation, 
but 
are 
not 
detected 
at 
the 
stage 
of 
C-complex 
formation 
(cf. 
supplements 
of 
(151)). 
Each 
set 
of 
reactions 
of 
a 
T-invariant 
provides 
a 
solution 
of 
the 
homogenous 
equation 
system 
derived 
from 
a 
matrix 
of 
all 
reactants 
and 
reactions 
under 
assumption 
of 
a 
steady 
state 
system. 
As 
result 
the 
removal 
of 
a 
single 
reaction 
from 
an 
invariant 
signaling 
pathway 
is 
critical 
for 
functioning 
of 
this 
pathway. 
In 
case 
of 
spliceosome 
assembly, 
this 
does 
not 
mean 
that 
the 
entire 
assembly 
process 
is 
stalled. 
The 
model 
clearly 
illustrates 
that 
dierent 
results 
from 
experimental 
studies 
shape 
up 
to 
a 
network 
with 
an 
inherent 
redundancy 
to 
sustain 
specic 
check 
points 
which 
represent 
crucial 
intermediate 
assembly 
stages. 
For 
example, 
a 
critical 
step 
of 
early 
spliceosome 
assembly 
is 
the 
donor 
or 
5. 
splice 
site 
recognition, 
for 
which 
several 
parallel 
occurring 
signaling 
pathways 
were 
modeled. 
These 
pathways 
contribute 
to 
the 
intermediate 
stage 
of 
E-complex 
assembly 
and 
are 
re
ected 
in 
the 
model 
by 
several 
sets 
of 
T-invariants 
which 
are 
listed 
below. 
Note 
that 
enumerated 
T-invariants 
are 
prexed 
by 
i\. 
T-invariants 
in 
brackets 
describe 
spliceosomal 


” 
subpathways 
that 
involve 
50ss 
recognition, 
but 
do 
not 
proceed 
via 
the 
productive 


branch 
of 
C-complex 
formation 
that 
results 
in 
exon 
ligation: 


i. 
T-invariants 
i13, 
i14 
(i68, 
i69) 
. 
U1 
snRNP 
independent 
50ss 
recognition: 
The 
central 
function 
of 
this 
branch 
is 
transition 
t16.U1 
indep 
5ss 
act, 
which 
models 
the 
activity 
of 
the 
SR 
protein 
SC35. 
The 
presence 
of 
SC35 
is 
sucient 
to 
dene 
a 
50ss 
in 
absence 
of 
a 
functional 
U1 
snRNP 
(143) 
and 
initiates 
contacts 
to 
the 
BP 
occupying 
proteins 
SF1 
and 
U2AF. 
This 
can 
result 
in 
selection 
of 
competing 
50ss, 
which 
render 
this 
pathway 
a 
potential 
candidate 
for 
the 
production 
of 
alternatively 
spliced 
mRNAs 
(171). 
The 
remaining 
transitions 
of 
this 
branch 
are 
t31.U2 
BPS 
bdg2 
and 
t32.U6 
5ss 
bdg2, 
which 
feed 
U2 
snRNP 
and 
U4U5U6 
tri-snRNP 
respectively 
and 
proceed 
the 
assembly 
pathway 
to 
the 
B-complex 
stage. 
Further 
dierences 
in 
the 
T-invariants, 
sharing 
this 
otherwise 
unique 
branch 
of 
5. 
selection, 
exists 
in 
two 
dierent 
ways 
of 
A-complex 
assembly 
via 
early 
t13.17S 
U2 
matur2 
(i13, 
i68) 
or 
late 
t22.17S 
U1 
matur1 
(i14, 
i69) 
action 
of 
the 
enzyme 
SF3b125. 
However, 
for 
i14 
it 
is 
not 
clear 
at 
which 
time 
SF3b125 
leaves 
the 
assembly 
process, 
hence 
this 
T-invariant 
includes 
additionally 
the 
input 
reaction 
t109.SF3b125 
in. 
Another 
variation 
between 
these 
similar 
T-invariants 
is 
the 
dierent 
proceeding 
during 
C-complex 
assembly 
after 
the 
remodeling 
step 
t93.U2 
3ss 
U6 
5ss 
U5 
remod. 
Either 
a 
productive 
spliceosome 
is 

Results 
95 


formed 
via 
one 
type 
of 
pathway 
(i13, 
i14) 
or 
the 
premature 
disassembly 
is 
re
ected 
via 
another 
type 
of 
discard 
pathway 
(i68, 
i69). 


ii. 
T-invariants 
i15-i20 
(i62-i67) 
. 
ASF/SF2 
dependent 
50ss 
recognition: 
These 
T-invariants 
describe 
the 
50ss 
recognition 
via 
contacts 
of 
U1 
factor 
U170K 
and 
the 
exon-bound 
splicing 
factor 
ASF/SF2 
(t12.ASFp 
U170K 
bdg), 
with 
subsequent 
contacts 
of 
U1C 
to 
intron 
bound 
splicing 
factor 
TIA1 
(t9.U1C 
TIA1 
bdg1 
) 
(242, 
243, 
170).The 
U1 
snRNP 
protein 
U170K 
hereby 
interacts 
with 
ASF/SF2 
via 
its 
RS 
domains 
(169). 
Subsequently, 
these 
T-invariants 
show 
some 
individualities, 
allowing 
to 
form 
three 
groups. 
(a) 
Firstly, 
T-invariants 
i15, 
i16 
(i66, 
i67) 
describe 
the 
E-complex 
formation 
via 
U1 
contacts 
to 
the 
branchpoint 
bound 
splicing 
factor 
SF1 
(t58.U1 
SF1 
bdg) 
and 
the 
joining 
of 
the 
auxiliary 
factor 
U2AF, 
after 
its 
recognition 
of 
the 
polypyrimidine 
tract 
t59.U1 
SF1 
U2AF 
bdg. 
Dependent 
on 
the 
U2 
maturation 
via 
SF3b125, 
there 
exist 
again 
two 
dierent 
T-invariants 
for 
this 
mode 
of 
E-complex 
formation. 
(b) 
Secondly, 
the 
presence 
of 
the 
splicing 
factor 
SC35, 
has 
been 
found 
to 
facilitate 
50ss 
recognition 
(t17.U170K 
U2AF35 
bdg) 
but 
required 
the 
U1-complex 
and 
U2AF 
((244)). 
Hereby, 
the 
protein 
FBP11 
helps 
to 
bridge 
U1 
and 
SF1, 
a 
constellation 
which 
is 
in 
agreement 
with 
the 
observation 
that 
SF1 
and 
U2AF 
bind 
cooperatively 
to 
the 
branchpoint 
and 
polypyrimidine 
tract, 
respectively 
(175, 
174). 
This 
mode 
of 
E-complex 
assembly 
is 
re
ected 
by 
the 
T-invariants 
i17, 
i18 
(i64, 
i65). 
(c) 
Thirdly 
the 
T-invariants 
i19, 
i20 
(i62, 
i63) 
describe 
a 
pathway 
of 
Ecomplex 
assembly, 
in 
which 
the 
splicing 
factor 
SC35 
can 
substitute 
for 
the 
requirement 
of 
the 
auxiliary 
factor 
U2AF. 
Again, 
SC35 
bridges 
the 
50ss 
and 
the 
branchpoint 
via 
contacts 
to 
U1 
and 
SF1 
t56.U1 
SC35 
SF1 
bdg, 
but 
subsequently 
U2 
is 
directly 
bound 
to 
this 
intermediate 
complex 
t152.U2 
SC35 
bdg. 
It 
should 
be 
noted 
that 
in 
the 
subsequent 
A-complex 
formation 
this 
pathway 
contains 
some 
ambiguity 
in 
that 
the 
helicase 
UAP56, 
which 
is 
required 
for 
conformational 
change 
in 
transition 
from 
E 
to 
A-complex, 
needs 
the 
U2AF 
component 
U2AF65 
as 
essential 
cofactor 
for 
its 
activity 
(184, 
165). 

Invariant 
Signaling 
Pathways 
in 
Spliceosome 
Assembly 


iii. 
T-invariants 
i21 
-i26 
(i56-i61) 
. 
50ss 
recognition 
in 
the 
5. 
terminal 
exon: 
The 
rst 
donor 
splice 
site 
within 
a 
transcript 
follows 
a 
dierent 
mode 
of 
recognition. 
Here, 
the 
interaction 
of 
U1 
snRNP 
proteins 
(U1C) 
to 
proteins 
of 
the 
cap 
binding 
complex 
(CBC) 
via 
LUC7 
has 
been 
shown 
and 
hence 
was 
modeled 
with 
a 
separate 
reaction 
(t10.U1 
CBC 
5ss 
bdg), 
which 
triggers 
another 
set 
of 
T-invariants. 
However, 
except 
for 
the 
interaction 
with 
the 
cap 
binding 
complex, 
these 
T-invariants 
are 
the 
same 
as 
above 
(ii 
a-c) 
and 
may 
constitute 
novel 
alternative 
pathways 
for 
the 
initial 
spliceosome 
assembly 
at 
newly 
synthesized 
transcripts. 
iv. 
T-invariants 
i27 
-i32 
(i50-i55) 
. 
50ss 
recognition 
via 
U1C 
contacts 
to 
intron 
bound 
splicing 
factor 
TIA1: 
This 
branch 
models 
the 
50ss 
proximal 
(intron) 
binding 
of 
the 
splicing 
regulator 
TIA1, 
which 
stabilizes 
contacts 
of 
U1 
with 
the 
50ss 
via 
interaction 
with 
the 
N-terminal 
region 
of 
the 
U1-C 
protein 
(t30.U1C 
TIA1 
bdg2 
) 
(170). 
As 
alternative 
pathways 
to 
the 
proposed 
joint 
action 
of 
ASF/SF2 
and 
TIA1 
in 
50ss 
selection 
(as 
described 
above), 
T-invariant 
i29 
(i52) 
involve 
SC35 
and 
FBP11 
in 
subsequent 
A-complex 
formation 
or 
either 
only 
SC35 
(i27, 
i54) 
or 
only 
FBP11 
(i31, 
i50) 
as 
U1 
snRNP 
binding 
supporting 
splicing 
factors. 
v. 
T-invariants 
i33 
-i38 
(i44-i49) 
. 
50ss 
recognition 
without 
additional 
splicing 
factors: 
These 
T-invariants 
model 
the 
50ss 
recognition 
without 
the 
parallel 
binding 
of 
the 
U1 
stabilizing 
factors 
ASF/SF2 
or 
TIA1, 
which 
could 
be 
described 
as 
a 
mode 
for 
strong 
donor 
splice 
site 
selection. 
However, 
the 
transition 
from 
E 
to 
A-complex 
may 
require 
conditions 
as 
described 
for 
the 
T-invariants 
in 
ii 
a-c. 
vi. 
T-invariants 
i39, 
i40 
(i70, 
i71) 
. 
50ss 
binding 
by 
U1 
snRNP 
after 
initiating 
contact 
to 
U2 
snRNP 
via 
Prp5: 
These 
T-invariants 
describe 
a 
pathway 
of 
splice 
site 
selection, 
which 
deviates 
from 
the 
classical 
model 
of 
initial 
50ss 
selection. 
The 
ATPase 
Prp5 
bridges 
U1 
and 
U2 
snRNP 
prior 
to 
substrate 
binding, 
that 
is 
U2 
is 
already 
associated 
with 
U1 
and 
Prp5 
when 
it 
binds 
to 
the 
intron, 
a 
fact 
that 
is 
experimentally 
armed 
by 
the 
binding 
of 
Prp5 
to 
U1 
and 
U2 
also 
in 
absence 
of 
pre-mRNA 
and 
by 
the 
ATP 
dependent 
requirement 
for 
Prp5 
for 
prespliceosome 
assembly 
(181). 
The 
T-invariants 
therefore 
contain 
the 
reaction 
t27.U1 
Prp5 
U2 
bdg 
as 
bridging 
step, 
followed 
by 
t28.U1 
5ss 
U2 
U2AF 
bdg 

Results 
97 


describing 
the 
contacts 
with 
the 
50ss 
and 
the 
branchpoint/polypyrimidine 
tract 
associated 
proteins. 
The 
ATP 
dependent 
structural 
rearrangements 
towards 
A-complex 
assembly 
are 
modeled 
by 
t55.unwind1 
U2 
stl2 
a 
step 
that 
releases 
the 
U2 
factor 
SF3a60 
(165). 
The 
UAP56 
catalyzed 
conformational 
changes 
in 
the 
U1/U2 
pre-mRNA 
complex 
are 
modeled 
by 
t29.U1U2 
BPS 
bdg 
and 
complete 
the 
transition 
from 
E 
to 
A-complex. 


Shortly 
after 
or 
in 
parallel 
to 
the 
recognition 
of 
the 
50ss 
by 
U1 
snRNP, 
the 
U2 
snRNP 
joins 
the 
assembly 
pathway 
and 
denes 
the 
branchpoint 
region. 
Maturation 
of 
the 
17S 
U2 
snRNP 
was 
proposed 
to 
proceed 
via 
two 
dierent 
actions 
of 
the 
putative 
DExD/H 
box 
helicase 
SF3b125 
(182). 
This 
enzyme 
can 
act 
at 
an 
early 
stage 
of 
17S 
U2 
formation 
by 
catalyzing 
a 
conformational 
change 
when 
SF3b 
is 
integrated 
into 
the 
12S 
U2 
snRNP 
to 
form 
the 
intermediate 
15S 
U2 
snRNP 
subcomplex 
(t13.17S 
U2 
matur2 
). 
Alternatively, 
SF3b125 
may 
act 
subsequent 
to 
the 
binding 
of 
SF3b, 
in 
this 
way 
supporting 
the 
conformational 
rearrangement 
to 
integrate 
the 
SF3a 
subcomplex 
into 
the 
U2 
snRNP 
(t22.17S 
U2 
matur1 
). 
Experimental 
evidence 
suggests 
that 
this 
putative 
enzyme 
is 
largely 
dissociating 
during 
17S 
U2 
assembly 
(182). 
Hence, 
at 
least 
one 
of 
the 
alternative 
reactions 
was 
modeled 
to 
set 
SF3b125 
free 
from 
the 
U2 
snRNP 
maturation 
subpathway. 
Due 
to 
the 
two 
dierent 
U2 
snRNP 
maturation 
scenarios, 
the 
number 
of 
T-invariants 
is 
doubled 
for 
all 
subsystems, 
which 
require 
the 
presence 
of 
a 
mature 
U2 
snRNP, 
thus 
demonstrating 
the 
emergence 
of 
combinatorial 
complexity 
in 
the 
modeled 
system. 
A 
similar 
property 
of 
the 
network 
can 
be 
observed 
during 
late 
spliceosome 
assembly, 
where 
a 
branching 
of 
the 
pathway 
was 
modeled 
according 
to 
the 
proposed 
function 
of 
the 
Prp16 
DExD/H 
box 
helicase. 
Although 
the 
model 
does 
not 
re
ect 
the 
kinetic 
behavior 
of 
Prp16 
in 
detail, 
the 
eect 
of 
two 
dierent 
possible 
kinetics 
can 
be 
described. 
The 
proper 
kinetics 
of 
Prp16 
requires 
a 
proper 
substrate 
of 
pre-mRNA 
and 
snRNP 
conformations 
and 
may 
channel 
spliceosome 
assembly 
into 
a 
productive 
pathway 
of 
C-complex 
assembly, 
such 
that 
the 
second 
step 
of 
splicing 
and 
exon 
ligation 
can 
proceed 
(via 
t101.Prp16 
remod 
step). 
In 
contrast, 
mutations 
in 
the 
involved 
RNA 
species 
or 
unfavorable 
conformations 
due 
to 
missing 
proteins, 
can 
result 
in 
a 
slowed 
Prp16 
kinetics 
which 
was 
proposed 
to 
activate 
a 
discard 
pathway 
(245, 
241) 
re
ected 
by 
transition 
t100.premature 
ATP 
hydrol. 
This 
must 
not 
necessarily 
be 
a 
degradation 
pathway 
as 
some 
of 
the 
involved 
factors 
(Spp382, 
Prp43) 
are 
also 
active 
in 
recycling 
spliceosomal 
components 
(165, 
197) 
modeled 
by 
t96.Spp382 
hPrp43 
act. 
Summarizing, 
two 
possible 
and 



Invariant 
Signaling 
Pathways 
in 
Spliceosome 
Assembly 


dierent 
major 
outcomes 
of 
spliceosome 
assembly 
are 
captured 
by 
the 
model 
and 
cause 
a 
doubling 
of 
observed 
T-invariants: 
i) 
the 
productive 
(T-invariants 
i13-i43) 
and 
ii) 
the 
unproductive 
(T-invariants 
i44-i71) 
branch 
of 
late 
spliceosome 
assembly, 
which 
are 
combined 
with 
all 
subpathways 
passing 
through 
E-and 
A-complex 
assembly. 


Conservation 
Relations 
Corresponding 
to 
P-invariants 


Compared 
to 
the 
number 
of 
T-invariants 
the 
present 
network 
structure 
generates 
four 
place 
invariants. 
A 
trivial 
P-invariant 
exists 
for 
the 
serin 
protein 
kinase 
1 
(SRPK1), 
which 
was 
modeled 
as 
a 
loop 
connected 
to 
the 
transition 
that 
describes 
the 
phosphorylation 
of 
the 
splicing 
factor 
ASF/SF2. 
In 
contrast 
to 
other 
putative 
enzymes, 
which 
act 
within 
the 
spliceosomal 
subcomplexes, 
SRPK1 
is 
active 
at 
an 
early 
stage, 
activating 
individual 
splicing 
factors. 
Thus, 
it 
is 
assumed 
not 
to 
pariticipate 
in 
further 
spliceosome 
assembly 
and 
has 
been 
modeled 
as 
available 
in 
a 
non-limited 
amount. 
Two 
other 
P-invariants 
are 
related 
to 
the 
factors 
Prp31 
and 
Prp38, 
which 
are 
present 
in 
the 
B-complex. 
Prp31 
has 
been 
shown 
to 
bind 
the 
U4 
snRNA 
and 
the 
U4/U6 
snRNA 
duplex 
in 
presence 
of 
another 
factor, 
Snu13. 
Hence, 
Prp31 
enters 
spliceosomal 
assembly 
at 
least 
in 
the 
stage 
of 
U4/U6 
subcomplex 
formation 
(246, 
189). 
Furthermore 
it 
was 
shown 
that 
Prp31 
is 
destabilized 
at 
the 
time 
of 
catalytic 
activation 
of 
the 
spliceosome. 
Thus, 
it 
was 
modeled 
to 
leave 
the 
spliceosomal 
main 
complex 
with 
the 
reaction 
t.68.B 
complex 
act. 
This 
results 
in 
seven 
places, 
describing 
the 
tri-snRNP 
and 
B-complex 
formation, 
which 
form 
a 
P-invariant 
for 
the 
system 
conservation 
of 
Prp31. 
The 
fact 
that 
Prp31 
is 
required 
for 
successive 
rounds 
of 
tri-snRNP 
and 
spliceosome 
formation 
suggests 
this 
protein 
to 
be 
abundantly 
available, 
which 
in 
turn 
justies 
a 
conservation 
relation. 
Prp31 
is 
furthermore 
a 
crucial 
factor 
in 
spliceosome 
assembly 
because 
mutations 
in 
its 
gene 
are 
related 
to 
the 
blindness 
causing 
disease 
retinitis 
pigmentosa 
(189). 
Since 
all 
T-invariants, 
which 
involve 
the 
reaction 
of 
U4/U6 
snRNP 
association 
(t47.U4 
U6 
bdg, 
79% 
of 
all 
T-invariants) 
depend 
on 
the 
presence 
of 
Prp31, 
the 
model 
suggests 
that 
more 
than 
three 
quarter 
of 
the 
network 
would 
fail 
if 
this 
protein 
would 
be 
knocked 
out. 
Prp38 
(yeast 
ortholog 
of 
human 
protein 
27K) 
forms 
a 
similar 
albeit 
smaller 
P-invariant 
of 
ve 
places, 
which 
except 
for 
Prp38, 
is 
itself 
a 
complete 
subset 
of 
the 
Prp31 
P-invariant. 
The 
time 
of 
appearance 
and 
release 
of 
Prp38 
is 
less 
clear 
but 
it 
was 
shown 
to 
associate 
with 
higher 
anity 
(and 
thus 
stability) 



Results 
99 


with 
the 
assembled 
U4/U6.U5 
tri-snRNP 
than 
with 
an 
individual 
U 
snRNP 
(247). 
Hence, 
it 
was 
modeled 
to 
enter 
tri-snRNP 
formation 
at 
the 
time 
of 
U5 
snRNP 
integration. 
Its 
involvement 
in 
structural 
rearrangement 
of 
the 
U4/U6 
complex 
without 
possessing 
a 
DExD/H 
domain 
to 
actively 
participate 
in 
the 
required 
hydrolyzation 
reactions, 
makes 
it 
a 
putative 
auxiliary 
factor 
for 
the 
DExD/H 
box 
helicase 
Prp28 
(247, 
248). 
The 
possible 
connection 
between 
Prp38 
and 
the 
helicase 
Prp28, 
which 
catalyzes 
the 
unwinding 
of 
the 
U4/U6 
snRNA 
duplex 
upon 
U2 
snRNP 
integration 
(247), 
implies 
that 
both 
proteins 
exit 
from 
the 
active 
assembly 
pathway 
after 
this 
step. 
In 
this 
way, 
Prp38 
might 
as 
well 
as 
Prp31 
form 
a 
conservation 
relation 
at 
the 
stage 
of 
B-complex 
formation. 


The 
fourth 
P-invariant 
denes 
the 
cycling 
of 
the 
DExD/H 
box 
helicase 
Prp16, 
which 
is 
a 
crucial 
determinant 
of 
the 
second 
catalytic 
step, 
by 
catalyzing 
the 
initial 
conformational 
changes 
in 
transition 
from 
rst 
to 
second 
catalytic 
step 
(165). 
Prp16 
binds 
transiently, 
being 
no 
integral 
snRNP 
component, 
and 
leaves 
the 
spliceosome 
upon 
ATP 
hydrolyzation 
(249). 
The 
Prp16 
P-invariant 
consists 
only 
of 
three 
places 
(including 
the 
free 
protein 
Prp16), 
denoting 
the 
intermediate 
complexes 
p90.U2 
5ss 
U6 
U5 
conf1 
and 
p100.U2 
5ss 
U6 
U5 
conf2, 
in 
which 
Prp16 
unfolds 
its 
catalytic 
activity. 
In 
contrast 
to 
other 
helicases 
involved 
in 
structural 
rearrangements 
(e.g., 
Prp28), 
this 
enzyme 
occurs 
not 
in 
dierent 
branches 
of 
the 
network, 
also 
explaining 
its 
appearance 
in 
a 
conservation 
relation. 


In 
general, 
the 
appearance 
of 
essential 
enzymes 
in 
conservation 
relations 
can 
be 
meaningful 
to 
re
ect 
their 
availability 
for 
subsequent 
rounds 
of 
spliceosome 
assembly, 
which 
may 
require 
a 
constant 
presence 
proximal 
to 
the 
location 
of 
spliceosome 
formation. 
The 
identied 
P-invariants 
suggests 
that 
more 
spliceosomal 
factors 
exists 
whose 
relative 
concentration 
do 
not 
change 
markedly 
via 
repeated 
rounds 
of 
spliceosome 
assembly. 
Lack 
of 
evidence 
at 
which 
time 
other 
catalytically 
active 
proteins 
specically 
(re)enter 
the 
assembly 
process 
or 
how 
long 
they 
remain 
associated 
with 
the 
main 
complex, 
presently 
limit 
the 
modeling 
of 
further 
P-invariants. 
In 
contrast 
to 
metabolic 
networks, 
where 
commonly 
only 
low 
molecular 
substances 
appear 
in 
conservation 
relations, 
here 
also 
enzymes 
or 
intermediate 
complexes 
can 
be 
conserved 
within 
a 
dened 
signaling 
network. 



Decomposition 
of 
the 
Spliceosomal 
Network 
into 
Functional 
Units 


3.3.4 
Decomposition 
of 
the 
Spliceosomal 
Network 
into 
Functional 
Units 
Analysis 
of 
Maximal 
Common 
Transition 
Sets 


Maximal 
common 
transition 
sets 
(MCTS) 
have 
been 
dened 
as 
a 
more 
generalized 
concept 
of 
enzyme 
subsets, 
which 
dene 
enzymes 
in 
a 
biochemical 
network, 
that 
operate 
under 
steady 
state 
conditions 
always 
together, 
in 
one 
or 
several 
metabolic 

uxes. 
Enzyme 
subsets 
further 
require 
that 
the 
enzymes 
involved 
are 
all 
regulated 
in 
the 
same 
direction 
and 
that 
their 

uxes 
behave 
proportional 
(250). 
MCTS, 
in 
contrast, 
relax 
the 
constraints 
imposed 
on 
mutually 
occurring 
sets 
of 
reactions 
to 
some 
extent. 
Due 
to 
missing 
stoichiometric 
coecients 
the 
constraint 
of 
proportionality 
does 
not 
apply. 
They 
describe 
sets 
of 
reactions 
that 
are 
in 
a 
maximal 
number 
of 
T-invariants 
(signaling 

uxes\) 
present, 
hence 
being 
shared 


"
by 
dierent 
signaling 
pathways. 
Given 
the 
correctness 
or 
biological 
plausibility 
of 
T-invariants, 
MCTS 
emphasize 
key 
parts 
of 
signaling 
routes 
and 
facilitate 
the 
description 
of 
functional 
parts 
of 
a 
network. 
Vice 
versa, 
they 
can 
be 
indicative 
of 
modeling 

aws 
if 
they 
combine 
transitions 
without 
proven 
biological 
relationship. 


Table 
3.1 
shows 
the 
MCTS 
computed 
on 
the 
set 
of 
all 
71 
T-invariants. 
There 
exist 
six 
smaller 
MCTS 
composed 
of 
only 
two 
transitions, 
which 
nevertheless 
represent 
crucial 
elements 
of 
the 
assembly 
pathway. 
MCTS 
1 
(t0.U2AF35 
3ss 
bdg, 
t1.3ss 
in) 
describes 
the 
recognition 
of 
the 
30ss 
by 
the 
factor 
U2AF35 
which 
occurs 
in 
more 
than 
56% 
of 
all 
T-invariants. 
Next 
frequently, 
MCTS 
7 
is 
formed 
by 
two 
reactions 
shared 
by 
32 
T-invariants 
(45%), 
which 
describe 
the 
in
ux 
of 
the 
bridging 
factor 
FBP11 
(t15.FBP11 
in) 
and 
subsequent 
binding 
of 
the 
U2 
snRNP 
to 
the 
branch 
site 
t24.U2 
BPS 
bdg1 
). 
Taken 
together, 
MCTS 
4 
and 
MCTS 
7 
form 
a 
module 
that 
covers 
the 
FBP11 
supported 
interaction 
of 
U1 
snRNP 
with 
the 
branchpoint 
bound 
factor 
SF1 
and 
the 
subsequent 
joining 
and 
structural 
rearrangement 
of 
U2 
snRNP 
with 
replacement 
of 
U2AF 
at 
the 
polypyrimidine 
tract. 
The 
remaining 
small 
MCTS 
occur 
still 
in 
more 
than 
one 
quarter 
of 
all 
invariant 
pathways 
and 
cover 
the 
NTC-complex 
and 
Prp19 
integration 
(MCTS 
16), 
the 
late 
SF3b125 
action 
in 
17S 
U2 
maturation 
(MCTS 
9), 
and 
the 
slowed 
ATP 
hydrolysis 
by 
Prp16 
with 
initiation 
of 
the 
discard 
pathway 
(MCTS 
18). 


The 
largest 
set 
of 
shared 
transitions 
(MCTS 
2) 
covers 
54/161 
(33%) 
of 
all 
transitions, 
occurring 
in 
more 
than 
three 
quarter 
of 
all 
T-invariants. 
This 
MCTS 



Results 
101 


is 
composed 
of 
several 
building 
blocks, 
which 
dene 
biological 
functions 
at 
essential 
stages 
of 
spliceosome 
assembly, 
for 
example, 
branchpoint 
denition, 
15S 
U2 
snRNP 
assembly, 
SF3a 
and 
SF3b 
subcomplex 
formation, 
U5-and 
U6 
snRNP 
maturation 
and 
the 
cyclophilin 
trimer 
formation. 
The 
energy 
supply 
by 
ATP 
and 
removal 
of 
ADP 
is 
part 
of 
MCTS 
2, 
which 
naturally 
has 
to 
be 
shared 
by 
many 
T-invariants 
as 
each 
stage 
requires 
ATP 
either 
for 
phosphorylation 
or 
hydrolyzation 
reactions. 
The 
maturation 
and 
recycling 
of 
the 
U1 
and 
U4 
snRNP 
is 
described 
by 
individual 
MCTS 
(11 
and 
13), 
the 
former 
consisting 
of 
reactions, 
which 
are 
shared 
by 
almost 
three 
quarter 
(73%) 
of 
all 
T-invariants. 
All 
MCTS 
further 
validate 
the 
model 
network, 
capturing 
crucial 
parts 
of 
the 
U1 
and 
U2 
snRNP 
maturation, 
U1 
independent 
5. 
ss 
recognition 
and 
the 
Prp16 
involved 
discard 
pathway 
in 
spliceosome 
assembly. 
A 
more 
detailed 
PN 
model 
of 
U1 
snRNP 
assembly 
was 
recently 
published 
(226). 



Decomposition 
of 
the 
Spliceosomal 
Network 
into 
Functional 
Units 


  
3.10: 
The 
complete 
network 
of 
spliceosome 
assembly 
modeled 
as 
transition 
bounded 
Petri 
net. 
The 
dierent 
stages 
are 
labeled 
with 
capital 
letters: 
E 
= 
E-complex, 
A 
= 
A-complex, 
B 
= 
B-complex, 
TRI 
= 
tri-snRNP 
complex, 
C 
= 
C-complex 
and 
R 
= 
recycling 
pathways. 
Places 
are 
colored 
according 
to 
dierent 
functions: 
orange 
= 
proteins 
with 
RS 
domains; 
blue 
= 
RNA; 
magenta 
= 
DExD/H 
box 
proteins 
functioning 
as 
ATP 
dependent 
RNA 
unwindases\; 
gray 
or 
hatched 
= 
logical 
places, 
indicating 
equal 
places 


"

occurring 
more 
than 
once 
in 
the 
gure, 
but 
not 
in 
the 
underlying 
graph 
structure 
of 
the 
PN. 
Transitions 
represented 
by 
two 
squares 
introduce 
hierarchical 
nodes, 
which 
connect 
to 
further 
reactions 
at 
a 
lower 
network 
level 
(see 
Figure 
3.12) 



Results 
103 


 
Figure 
3.11: 
Network 
of 
spliceosome 
assembly. 
The 
red 
T-invariant 
transitions 
belong 
to 
a 
possible 
scenario 
of 
E-complex 
assembly, 
which 
is 
part 
of 
dierent 
concurring 
redundant 
subpathways. 
Here, 
the 
5. 
splice 
site 
is 
recognized 
via 
an 
U1 
snRNP 
independent 
pathway 



Decomposition 
of 
the 
Spliceosomal 
Network 
into 
Functional 
Units 


ADPATPSF3b125SF3b49SF3b14aSF3b14bSF3b10SF3b155SF3b130SF3b145SF3bSF3b10_inSF3b14a_inSF3b125_inSF3b155_inSF3b_dimSF3b49_inSF3b130_inSF3b145_inSF3b14b_inaCypH_trimerPrp3P3P4_dimPrp4CypHCypH_P3P4dim_bdgPrp3_Prp4_bdgCypH_inPrp4_inbcSF3a60SF3aSF3a66SF3a120SF3a_dimSF3a66_inSF3a120_indefghijk
Figure 
3.12: 
(a-k) 
Subhierarchy 
levels 
of 
the 
spliceosomal 
assembly 
network. 
Red 
highl
ighted 
transitions 
showing 
exemplarily 
T-invariant 
13, 
which 
describes 
one 
possible 
scen
ario 
of 
E-complex 
assembly 
as 
shown 
in 
Figure 
3.11 



Tabl
e 
3.1
: 
Maxima
l 
commo
n 
transitio
n 
set
s 
(MCTS
) 
a
s 
determine
d 
fro
m 
th
e 
7
1 
T-invariant
s 
tha
t 
cove
r 
th
e 
network
Eac
MCT
comprise
reaction
s 
tha
t 
ar
e 
exclusivel
y 
share
d 
b
y 
severa
l 
T-invariant
s 
an
d 
henc
e 
describ
e 
frequentl
y 
use
d 
route
s 
throug
h 
th
e 
spliceosoma
assembl
network


MCT
S 
Transition
s 
T-Invariant
s 


IDID
s 
# 
% 


ID
s 
#
% 


Biologica
l 
Interpretatio


1 
t0, 
t1 
2 
2 
t3, 
t7, 
t11, 
t18-t21, 
t26, 
t42, 
54 
t44-t54, 
t68-t72, 
t86, 
t87, 
t93, 
t96-t98, 
t104-t106, 
t110, 
t111, 
t113, 
t114, 
t118-t129, 
t144, 
t145, 
t150, 
t151 
3 
t4-t6 
3 
4 
t8, 
t23, 
t57, 
t158, 
t159 
5 
5 
t9, 
t12, 
t141-t143 
5 
6 
t10, 
t137-t139 
4 
7 
t15, 
t24 
2 
8 
t16, 
t31, 
t32 
3 
9 
t22, 
t109 
2 
10 
t25, 
t27-t29, 
t55 
5 
11 
t33, 
t34, 
t133-t136 
6 
12 
t35, 
t56, 
t152, 
t155 
4 


1.24 


33.54 


1.86 


3.11 


3.11 


2.48 


1.24 


1.86 


1.24 


3.11 


3.73 


2.48 


i13-i18, 
i23-i26, 
i29-i32, 
i35-i40, 
i47, 
i50-i53, 
i56-i59, 
i64-i71 
i13-i40, 
i44-i71 
i4440 
56 
i13-i18, 
i21-i26, 
i29-i32, 
i35-i40, 
i47, 
i50-i53, 
i56-i61, 
i64-i71 
i13-i18, 
i23-i26, 
i29-i32, 
i35-i38, 
i47, 
i50-i53, 
i56-i59, 
i64-i69 
i15-i20, 
i62-i67 
i44i4444 
36 
12 
i21-i26, 
i56-i61 
12 
i15-i18, 
i23-i26, 
i29-i32, 
i35-i38, 
i47, 
i50-i53, 
i56-i59, 
i64-i67 
i13, 
i14, 
i68, 
i69 
i4432 
4 
i14, 
i16, 
i18, 
i20, 
i22, 
i24, 
i26, 
i28, 
i30, 
i32, 
i34, 
i36, 
i38, 
i40, 
i45, 
i47, 
i49, 
i51, 
i53, 
i55, 
i57, 
i59, 
i61, 
i63, 
i65, 
i67, 
i69, 
i71 
i39, 
i40, 
i70, 
i71 
28 
4 
i15-i40, 
i44-i67, 
i70, 
i71 
i19-i22, 
i27, 
i28, 
i33, 
i34, 
i48, 
i49, 
i54, 
i55, 
i60-i63 
52 
16 


56.34 


78.87 


61.97 


50.70 


16.90 


16.90 


45.07 


5.63 


39.44 


5.63 


73.24 


22.54 


Recognition 
of 
30ss 
by 
U2AF35 


Branch 
point 
denition, 
15S 
U2 
snRNP 
assembly, 
SF3a 
formation, 
partial 
SF3b 
formation, 
U5-and 
U6maturation, 
CypH 
trimer 
formation, 
ATP-in-ADP-eux 


U2AF 
dimerization 


Reactions 
of 
U2 
snRNP 
remodeling 
variant 
1, 
involving 
hPrp43 
and 
UAP56 
ASF 
phosphorylation 
and 
50ss 
denition 
via 
ASF/U1 
snRNP/TIA1 
interactions 
5
. 
Terminal 
50ss 
denition 
via 
U1 
snRNP 
interactions 
with 
cap 
binding 
complex 
FBP11 
dependent 
U2 
snRNP 
binding 
to 
the 
branch 
point 


U1 
snRNP 
independent 
50ss 
denition 
and 
complex 
formation 
SF3b125 
dependent 
17S 
U2 
snRNP 
maturation 


U1/U2 
snRNP 
bridging 
by 
Prp5 
and 
simultaneous 
binding 
to 
50ss 
and 
branch 
point 
U1 
snRNP 
maturation 
and 
recycling 
U2AF 
independent 
complex 
formation 


continued 
next 
page 


Results 
105 



Tabl
e 
3.1
: 
Maxima
l 
commo
n 
transitio
n 
set
s 
(MCTS
) 
a
s 
determine
d 
fro
m 
th
e 
7
1 
T-invariant
s 
tha
t 
cove
r 
th
e 
network
Eac
MCT
comprise
reaction
s 
tha
t 
ar
e 
exclusivel
y 
share
d 
b
y 
severa
l 
T-invariant
s 
an
d 
henc
e 
describ
e 
frequentl
y 
use
d 
route
s 
throug
h 
th
e 
spliceosoma
assembl
network


MCT
S 
Transition
s 
T-Invariant
s 


IDID
s 
# 
% 


ID
s 
#
% 


Biologica
l 
Interpretatio


13 
t36, 
t146, 
t147, 
t149 
4 
2.48 
14 
t58, 
t59 
2 
1.24 


15 
t60-t63 
4 
2.48 
16 
t65, 
t66 
2 
1.24 
17 
t76, 
t77, 
t81, 
t82, 
t84, 
t85, 
t88-15 
9.32 


t92, 
t94, 
t95, 
t101, 
t160 


18 
t99, 
t100 
2 
1.24 


19 
t130-t132 
3 
1.86 


i7 
1 
i15, 
i16, 
i25, 
i26, 
i31, 
i32, 
i37, 
i38, 
i44, 
16 
i45, 
i50, 
i51, 
i56, 
i57, 
i66, 
i67 
i3 
1 
i41, 
i44-i71 
29 
i13-i40 
28 


i44-i71 
28 


i15-i20, 
i27-i32, 
i50-i55, 
i62-i67 
24 


1.41 


22.54 


1.41 


40.85 


39.44 


39.44 


33.80 


U4 
snRNP 
maturation 
and 
recycling 
FBP11 
supported 
U1 
snRNP/SF1 
binding 
and 
subsequent 
interaction 
with 
U2AF 
bound 
PPT-30ss 
PTB 
inhibitory 
pathway 
(without 
t2.PP
in
NTC-complex 
formation 
and 
stable 
Prp19 
integration 
Prp16 
dependent 
remodeling 
of 
C-complex, 
2nd 
catalytic 
step 
of 
splicing, 
release 
of 
ligated 
exons 
and 
disassembly 
of 
postspliceosomal 
complex 
Prp16 
induced 
and 
slowed 
ATP 
hydrolysis 
and 
commitment 
of 
C-complex 
to 
discard 
pathway, 
disassembly 
supported 
by 
hPrp43 
TIA1 
intron 
binding 


Decomposition 
of 
the 
Spliceosomal 
Network 
into 
Functional 
Units 


-127 
78.86 


-


Results 
107 


Clustering 
of 
T-invariants 
and 
MCTS 


The 
computed 
T-invariants 
(cf. 
Table 
C.3) 
were 
aligned 
to 
determine 
the 
individual 
distance 
between 
the 
signaling 
pathways. 
Similar 
to 
sequence 
alignments 
the 
multiple 
comparisons 
among 
all 
T-invariants 
can 
be 
used 
to 
built 
a 
distance 
matrix 
based 
on 
which 
a 
clustering 
can 
be 
performed. 
Clusters 
re
ect 
groups 
of 
signaling 
pathways, 
which 
share 
a 
given 
percentage 
of 
reactions. 
Here, 
a 
threshold 
of 
80% 
was 
chosen 
to 
merge 
T-invariants 
with 
less 
than 
20% 
dierence 
into 
the 
same 
subtree. 
For 
example, 
the 
T-invariants 
i13 
and 
i14 
show 
a 
dierence 
in 
four 
reactions 
in 
a 
total 
pathway 
length 
of 
92 
reactions, 
i.e. 
the 
transition 
t13.17S 
U2 
matur2 
is 
missed 
in 
i14 
and 
t12.ASFp 
U170K 
bdg, 
t22.17S 
U2 
matur1, 
t109.SF3b125 
in 
are 
missed 
in 
i13, 
which 
makes 
both 
invariant 
to 
96% 
similar 
(cf. 
Equation 
3.9). 
Comparing 
dierent 
subclusters 
helps 
to 
identify 
those 
reactions, 
which 
distinguish 
the 
T-invariants 
and 
which 
re
ect 
dierent 
functions 
in 
dierent 
stages 
of 
spliceosome 
assembly. 


The 
cluster 
representation 
depicts 
all 
trivial 
T-invariants 
in 
one 
group 
in 
the 
lower 
part 
of 
the 
tree 
(Figure 
3.13 
C1-C13, 
C22, 
C23), 
which 
is 
reasonable 
since 
they 
share 
maximal 
two 
transitions 
with 
the 
remaining 
T-invariants 
(cluster 
I). 
Also 
three 
short 
T-invariants, 
which 
describe 
the 
NTC-complex 
formation, 
the 
subpathway 
of 
U4 
snRNP 
maturation 
and 
the 
PTB 
inhibition 
pathway 
group 
separately, 
re
ecting 
subpathways, 
which 
are 
not 
shared 
by 
other 
signaling 

uxes. 
In 
contrast 
to 
the 
out-group, 
cluster 
I 
combines 
all 
T-invariants 
of 
at 
least 
four 
reactions. 
Subclusters 
can 
contain 
complete 
or 
partial 
MCTS. 
For 
example, 
cluster 
C17 
and 
C18 
together 
constitute 
MCTS 
10, 
which 
is 
composed 
of 
ve 
reactions, 
describing 
the 
subpathway 
of 
bridging 
the 
U1/U2 
snRNP 
by 
Prp5 
and 
occurs 
in 
four 
Tinvariants. 
In 
contrast, 
T-invariants 
i15-i20 
and 
i62-i67 
share 
also 
ve 
reactions 
involving 
ASF/SF2 
within 
MCTS 
5 
but 
are 
part 
of 
the 
two 
dierent 
major 
clusters 
I 
and 
IV. 
These 
clusters 
partition 
the 
T-invariants 
in 
two 
sets 
of 
reactions: 
one 
is 
reaching 
the 
productive 
end 
of 
the 
spliceosomal 
assembly 
pathway 
(resulting 
in 
spliced 
mRNA) 
and 
the 
other 
one 
is 
representing 
the 
discard 
pathway 
during 
C-Complex 
stage. 
This 
splitting 
can 
also 
be 
seen 
by 
visualizing 
all 
invariant 
pathways 
and 
their 
shared 
reactions 
via 
a 
color 
map 
(cf. 
Figure 
3.14). 
The 
color 
map 
representation 
was 
used 
to 
aid 
and 
accelerate 
the 
visual 
identication 
of 
groups 
of 
reactions 
that 
participate 
in 
dierent 
T-invariants 
in 
conjunction 
with 
the 
dendrogram 
(Figure 



Decomposition 
of 
the 
Spliceosomal 
Network 
into 
Functional 
Units 


SF1SF3b10Prp5SC35hDib1hPrp6SKIPSF3b14bSF3b14aSF3b49PTBinhibitionASF/SF2Prp28U4maturationNTCcomplexformationClusterIClusterIIIC19C21C17C1-C13,C22,C23C20C18C14C15C16ClusterIIClusterIVClusterVIClusterV302520151050DistanceMin.T-InvariantenClustering(Tanimoto/80%)
 
Figure 
3.13: 
The 
clustering 
of 
T-invariants, 
whereas 
trivial 
and 
small 
T-invariants 
are 
shown 
in 
the 
bottom 
of 
the 
tree. 
The 
two 
main 
clusters 
are 
colored 
in 
blue 
and 
red. 
Latin 
enumeration 
of 
clusters 
refers 
to 
the 
output 
as 
obtained 
from 
PInA 
using 
the 
Tanimoto 
distance 
measure 
and 
80% 
similarity 
cuto. 
Roman 
enumeration 
labels 
clusters 
that 
form 
larger 
groups 



Results 
109 


TransitionsT-invariantstt0tt1tt2tt3tt4tt5tt6tt7tt8tt9tt10tt11tt12tt13tt14tt15tt16tt17tt18tt19tt20tt21tt22tt23tt24tt25tt26tt27tt28tt29tt30tt3
tt32tt33tt34tt35tt36tt37tt38tt39tt40tt41tt42tt43tt44tt45tt46tt47tt48tt49tt50tt51tt52tt53tt54tt55tt56tt57tt58tt59tt60tt61tt62tt6
tt64tt65tt66tt67tt68tt69tt70tt71tt72tt73tt74tt75tt76tt77tt78tt79tt80tt81tt82tt83tt84tt85tt86tt87tt88tt89tt90tt91tt92tt93tt94tt9
tt96tt97tt98tt99tt100tt101tt102tt103tt104tt105tt106tt107tt108tt109tt110tt111tt112tt113tt114tt115tt116tt117tt118tt119tt120tt121t122tt123tt124tt125tt126tt127tt128tt129tt130tt131tt132tt133tt134tt135tt136tt137tt138tt139tt140tt141tt142tt143tt144tt145tt146tt14
tt148tt149tt150tt151tt152tt153tt154tt155tt156tt157tt158tt159tt160i4i5i6i2i43i42i11i10i9i8i3i12i1i7i41i21i22i19i20i27i28i33i34i1
i14i15i16i17i18i31i32i29i30i25i26i23i24i37i38i35i36i39i40i70i71i60i61i62i63i54i55i48i49i68i69i66i67i64i65i56i57i58i59i50i51i52i3i44i45i46i47t103t107,t108t112t115
Figure 
3.14: 
Color 
map 
(see 
methods, 
page 
84) 
to 
visualize, 
which 
transitions 
occur 
within 
similar 
T-invariants 
through 
the 
network 
of 
spliceosome 
assembly. 
The 
similarity 
can 
be 
compared 
via 
the 
dendrogram 
at 
top 
off 
the 
gure, 
which 
is 
the 
same 
as 
shown 
in 
Figure 
3.13. 
Dark 
or 
light 
blue 
colors 
indicate 
presence 
or 
absence 
of 
one 
or 
several 
reactions 
respectively, 
among 
the 
total 
set 
of 
modeled 
reactions 
within 
one 
or 
several 
Ti
nvariants. 
The 
red 
dashed 
rectangles 
show 
an 
example 
of 
groups 
of 
T-invariants, 
which 
specically 
lack 
the 
reactions 
indicated 
at 
the 
right 
side. 
It 
is 
notable 
that 
these 
groups 
of 
T-invariant 
appear 
in 
dierent 
clusters 
and 
their 
common 
features 
is 
easier 
detectable 
via 
the 
color 
map 
compared 
to 
the 
dendrogram. 



Decomposition 
of 
the 
Spliceosomal 
Network 
into 
Functional 
Units 


3.13). 
It 
is 
thought 
as 
proposal 
to 
introduce 
a 
compact 
representation 
of 
the 
network 
structure 
to 
facilitate 
the 
interpretation 
of 
dierences 
between 
signaling 
pathways. 
In 
this 
case, 
darker 
colors 
symbolize 
the 
presence 
of 
an 
reaction 
within 
a 
T-invariant 
in 
vertical 
direction, 
but 
participation 
in 
several 
T-invariants 
in 
horizontal 
direction. 
Bright 
colors 
mean 
that 
a 
reaction 
is 
not 
present 
in 
T-invariants 
and 
consequently 
also 
not 
in 
MCTS. 
This 
representation 
suggests 
to 
extend 
the 
analysis 
of 
MCTS 
also 
for 
sets 
of 
reactions, 
which 
form 
excluded 
subsets 
specically 
missing 
in 
certain 
MCTS. 
For 
example, 
in 
Figure 
3.14 
one 
can 
easily 
recognize 
two 
brighter 
colored 
horizontal 
areas 
within 
the 
line 
of 
transition 
t103.ASF 
SF2 
out, 
which 
stretches 
exactly 
over 
the 
columns 
that 
contain 
the 
T-invariants 
of 
MCTS 
5 
(cf. 
dashed 
rectangle 
in 
Figure 
3.14). 
This 
means, 
transition 
t103ASF 
SF2 
out 
is 
specically 
not 
involved 
in 
the 
T-invariants 
i15-i20 
and 
i62-i67. 


Further 
analysis 
of 
line 
t103.ASF 
SF2 
out 
within 
the 
color 
map 
reveals 
that 
it 
is 
present 
in 
almost 
all 
other 
T-invariants, 
as 
expectedly 
in 
i12, 
which 
poses 
the 
trivial 
in
ux 
and 
eux 
of 
ASF/SF2. 
For 
example, 
two 
closely 
related 
T-invariants, 
using 
this 
reactions 
are 
i13 
and 
i14. 
They 
describe 
the 
U1 
snRNP 
independent 
50ss 
denition, 
which 
again 
can 
occur 
in 
the 
two 
variants 
generated 
by 
the 
alternative 
reactions 
22.17S 
U2 
matur1 
(i14) 
and 
t13.17S 
U2 
matur2 
(i13). 
Interestingly, 
the 
T-invariants 
i13 
and 
i14 
do 
not 
contain 
the 
ASF/SF2 
in
ux 
reaction 
(140.ASF 
SF2 
in), 
in 
contrast 
to 
its 
eux 
reaction 
(103.ASF 
SF2 
out), 
which 
is 
only 
missing 
in 
MCTS 
5. 
This 
suggests 
that 
the 
pathways 
involved 
in 
MCTS 
5 
describe 
a 
state 
in 
which 
the 
spliceosome 
has 
entered 
a 
repetition 
cycle, 
where 
many 
factors 
are 
already 
present 
for 
recurring 
assembly. 
Thus, 
it 
is 
conceivable 
that 
U1 
snRNP 
is 
initially 
available, 
but 
dispensable 
in 
subsequent 
rounds 
of 
assembly 
when 
a 
critical 
amount 
of 
SR 
proteins 
(e.g., 
ASF/SF2) 
are 
present 
to 
enable 
U1 
snRNP 
independent 
50ss 
denition. 
It 
was 
previously 
shown 
that 
SR 
proteins 
are 
perfectly 
capable 
to 
organize 
the 
cross 
talk 
between 
50ss 
and 
branchpoint/30ss 
by 
interacting 
via 
their 
SR 
domains 
(20), 
which 
is 
re
ected 
in 
the 
PN 
model. 


Another 
observation 
that 
could 
be 
made 
from 
the 
color 
map 
concerns 
the 
absence 
of 
four 
reactions, 
t107.SF3b10 
in, 
t108.SF3b14a 
in, 
t112.SF3b49 
in 
and 
t115.SF3b14b 
in 
in, 
from 
a 
large 
body 
of 
T-invariants 
(i13-i38), 
independent 
of 



Discussion 
111 


the 
trivial 
T-invariants. 
These 
reactions 
model 
the 
in
ux 
of 
four 
SF3b 
specic 
factors. 
They 
were 
modeled 
as 
logical 
places, 
each 
with 
a 
specied 
eux 
reaction 
prior 
to 
the 
C-complex 
stage, 
because 
experiments 
failed 
to 
detect 
these 
factors 
within 
spliceosomal 
C-complexes 
(151) 
(supplementary 
material). 
The 
T-invariants 
i13-i38 
describe 
the 
signaling 
pathways 
that 
reach 
the 
nal 
stage 
of 
spliceosome 
assembly. 
Hence, 
these 
routes 
pass 
the 
stage, 
where 
the 
SF3b 
factors 
are 
leaving 
the 
active 
assembly 
process. 
Among 
others, 
the 
specied 
SF3b 
factors 
are 
required 
for 
subsequent 
rounds 
of 
spliceosome 
assembly, 
thus 
the 
absence 
of 
their 
in
ux 
reactions 
from 
T-invariants 
i13-i38 
can 
be 
interpreted 
as 
a 
way 
to 
remain 
within 
range 
of 
the 
spliceosome 
assembly 
site. 
A 
dierent 
scenario 
occurs 
for 
the 
T-invariants 
that 
enter 
the 
discard 
pathway, 
which 
is 
triggered 
before 
C-complex 
formation. 
Here, 
no 
explicit 
eux 
reactions 
could 
be 
adapted 
from 
literature 
for 
the 
SF3b 
factors, 
hence 
their 
in
ux 
reaction 
is 
part 
of 
the 
Tinvariants 
i44-i71 
(cf. 
Figure 
3.14). 
This 
raises 
the 
question, 
what 
happens 
with 
these 
factors 
and 
when 
if 
the 
discard 
pathway 
is 
activated 
during 
spliceosome 
assembly. 


3.4 
Discussion 
The 
present 
work 
describes 
a 
PN 
model, 
which 
combines 
dierent 
scenarios 
of 
spliceosome 
assembly 
over 
the 
basic 
assembly 
stages 
of 
this 
multi 
protein 
complex. 
The 
spliceosome 
is 
a 
nuclear 
macrocomplex, 
which 
is 
newly 
built 
after 
or 
as 
soon 
as 
an 
precursor 
RNA 
emerges 
from 
the 
Polymerase 
II 
transcription 
complex. 
The 
assembly 
process 
involves 
biochemical 
reactions, 
which 
can 
be 
distinguished 
in 
enzymatic 
and 
association 
reactions. 
Unlike 
in 
metabolic 
networks, 
which 
commonly 
model 
the 
conversion 
of 
low 
molecular 
compounds 
to 
produce 
energy 
or 
target 
metabolites, 
e.g. 
aminoacids 
(220, 
251), 
the 
spliceosome 
assembly 
involves 
many 
enzymatic 
reactions, 
which 
act 
on 
double 
stranded 
RNAs 
as 
substrate 
and 
proteins 
or 
NTPs 
as 
co-factors 
(165, 
198). 
This 
is 
due 
to 
the 
snRNA 
containing 
core 
components 
of 
the 
spliceosome, 
which 
interact 
via 
multiple 
RNA-RNA 
contacts 
making 
it 
necessary 
to 
re-open 
intermediate 
conformations 
at 
several 
stages 
during 
the 
assembly 
process. 
Additionally, 
phosphorylation 
reactions 
as 
known 
from 
signal 
transduction 
networks 
(252) 
assure 
the 
specicity 
and 
localization 
of 
splicing 
factors. 
Consequently, 
the 
model 
presented 
here, 
consists 
of 
several 
types 
of 
molecules 
(RNA, 
proteins 
and 
compounds 
of 
both) 
and 
reactions, 
which 
have 
been 
designed 
and 
tested 
individually 
(cf. 
Chapter 
3.3.1) 
prior 
to 
the 
setup 
of 



112 
Discussion 


the 
complete 
network. 
Hundreds 
of 
individual 
studies 
have 
investigated 
components 
of 
the 
spliceosome 
or 
individual 
biochemical 
reactions. 
The 
knowledge 
of 
almost 
two 
decades 
lab 
work 
is 
available 
but 
needs 
to 
be 
translated 
into 
a 
machine 
readable 
and 
human 
comprehensible 
language. 
Thus, 
one 
of 
the 
main 
purposes 
of 
this 
work 
is 
to 
channel 
biochemical 
knowledge 
about 
the 
spliceosome 
into 
a 
formalized 
description, 
suitable 
for 
computational 
analysis. 
Among 
the 
major 
diculties 
is 
the 
handling 
of 
non-standardized 
identiers 
of 
the 
involved 
proteins, 
which 
exacerbates 
the 
combination 
of 
smaller 
models, 
initially 
devised 
from 
indivial 
reports 
and 
successively 
combined 
into 
a 
larger 
network 
of 
interactions. 
Thus, 
the 
power 
of 
predictive 
modeling 
will 
increase 
as 
more 
submodels 
become 
integrated, 
covering 
more 
details 
of 
the 
spliceosome 
assembly 
pathway. 


A 
network 
of 
ordered 
basic 
interactions 
was 
established, 
leading 
to 
the 
assembly 
of 
an 
active 
spliceosome 
and 
including 
also 
the 
example 
of 
a 
discard 
pathway, 
which 
was 
previously 
suggested 
(197). 
In 
total, 
about 
100 
proteins 
where 
integrated 
into 
the 
model. 
Many 
proteins, 
participating 
in 
the 
spliceosomal 
assembly 
pathway, 
are 
themselves 
alternatively 
spliced 
hence 
may 
occur 
in 
several 
isoforms. 
For 
example, 
the 
U2 
snRNP 
specic 
component 
SF3b14 
shows 
ve 
predicted 
alternative 
splice 
forms 
of 
dierent 
types 
(source 
Asd 
(53)). 
The 
interactions 
of 
SF3b14 
are 
well 
described 
(180), 
including 
the 
location 
of 
functional 
domains 
within 
the 
protein 
sequence. 
Since 
increasingly 
more 
spliceosomal 
factors 
are 
described 
in 
such 
detail, 
it 
should 
be 
possible 
in 
the 
near 
future 
to 
estimate 
the 
impact 
of 
alternative 
splicing 
on 
the 
spliceosome, 
which 
poses 
an 
interesting 
example 
of 
combinatorial 
complexity. 
Suppose 
that 
in 
four 
stages 
of 
spliceosome 
assembly 
occurs 
only 
one 
protein 
factor 
in 
two 
functional 
dierent 
isoforms, 
about 
24 
= 
16 
dierent 
spliceosomes 
could 
be 
assembled 
and 
contribute 
to 
dierent 
alternative 
splicing 
decisions 
(neglecting 
that 
some 
alternative 
splice 
forms 
do 
not 
reach 
the 
protein 
level). 
This 
is 
a 
rough 
estimate 
of 
the 
lower 
boundary 
as 
many 
more 
spliceosomal 
proteins 
exist, 
and 
most 
of 
their 
genes 
are 
likely 
to 
be 
alternatively 
spliced. 
Concerning 
the 
presented 
Petri 
Net 
modeling 
approach, 
one 
can 
summarize 
the 
following 
achievements: 


i. 
Translation 
of 
dierent 
lines 
of 
evidence 
for 
modular 
subsystems 
of 
the 
spliceosomal 
assembly 
pathway 
from 
experimental 
literature 
into 
one 
unique 
mathematical 
formalism. 

Discussion 
113 


ii. 
Compilation 
of 
minimal 
positive 
T-Invariants 
(P-invariants) 
based 
on 
the 
commonly 
applied 
steady 
state 
assumption 
for 
biochemical 
networks. 
iii. 
Model 
validation 
resulting 
in 
a 
network 
completely 
covered 
with 
Tinvariants. 
iv. 
Representation 
of 
combined 
partial 
pathways, 
each 
supported 
by 
experimental 
reports, 
allowing 
for 
model 
expansion 
and 
testing 
of 
new 
hypotheses. 
v. 
Inclusion 
of 
special 
aspects 
of 
5. 
splice 
site 
recognition 
during 
E-complex 
formation 
as 
well 
as 
the 
potential 
activation 
of 
a 
discard 
pathway 
as 
simplied 
model 
for 
a 
kinetic 
proof 
reading 
mechanism 
during 
C-complex 
formation. 
vi. 
Easier 
identication 
of 
discrepancies 
in 
current 
experimental 
data 
by 
the 
combinatorial 
arrangement 
of 
the 
subpathways. 
For 
example, 
the 
activation 
of 
UAP56 
by 
U2AF 
stands 
in 
contradiction 
to 
the 
apparent 
requirement 
of 
UAP56 
for 
transition 
from 
E-to 
A-complex 
within 
the 
U2AF 
independent 
A-complex 
assembly 
pathway. 
vii. 
Comprehensive 
and 
condensed 
visualization 
of 
the 
spliceosomal 
assembly 
process, 
allowing 
the 
global 
inspection 
of 
similar 
and 
distinct 
routes. 
This 
facilitates 
the 
apprehension 
of 
a 
large 
network 
such 
as 
the 
spliceosomal 
assembly 
pathway 
and 
its 
further 
extension. 
The 
clustering 
of 
the 
T-invariants 
representing 
signaling 
pathways 
and 
participating 
in 
spliceosome 
formation, 
indicates 
that 
there 
exists 
a 
variety 
of 
similar 
pathways 
leading 
to 
the 
same 
intermediate 
complexes. 
Although 
each 
T-
invariant, 
describing 
one 
of 
these 
routes, 
is 
minimal 
in 
that 
it 
would 
fail 
with 
the 
loss 
of 
one 
reaction, 
it 
is 
clearly 
visible 
that 
there 
exists 
a 
redundancy 
in 
routes 
leading 
to 
the 
formation 
of 
intermediate 
states. 
This 
observation 
provides 
the 
interesting 
aspect 
of 
a 
backup 
failure 
mechanism, 
ensuring 
that 
independently 
from 
alternating 
conditions 
the 
spliceosome 
reaches 
critical 
assembly 
checkpoints 
with 
dierent 
protein 
complements. 
Alternatively, 
this 
might 
extend 
our 
view 
on 
dierent 
spliceosomes 
in 
dependence 
on 
dierent 
cellular 
or 
environmental 
conditions. 
The 
existence 
of 
a 
major 
and 
a 
minor 
spliceosome 
with 
dierent 
mRNA 
substrate 
specicities 
could 
support 
this 
notion, 
but 
the 
major 
spliceosome 
as 
modeled 
here 
suggests 
for 
itself 
a 
highly 
dynamic 
assembly 
process. 
The 

ood 
of 



114 
Discussion 


dierent 
mechanistic 
examples 
of 
individual 
and 
sometimes 
quite 
dierent 
intermediate 
steps 
makes 
it 
clear 
that 
there 
is 
no 
one 
spliceosome. 
Hence, 
there 
can 
be 
no 
single 
model 
of 
spliceosome 
assembly. 
Although 
the 
current 
model 
represents 
a 
higher 
coverage 
of 
experimentally 
supported 
subsystems 
in 
the 
early 
(E-and 
A-complex) 
in 
comparison 
to 
later 
assembly 
stages, 
it 
is 
tempting 
to 
hypothesize 
that 
the 
number 
of 
dierent 
routes 
increases 
with 
the 
importance 
of 
the 
intermediate 
complex 
for 
the 
overall 
assembly 
process. 


Several 
requirements 
to 
future 
works 
on 
spliceosome 
analysis 
can 
be 
asserted 
from 
this 
work, 
addressing 
both, 
experimental 
biologists 
and 
computational 
scientists. 
First, 
experimental 
data 
should 
immediately 
be 
stored 
in 
a 
structured 
pre-formatted 
way, 
making 
use 
of 
existing 
formalisms 
and 
avoiding 
unnecessary 
naming 
morphisms. 
Experimental 
data 
provides 
precious 
facts 
which 
are 
necessary 
to 
prepare 
subsequent 
in 
silico 
analyses. 
Second, 
theoretical 
and 
computational 
contributions 
can 
still 
be 
improved 
in 
the 
supply 
of 
data 
collection 
tools 
as 
well 
as 
integrated 
pipelines 
for 
their 
global 
evaluation 
and 
analysis. 
For 
example, 
text 
mining 
tools 
at 
the 
level 
of 
network 
design 
and 
statistical 
measures 
at 
the 
level 
of 
substructure 
analysis 
(e.g., 
T-invariants, 
MCTS) 
can 
enhance 
the 
output 
of 
this 
modeling 
approach. 


Finally, 
one 
needs 
to 
keep 
in 
mind, 
that 
structural 
properties 
depend 
at 
rst 
hand 
on 
the 
knowledge 
put 
into 
the 
model. 
In 
light 
of 
the 
wealth 
of 
biological 
information, 
a 
direct 
consequence 
is 
the 
possibility 
that 
parts 
of 
the 
model 
are 
better 
covered 
by 
data 
than 
others, 
and 
therefore 
exhibiting 
a 
higher 
complexity. 
Consequently, 
these 
parts 
are 
stronger 
represented 
by 
T-invariants. 
Nevertheless, 
the 
fact 
to 
observe 
a 
stronger 
representation 
of 
individual 
aspects 
of 
a 
biological 
network 
justies 
a 
model, 
because 
it 
captures 
relations 
and 
trends 
that 
are 
hard 
to 
detect 
using 
detailed 
mechanistic 
studies, 
which 
moreover 
are 
to 
numerous 
for 
individual 
analyses. 



Conclusions 


The 
present 
dissertation 
is 
structured 
into 
three 
parts, 
which 
are 
connected 
by 
the 
overall 
topic 
of 
Alternative 
Splicing 
and 
its 
computational 
analysis. 
The 
rst 
and 
second 
chapter 
concentrate 
on 
sequence 
based 
approaches, 
aiming 
on 
how 
transcripts, 
which 
are 
nowadays 
abundantly 
present 
in 
public 
databases, 
can 
be 
used 
to 
infer 
knowledge 
on 
the 
regulation 
of 
alternatively 
splicing. 
The 
third 
chapter 
adds 
a 
systems 
biological 
approach 
by 
modeling 
the 
spliceosome, 
the 
biological 
component, 
which 
enables 
eukaryotic 
organisms 
to 
perform 
the 
splicing 
reaction. 


Chapter 
1 
The 
outcome 
of 
initial 
preparative 
sequence 
processing 
usually 
results 
in 
repositories 
of 
alternatively 
spliced 
transcripts 
such 
as 
the 
Eased 
database 
(253). 
In 
consequence, 
a 
variety 
of 
databases 
storing 
alternative 
splice 
forms 
exists 
but 
although 
most 
of 
them 
are 
based 
on 
transcript 
and 
genome 
sequences 
of 
the 
same 
primary 
databases, 
the 
alignment 
algorithms 
and 
lter 
criteria 
applied 
in 
prediction 
pipelines 
vary 
considerably. 
Hence, 
it 
is 
dicult 
to 
compare 
alternative 
splice 
events 
from 
dierent 
databases. 
In 
essence 
each 
dataset 
should 
be 
characterized 
before 
being 
subjected 
to 
further 
analyses. 
Following 
this 
idea, 
the 
rst 
part 
of 
this 
work 
aimed 
on 
the 
investigation 
of 
features, 
which 
arm 
the 
Eased 
dataset 
of 
splice 
forms, 
justifying 
to 
pursue 
new 
questions 
based 
on 
these 
data. 
To 
this 
end 
the 
dataset 
was 
partitioned 
into 
sets 
of 
splice 
forms 
with 
reference 
(rss) 
and 
alternative 
splice 
sites 
(ass). 
The 
distribution 
of 
donor-and 
acceptor 
signal 
strength 
was 
compared 
between 
both 
sets 
and 
between 
both 
types 
of 
splicing 
signals. 
From 
these 
comparisons 
it 
can 
be 
concluded 
that: 


• 
Donor-and 
acceptor 
strengths 
dier 
signicantly 
between 
alternative 
and 
reference 
splice 
sites, 
both 
signals 
showing 
a 
trend 
to 
weaker 
signals 
in 
the 
set 
of 
alternative 
splice 
sites. 
However, 
there 
exists 
an 
overlap 
in 
the 
score 
115 



116 
Conclusions 


distributions, 
were 
reference 
and 
alternative 
splice 
forms 
show 
an 
equal 
splice 
site 
strength. 


• 
The 
dierence 
of 
the 
average 
splice 
site 
strength 
(S 
= 
jSrss 
- 
Sassj) 
between 
alternative 
and 
reference 
splice 
sites 
is 
lower 
at 
acceptor 
compared 
to 
donor 
sites. 
• 
Donor 
splicing 
signals 
are 
weaker 
than 
acceptor 
splicing 
signals 
irrespective 
of 
the 
classication 
into 
alternative 
or 
reference 
splice 
sites, 
being 
in 
agreement 
with 
the 
fact 
that 
initial 
donor 
site 
selection 
often 
requires 
additional 
determinants 
to 
achieve 
the 
necessary 
level 
of 
specicity. 
Additionally, 
the 
transcript 
abundance 
representing 
alternative 
and 
reference 
splice 
sites 
was 
compared. 
Here, 
the 
Eased 
dataset 
showed 
the 
general 
trend 
that 
alternative 
splice 
sites 
(pairs 
of 
donor-and 
acceptor 
sites) 
are 
less 
abundantly 
supported 
by 
transcripts 
than 
the 
reference 
splice 
sites. 
This 
nding 
was 
complemented 
by 
the 
observation 
that 
the 
set 
of 
more 
frequently 
used 
splice 
sites 
(as 
derived 
from 
the 
transcript 
abundance) 
tend 
to 
pose 
a 
stronger 
splicing 
signal 
than 
splice 
sites 
of 
low 
utilization. 
The 
lower 
signal 
strength 
of 
alternative 
compared 
to 
constitutive 
splice 
sites 
has 
been 
previously 
reported 
for 
other 
datasets 
of 
splice 
events 
(254). 
This 
agreement 
was 
taken 
to 
conclude 
that 
the 
splice 
site 
strength 
of 
alternative 
splice 
forms 
of 
the 
Eased 
database 
can 
be 
suciently 
discriminated 
from 
reference 
splice 
site. 
A 
follow-up 
analysis 
on 
additional 
splicing 
regulating 
motifs 
was 
conducted. 
The 
weaker 
signal 
or 
information 
content 
of 
alternative 
splice 
sites 
raises 
the 
question 
how 
trans-acting 
factors 
(e.g., 
the 
spliceosome) 
distinguish 
between 
these 
splice 
sites. 
As 
one 
possible 
explanation 
the 
action 
of 
regulatory 
proteins 
was 
proposed 
(6), 
which 
bind 
to 
motifs 
near 
alternative 
splice 
sites 
and 
serve 
as 
guiding 
elements 
for 
example 
by 
interactions 
between 
RS 
domains. 
Hence, 
the 
presence 
of 
binding 
motifs 
of 
four 
well 
characterized 
splicing 
regulating 
proteins 
(ASF/SF2, 
SC35, 
SRp40 
and 
SRp55) 
was 
analyzed 
and 
compared 
between 
alternative 
and 
reference 
splice 
sites 
and 
between 
exon 
and 
intron 
splice 
site 
context. 
This 
led 
to 
the 
following 
conclusions: 


• 
The 
four 
investigated 
splicing 
factor 
binding 
motifs 
occur 
among 
ass 
and 
rss 
individually 
more 
frequently 
in 
the 
intronic 
donor 
than 
in 
the 
intronic 
acceptor 
context, 
but 
with 
about 
the 
same 
frequency 
in 
the 
exonic 
donor 
region. 
This 
indicates 
that 
the 
conservation 
of 
these 
motifs 
is 
reduced 
in 

the 
intron 
compared 
to 
the 
exon 
region 
near 
donor 
sites, 
and 
even 
more 
reduced 
in 
the 
intron 
region 
near 
acceptor 
sites. 
Such 
constraints 
can 
be 
imposed 
by 
the 
coding 
region, 
polypyrimidine 
tract 
and 
branchpoint, 
which 
are 
determinants 
of 
splice 
site 
selection 
in 
both, 
ass 
and 
rss. 


• 
While 
the 
investigated 
motif 
frequencies 
are 
about 
equal 
between 
ass 
and 
rss 
in 
the 
exonic 
contexts 
of 
all 
four 
binding 
motif, 
the 
intronic 
regions 
show 
more 
binding 
motifs 
near 
alternative 
than 
near 
reference 
splice 
sites, 
indicating 
a 
higher 
conservation 
pressure 
for 
splicing 
factor 
binding 
sites 
for 
the 
alternative 
splice 
forms. 
• 
For 
all 
four 
SR 
proteins 
it 
is 
found 
that 
although 
there 
are 
less 
binding 
motifs 
in 
the 
intron 
context 
compared 
to 
the 
exon 
context 
near 
splice 
sites, 
the 
intron 
context 
of 
rss 
is 
indicative 
of 
less 
binding 
motifs 
than 
the 
intron 
context 
in 
ass. 
• 
The 
polarization 
(dierence 
in 
binding 
motif 
frequency 
between 
exon 
and 
intron 
context) 
is 
for 
ASF/SF2 
stronger 
in 
ass 
than 
in 
rss 
around 
both, 
donor 
and 
acceptor 
sites. 
SC35 
shows 
almost 
no 
polarization 
and 
SRp40 
and 
SRp55 
only 
at 
donor 
sites, 
with 
more 
binding 
sites 
in 
the 
exonic 
than 
intronic 
donor 
context. 
It 
can 
be 
concluded 
that 
although 
ASF/SF2 
has 
been 
found 
to 
participate 
in 
splicing 
of 
non-alternative 
exons, 
the 
frequency 
of 
its 
binding 
motifs 
is 
discriminative 
for 
alternative 
and 
reference 
splice 
sites. 
Summarizing, 
there 
are 
qualitative 
dierences 
in 
the 
distribution 
of 
binding 
motifs 
for 
auxiliary 
splicing 
factors, 
depending 
on 
the 
splice 
site 
proximal 
location 
within 
one 
and 
the 
same 
class 
(ass 
or 
rss). 
Additionally, 
also 
dierences 
between 
ass 
and 
rss 
exists, 
which 
stand 
in 
line 
with 
the 
compensatory 
eect 
that 
splicing 
factors 
convey 
to 
the 
selection 
of 
weak 
alternative 
splice 
sites. 
Although 
binding 
motifs 
for 
splicing 
factors 
are 
thought 
to 
be 
degenerated 
in 
composition, 
their 
presence 
provide 
an 
eective 
means 
for 
the 
cell 
to 
regulate 
splice 
site 
selection 
via 
concentration 
gradients 
of 
these 
factors. 
It 
is 
tempting 
to 
speculate 
that 
an 
additional 
layer 
of 
specicity 
in 
modulating 
splice 
site 
selection 
may 
be 
achieved 
by 
combinations 
of 
binding 
motifs, 
similar 
as 
found 
for 
transcription 
factors 
(255). 
This 
analysis 
did 
not 
distinguish 
between 
dierent 
types 
of 
ass 
(an 
alternative 
site 
can 
either 
be 
an 
alternative 
acceptor 
or 
donor 
site), 
however, 
since 
eukaryotic 
exons 
are 
relatively 
short 
(in 
average 
around 
100-140nt) 
the 
denition 
of 
splice 



118 
Conclusions 


sites 
occurs 
predominantly 
across 
exons 
(known 
as 
exon 
denition 
model 
(85)). 
In 
consequence, 
the 
binding 
of 
regulatory 
factors 
on 
either 
site 
may 
have 
an 
eect 
on 
the 
alternative 
splicing 
of 
both 
splice 
sites. 


Chapter 
2 
The 
Eased 
database 
does 
not 
resolve 
dierent 
alternative 
splicing 
patterns 
in 
more 
detail, 
for 
example, 
exon 
skipping 
or 
specic 
A5E 
events. 
Additionally, 
the 
lter 
parameter 
of 
the 
Eased 
prediction 
pipeline 
was 
restricted 
to 
alignment 
variations 
of 
at 
least 
six 
nucleotides, 
a 
limitation 
of 
which 
also 
other 
databases 
suer. 
In 
consequence, 
minor 
splice 
site 
variations 
were 
commonly 
considered 
as 
noise 
of 
the 
alignment 
algorithm 
in 
the 
past. 
In 
2004, 
the 
rst 
reports 
armed 
the 
frequent 
occurrence 
of 
subtle 
variations 
at 
human 
acceptor 
splice 
sites 
and 
experimentally 
proved 
their 
plausibility 
as 
alternative 
splice 
forms 
(2). 
The 
second 
part 
of 
this 
thesis 
connects 
to 
this 
development 
by 
focusing 
on 
a 
special 
kind 
of 
subtle 
splice 
events 
at 
donor 
splice 
sites. 
Alterations 
at 
donor 
sites 
were 
already 
early 
shown 
to 
pose 
a 
crucial 
element 
for 
splice 
site 
selection 
(86). 
The 
complementarity 
of 
the 
50ss 
to 
the 
3. 
end 
of 
the 
U1 
snRNA 
introduces 
a 
naturally 
favored 
second 
donor 
signal 
and 
raises 
the 
question 
whether 
alternative 
splicing 
at 
this 
tandem 
splicing 
signal 
leads 
to 
a 
regulatory 
eect 
in 
gene 
expression. 
Using 
the 
dataset 
of 
the 
Hollywood 
database 
(3), 
at 
rst 
the 
prevalence 
of 
alternative 
donor 
splice 
events 
with 
focus 
on 
single 
exons 
was 
determined, 
This 
led 
to 
the 
following 
conclusions: 


• 
An 
initial 
dataset 
of 
5,275 
exons, 
indicating 
alternative 
splicing 
at 
their 
downstream 

anking 
donor 
site, 
shows 
a 
dominant 
fraction 
(17 
-38%) 
of 
subtle 
variations 
(e.g., 
< 
6 
bp), 
which 
is 
higher 
than 
previous 
estimates 
of 
alignment 
error 
rates 
stated 
(256). 
• 
Samples 
indicate 
that 
the 
initial 
annotation 
method 
(Sim4) 
produces 
false 
positive 
splice 
forms 
(see 
Appendix 
B.1(a)), 
which 
are 
corrected 
by 
a 
more 
recent 
method 
using 
dynamic 
programming 
in 
conjunction 
with 
a 
splice 
site 
model 
(Exalin). 
This 
advises 
to 
reckon 
with 
a 
higher 
false 
positive 
rate 
in 
predicting 
subtle 
splice 
events 
without 
additional 
constraints 
in 
the 
transcript-genome 
alignment 
strategy. 
• 
Independent 
from 
the 
prediction 
method 
the 
most 
frequent 
alternative 
donor 
splice 
pattern 
diers 
by 
4 
nucleotides 
(9 
-28% 
of 
all 
A5E), 
and 
thus, 
contrast 
the 
most 
frequent 
variation 
(3 
nt) 
observed 
at 
acceptors 
sites. 

• 
The 
abundance 
of 
subtle 
splice 
events 
(< 
6 
nt), 
including 
the 
most 
frequently 
occurring 
4 
isoforms 
could 
also 
be 
observed 
in 
the 
comparative 
species 
Mus 
musculus 
The 
amount 
of 
A5E4 
splicing 
is 
in 
agreement 
with 
the 
frequency 
reported 
previously 
in 
a 
smaller 
dataset 
(7.5%), 
which 
however, 
was 
based 
on 
alternative 
splice 
events 
conserved 
in 
human 
and 
mouse 
(14). 
Trusting 
such 
rigor, 
all 
further 
analyses 
and 
results 
are 
based 
on 
the 
most 
stringent 
dataset 
rened 
by 
the 
Exalin 
alignment 
method, 
pointing 
to 
a 
somewhat 
higher 
occurrence 
of 
9% 
4 
isoforms. 
Based 
on 
the 
transcript 
evidence, 
supporting 
each 
of 
two 
alternative 
splice 
events, 
an 
obvious 
dierence 
between 
donor 
and 
acceptor 
sites 
is 
found. 
While 
A3E 
show 
no 
clear 
separation 
into 
classes, 
where 
either 
one 
of 
the 
distal 
or 
proximal 
splice 
sites 
is 
preferred, 
such 
a 
dichotomy 
is 
observed 
for 
the 
A5E 
including 
the 
4 
splice 
variants. 
Thus 
the 
transcript 
support 
suggests 
that 
in 
general 
two 
types 
of 
alternative 
donor 
splice 
events 
are 
distinguishable: 
a 
proximal 
major 
event 
(type-I, 
A5E 
P4) 
and 
a 
distal 
major 
event 
(type-II, 
A5E 
D4). 
Further, 
the 
transcript 
evidence 
indicates 
a 
higher 
occurrence 
of 
type-II 
than 
type-I 
A5E4 
events, 
a 
polarization, 
which 
appears 
exactly 
opposite 
within 
the 
remaining 
A5E 
events. 
This 
situation 
suggests 
a 
formal 
distinction 
between 
non-overlapping 
and 
overlapping 
(tandem) 
donor 
sites, 
which 
in 
similar 
way 
was 
found 
by 
other 
reports 
(257, 
121). 
Following 
this 
classication 
scheme, 
A5E4 
splice 
forms 
are 
characterized, 
leading 
to 
the 
following 
conclusions: 


• 
The 
minor 
form 
of 
both 
A5E4 
types 
exhibits 
in 
general 
a 
weaker 
splice 
site 
score 
compared 
to 
the 
major 
form, 
however, 
type-I 
minor 
donor 
splice 
sites 
are 
still 
closer 
to 
the 
U1 
snRNA 
consensus 
motif 
than 
type-II 
minor 
sites, 
implicating 
a 
higher 
potential 
for 
selection 
of 
the 
minor 
upstream 
donor 
(d4) 
in 
type-I 
A5E4 
isoforms. 
• 
Pseudo 
splice 
sites 
distanced 
by 
four 
nucleotides 
from 
authentic 
donor 
splice 
sites 
in 
constitutive 
exons 
are 
more 
reliably 
distinguished 
than 
minor 
and 
major 
sites 
of 
tandem 
donors 
in 
both, 
type-I 
and 
II 
classication 
(estimated 
accuracy 
of 
95%). 
However, 
type-II 
A5E4 
events 
reach 
almost 
the 
same 
accuracy 
(92%) 
in 
distinguishing 
the 
minor 
from 
the 
major 
donor 
splice 
site, 
indicating 
a 
risk 
to 
enrich 
also 
false 
positive 
events 
(pseudo 
splice 
sites) 
in 
this 
class. 

120 
Conclusions 


• 
Both 
classes 
showed 
a 
dierent 
scheme 
in 
position 
specic 
nucleotide 
conservation, 
which 
may 
in
uence 
the 
binding 
selectivity 
by 
snRNAs 
of 
the 
spliceosomal 
complexes 
U1, 
U5 
and 
U6. 
With 
respect 
to 
the 
weaker 
tan-
dem 
donor 
sites, 
type-I 
A5E4 
events 
show 
positions 
conserved, 
which 
are 
crucial 
for 
U1 
and 
U6 
snRNA 
binding, 
while 
type-II 
A5E4 
events 
show 
less 
conservation 
and 
in 
dierent 
positions. 
The 
nding 
of 
individual 
splice 
site 
nucleotides 
complement 
the 
results 
determined 
in 
the 
splice 
site 
score 
analysis. 
• 
The 
presence 
of 
cis-elements 
for 
binding 
of 
splicing 
silencing 
proteins, 
differed 
between 
both 
classes, 
with 
a 
tendency 
to 
more 
silencer 
elements 
within 
the 
downstream 
intron 
of 
type-I 
A5E4 
events 
and 
less 
silencer 
elements 
of 
type-II 
A5E4 
events 
compared 
to 
introns 
downstream 
of 
constitutive 
donors. 
This 
arms 
a 
functional 
role 
for 
the 
distal 
minor 
donor, 
while 
it 
appears 
less 
likely 
that 
the 
distal 
major 
donor 
is 
silenced 
to 
improve 
the 
proximal 
minor 
donor. 
• 
The 
comparison 
of 
orthologous 
tandem 
donor 
regions 
indicate 
that 
both 
classes 
do 
not 
reach 
conservation 
levels 
in 
the 

anking 
intronic 
region, 
as 
observed 
for 
skipped 
exons. 
However, 
type-I 
AS 
events 
showed 
a 
better 
intron 
conservation 
in 
the 
corresponding 
mouse 
genes, 
supporting 
the 
presence 
of 
evolutionary 
conserved 
regulatory 
elements. 
Additionally, 
examples 
are 
found, 
where 
not 
only 
the 
splice 
site 
motif 
was 
conserved 
in 
the 
orthologous 
mouse 
gene 
but 
also 
both 
donors 
are 
conrmed 
by 
transcript 
data, 
pointing 
to 
regulatory 
importance 
of 
this 
splicing 
modus. 
• 
A5E4 
splice 
events 
are 
in 
more 
than 
90% 
of 
all 
cases 
located 
within 
the 
coding 
region 
and, 
thus, 
cause 
a 
shift 
in 
the 
open 
reading 
frame. 
Type-II 
AS 
events 
introduced 
in 
nearly 
70% 
a 
premature 
termination 
codon 
(PTC) 
when 
spliced 
at 
the 
minor 
donor, 
while 
splicing 
of 
only 
one 
quarter 
of 
type-I 
minor 
donors 
caused 
a 
PTC. 
Most 
of 
these 
PTC 
are 
located 
at 
a 
distance 
from 
the 
alternative 
translation 
stop 
signal 
to 
qualify 
as 
triggers 
of 
the 
nonsense 
mediated 
decay 
(NMD) 
pathway. 
This 
suggests 
especially 
type-II 
alternative 
splicing 
to 
be 
a 
mechanism 
of 
down-regulating 
protein 
isoforms 
within 
the 
cell, 
as 
contrasted 
by 
type-I 
isoforms, 
which 
more 
frequently 
may 
produce 
truncated 
proteins. 
Moreover, 
type-I 
splice 
events 
occurred 
signicantly 
more 
frequently 
in 
genes, 
which 
encode 
RNA 
binding 
proteins, 
suggesting 
a 
regulatory 
function 
at 
the 
RNA 
level 
of 
gene 
expression. 

Some 
questions 
have 
been 
posed 
by 
this 
analysis 
and 
remain 
open 
for 
further 
analysis. 
The 
set 
of 
tandem 
donors, 
showing 
the 
highest 
conservation 
compared 
to 
mouse 
(for 
example 
SFRS16 
exon 
15, 
MSF2 
exon 
3, 
CASD1 
exon 
14 
or 
BRSK1 
exon 
3) 
can 
be 
investigated 
on 
a 
broader 
range 
of 
species 
to 
infer 
knowledge 
about 
their 
evolutional 
background. 
Although 
small, 
the 
subset 
of 
A5E4 
tan-
dem 
donors 
with 
high 
conserved 

anking 
introns 
may 
serve 
to 
isolate, 
in 
more 
detail, 
sequence 
regions 
with 
impact 
on 
the 
U1 
or 
U6 
binding 
behavior. 
Another 
extension 
of 
this 
work 
can 
investigate 
to 
what 
extent 
A5E4 
events 
and 
other 
frame 
shifting 
AS 
events 
occur 
mutually 
and 
compensate 
each 
other 
to 
avoid 
the 
NMD 
pathway. 
The 
problem 
here 
is 
that 
EST 
inferred 
AS 
events 
are 
not 
well 
suited 
for 
such 
an 
in 
silico 
analysis 
as 
it 
has 
to 
be 
assured 
that 
two 
or 
more 
AS 
events 
derive 
from 
the 
same 
mRNA. 
The 
analysis 
of 
conserved 
positions 
within 
tandem 
donor 
sites 
raised 
the 
question, 
to 
what 
extend, 
secondary 
structures 
of 
precursor 
messenger 
RNAs 
contribute 
to 
the 
specic 
selection 
of 
overlapping 
alternative 
splice 
sites. 
For 
example, 
sequence 
parts 
downstream 
of 
the 
tan-
dem 
donor 
and 
complementary 
to 
the 
distal 
donor, 
may, 
under 
specic 
cellular 
conditions 
occupy 
the 
distal 
donor 
within 
a 
stemloop 
structure, 
such, 
that 
the 
proximal 
donor 
locates 
in 
an 
open 
loop, 
and 
hence, 
remain 
accessible 
for 
spliceosomal 
components. 
This 
would 
suggest 
a 
model, 
which 
controls 
the 
regulation 
of 
overlapping 
donor 
sites 
by 
two 
dierent 
layers: 
i) 
the 
increased 
specicity 
due 
to 
the 
larger 
sequence 
context 
involved 
in 
stemloop 
formation 
around 
the 
splice 
site 
and 
ii) 
the 
presence 
of 
proteins, 
which 
control 
the 
structural 
conformation 
of 
the 
pre-mRNA 
molecule. 


It 
also 
remains 
to 
be 
investigated 
to 
what 
extent 
mutations 
in 
splice 
sites 
result 
in 
the 
creation 
of 
A5E4 
tandem 
donors 
or 
shift 
splicing 
from 
the 
proximal 
to 
the 
distal 
donor 
and 
vice 
versa. 
Aberrant 
donor 
splice 
sites 
have 
been 
previously 
investigated, 
but 
without 
closer 
examination 
of 
the 
impact 
on 
subtle 
splice 
events 
(258). 
Some 
earlier 
experimental 
reports 
indicate 
the 
involvement 
of 
subtle 
splice 
variations 
in 
disease 
formation. 
For 
example, 
a 
G 
to 
A 
mutation 
at 
the 
rst 
position 
of 
intron 
10 
in 
the 
adenosine 
deaminase 
(ADA) 
gene 
results 
in 
a 
shift 
from 
the 
distal 
to 
the 
proximal 
donor 
site. 
In 
consequence, 
a 
4 
nt 
insertion 
can 
be 
observed 
in 
cDNAs 
of 
ADA 
decient 
patients 
(259). 
In 
contrast 
to 
this 
kind 
of 
proximal 
4 
donors 
usage 
other 
cases 
of 
donor 
mutations 
at 
the 
rst 
intron 
base 
shifted 
splicing 
to 
cryptic 
donor 
splice 
sites 
located 
more 
than 
10 
nt 
downstream 
of 
the 
mutation 
(258). 
Finally, 
to 
address 
subtle 
donor 
splicing 
in 
a 
medical 
way, 
the 
measuring 
of 
this 
pattern 
over 
various 
tissues 
and 
by 
uti



122 
Conclusions 


lizing 
microarrays, 
will 
give 
further 
insights 
in 
the 
regulation 
of 
these 
splice 
forms. 


Chapter 
3 
The 
DNA 
and 
mRNA 
sequence 
based 
analysis 
of 
alternative 
splicing 
has 
revealed 
a 
magnitude 
of 
information, 
covering 
dierent 
splicing 
patterns, 
signals 
and 
binding 
motifs 
and 
their 
evolutionary 
conservation. 
However, 
to 
understand 
what 
initiates 
and 
governs 
the 
position 
specic 
splicing 
of 
mRNA 
requires 
to 
understand 
the 
network 
of 
molecules, 
which 
participate 
in 
the 
splicing 
reaction. 
The 
rst 
and 
second 
chapter 
demonstrated, 
how 
sequence 
inherent 
signals 
concur 
with 
the 
occurrence 
of 
alternative 
splicing 
patterns 
and 
how 
A5E4 
splice 
variants 
are 
enriched 
in 
the 
functional 
category 
of 
mRNA-binding 
factors. 
Also 
the 
importance 
of 
the 
donor 
site 
nucleotide 
composition 
is 
clearly 
a 
crucial 
parameter 
to 
distinguish 
AS 
types. 
These 
ndings 
strongly 
underline 
the 
in
uence 
of 
trans-acting 
elements 
in 
the 
splicing 
process. 
Which 
proteins 
do 
interact 
at 
which 
time 
to 
realize 
a 
specic 
spliceosome 
and 
how? 
Does 
a 
redundancy 
in 
the 
proteomic 
complement 
exists, 
which 
allows 
to 
perform 
the 
splicing 
reaction 
with 
high 
accuracy 
and 
reproducibility 
even 
under 
changing 
conditions 
? 
Presently, 
no 
database 
can 
serve 
with 
a 
map 
of 
interactions 
signaling 
reactions, 
which 
describe 
the 
spliceosome 
assembly 
or 
summarize 
and 
visualize 
possible 
nuances 
of 
this 
network. 
The 
last 
chapter 
aimed 
on 
xating 
and 
arranging 
a 
large 
part 
of 
data 
on 
spliceosomal 
proteins 
and 
ribonucleoprotein 
complexes 
into 
a 
model 
for 
structural 
analysis. 
The 
achievements 
of 
the 
general 
model 
can 
be 
summarized 
as 
follows: 


• 
The 
model 
establishes 
a 
network 
of 
reactions 
leading 
to 
the 
assembly 
of 
an 
active 
spliceosome. 
• 
The 
model 
includes 
more 
than 
one 
hundred 
molecular 
components 
including 
their 
ordered 
and 
directed 
interactions, 
derived 
by 
extensive 
screening 
of 
literature. 
• 
The 
model 
combines 
RNA 
as 
well 
as 
proteins 
and 
intermediate 
compounds 
of 
both 
molecules 
as 
reacting 
and 
interacting 
species 
in 
the 
network. 
• 
Components 
of 
the 
spliceosome 
are 
modeled 
in 
dierent 
layers 
to 
provide 
a 
clear 
representation. 
Large 
biological 
networks 
often 
suer 
the 
problem 
of 
overcrowded 
layout, 
which 
impedes 
orientation. 
Compartmentalization 
and 

hierarchical 
layers 
as 
shown 
in 
Figure 
3.10 
and 
Figure 
3.11 
are 
approaches 
to 
circumvent 
this 
problem. 


• 
The 
network 
is 
designed 
as 
Petri 
net 
model, 
which 
allowed 
the 
computational 
validation 
by 
decomposition 
into 
minimal 
T-invariants. 
• 
The 
model 
network 
is 
completely 
covered 
by 
T-invariants, 
each 
corresponding 
to 
a 
biological 
process 
during 
spliceosome 
assembly. 
• 
T-invariants 
are 
further 
grouped 
into 
functional 
clusters, 
which 
are 
a 
helpful 
technique 
to 
accentuate 
similarities 
and 
dierences 
in 
long 
pathways. 
Further, 
as 
regards 
biological 
content, 
the 
model 
allows 
the 
following 
conclusions: 


• 
Excluding 
the 
decomposition 
reactions 
of 
the 
multitude 
of 
intermediate 
complexes 
prevents 
the 
formation 
of 
futile 
cycles. 
• 
The 
presence 
of 
a 
discard 
pathways 
as 
previously 
suggested 
(197), 
is 
demonstrative 
for 
a 
regulatory 
circuit, 
but 
also 
for 
the 
problem 
of 
combinatorial 
complexity 
of 
spliceosome 
assembly. 
• 
The 
dierent 
pathways 
of 
E-complex 
assembly 
indicate 
redundant 
modes 
of 
spliceosome 
assembly. 
• 
All 
stages 
of 
spliceosome 
assembly 
involve 
factors, 
which 
possess 
a 
multitude 
of 
interaction 
partners, 
which 
presently 
cannot 
be 
included 
into 
the 
model 
due 
to 
uncertainty 
about 
their 
function 
and 
time 
of 
entering 
the 
assembly 
process. 
• 
The 
feedback 
of 
non-protein 
coding 
alternative 
splice 
forms 
on 
spliceosome 
assembly 
as 
demonstrated 
in 
chapter 
2, 
is 
presently 
not 
clear. 
The 
network 
of 
spliceosomal 
assembly 
as 
presented 
here, 
serves 
as 
a 
basic 
scaold 
to 
successively 
map 
the 
occurrence 
and 
impact 
of 
alternative 
splice 
events 
on 
the 
assembly 
pathway. 
This 
may 
aid 
in 
the 
investigation 
of 
new 
hypotheses 
about 
which 
alternative 
splice 
events 
contribute 
to 
which 
spliceosomal 
states, 
allowing 
to 
classify 
spliceosomes 
in 
more 
detail 
according 
to 
their 
composition 
and 
assembly. 
For 
example, 
if 
a 
general 
splicing 
factor 
like 
ASF/SF2 
becomes 
alternatively 
spliced, 
such 
that 
it 
can 
not 
be 
phosporylated 
and 
hence, 
participate 
in 
splice 
site 
recognition 
anymore, 
one 
would 
expect 
a 
spliceosome, 
which 
can 
only 
recognize 



124 
Conclusions 


very 
strong 
consensus 
donor 
sites. 
Since 
also 
other 
splicing 
factors, 
for 
example, 
SC35 
or 
TIA1 
are 
known 
to 
in
uence 
the 
initial 
steps 
during 
E-complex 
assembly, 
redundancy 
in 
recognition 
of 
pre-mRNA 
signals 
by 
interchangeable 
factors 
must 
be 
taken 
into 
account. 
This 
introduces 
some 
degree 
of 
redundancy, 
but 
may 
assure 
the 
delity 
of 
the 
splice 
reaction 
under 
dierent 
conditions. 
Finally, 
another 
important 
aspect 
for 
future 
works, 
envisioning 
the 
step 
from 
structural 
to 
kinetic 
modeling, 
is 
the 
consideration 
of 
concentration 
levels 
of 
spliceosomal 
core 
components 
and 
auxiliary 
factors. 
For 
example, 
RNAi 
knockouts 
of 
transcripts 
of 
several 
DExH/D 
helicases 
(Brr2, 
Prp5, 
Prp22) 
aect 
the 
exon 
inclusion 
levels 
of 
the 
DSCAM 
exon 
4 
cluster 
(201). 
Since 
alternative 
splicing 
can 
be 
associated 
with 
weaker 
splice 
sites, 
the 
kinetics 
of 
generating 
such 
splice 
patterns 
might 
be 
more 
aected 
by 

uctuating 
levels 
of 
spliceosomal 
proteins, 
compared 
to 
constitutive 
splice 
sites. 
Consistent 
with 
this, 
the 
concentration 
ratios 
of 
antagonistic 
splicing 
factors 
as 
ASF/SF2 
and 
hnRNP 
A1 
have 
been 
shown 
to 
vary 
over 
a 
wide 
range 
of 
tissues 
(260), 
which 
suggests 
a 
tissue 
dependent 
dierence 
in 
the 
kinetics 
of 
spliceosome 
assembly. 


Beside 
the 
core 
spliceosome 
there 
exist 
related 
regulatory 
networks, 
which 
involve 
essential 
alternative 
splicing 
events, 
hence 
being 
interesting 
candidate 
networks 
for 
follow-up 
models 
to 
this 
work. 
Two 
examples 
are 
given 
below: 


• 
The 
Drosophila 
female-specic 
splicing 
regulatory 
protein 
Tra2 
stabilizes 
the 
binding 
of 
SR 
proteins 
to 
a 
splicing 
enhancer 
element, 
which 
facilitates 
recruitment 
of 
the 
U2AF 
heterodimer 
to 
the 
weak 
female-specic 
polypyrimidine 
tract 
of 
the 
dsx 
gene 
(6). 
Together 
with 
an 
auto-regulatory 
splicing 
mechanism 
on 
its 
own 
pre-mRNA, 
this 
protein 
controls 
the 
production 
of 
sex 
specic 
alternative 
isoforms 
in 
the 
fruit
y 
development. 
The 
human 
orthologue 
Tra2ß 
is 
not 
less 
important, 
because 
it 
regulates 
splicing 
of 
exon 
10 
in 
the 
Tau 
gene 
(261), 
whose 
missplicing 
causes 
a 
severe 
variant 
of 
frontotemporal 
dementia 
(FTPD17). 
• 
The 
Drosophila 
SXL 
protein 
constitutes 
a 
splicing 
factor, 
which 
is 
functioning 
prior 
to 
Tra2. 
It 
involves 
SPF45 
as 
co-regulator 
and 
binds 
cooperatively 
up 
and 
downstream 
of 
an 
alternative 
exon 
in 
its 
own 
pre-mRNA 
(6). 
The 
auto-regulatory 
feedback 
systems 
in 
the 
sex 
determination 
cascade 
of 
the 
fruit
y, 
constitutes 
interesting 
models 
for 
system 
biological 
analyses 
(262). 
Some 
conclusions 
emerged 
from 
this 
work 
also 
with 
respect 
to 
technical 
aspects 



of 
Petri 
net 
modeling: 
i) 
Petri 
net 
modeling 
software, 
as 
used 
here 
(235), 
should 
be 
improved 
to 
overcome 
the 
problem 
of 
node 
ID 
preservation. 
Presently, 
the 
modication 
of 
parts 
of 
the 
network 
resets 
the 
IDs 
of 
all, 
even 
of 
unchanged, 
places 
and 
transitions. 
In 
consequence, 
it 
is 
unnecessary 
exacerbated, 
especially 
in 
large 
networks, 
to 
compare 
the 
outcomes 
of 
T-invariant 
and 
MCTS 
analyses 
between 
dierent 
model 
scenarios. 
ii) 
Reactions 
and 
reactants 
should 
be 
labeled 
uniformly 
in 
a 
standardized 
way. 
The 
challenge 
lies 
in 
the 
balance 
of 
mnemonic 
labels 
to 
preserve 
a 
clear 
network 
representation. 
Ideally, 
the 
model 
would 
be 
converted 
into 
a 
standardized 
design 
language 
(e.g., 
systems 
biology 
markup 
lang
uage 
(263)) 
to 
make 
it 
comparable 
and 
applicable 
for 
other 
analysis 
tools. 
iii) 
A 
great 
deal 
of 
progress 
in 
the 
ab 
initio 
design 
of 
such 
model 
networks 
will 
be 
gained, 
if 
an 
automated 
text 
mining 
procedure 
for 
timed 
biological 
interactions 
between 
spliceosomal 
components 
is 
integrated 
into 
the 
design 
phase. 
This 
ascertains 
a 
higher 
information 
background 
on 
biochemical 
reactions 
and 
provides 
the 
possibility 
to 
estimate 
a 
reliability 
measure 
via 
the 
number 
of 
independent 
reports 
for 
each 
modeled 
reaction. 



126 
Conclusions 



Erklarung 


Hiermit 
erklare 
ich, 
dass 
ich 
die 
vorliegende 
Arbeit 
selbstandig 
und 
nur 
unter 
Verwendung 
der 
angegebenen 
Hilfsmittel 
angefertigt 
habe. 
Mir 
ist 
die 
geltende 
Promotionsordnung 
bekannt 
und 
ich 
habe 
weder 
die 
Hilfe 
eines 
Promotionsberaters 
in 
Anspruch 
genommen, 
noch 
haben 
Dritte 
unmittelbare 
oder 
mittelbare 
geldwerte 
Leistungen 
fur 
Arbeit 
erhalten, 
die 
im 
Zusammenhang 
mit 
dem 
Inhalt 
der 
vorgelegten 
Dissertation 
stehen. 
Die 
vorgelegte 
Dissertation 
wurde 
noch 
nicht 
als 
Prufungsarbeit 
fur 
eine 
staatliche 
oder 
andere 
wissenschaftliche 
Prufung 
eingereicht. 
Weiterhin 
habe 
ich 
mich 
mit 
der 
vorliegenden 
Arbeit 
an 
keiner 
anderen 
Hochschule 
um 
den 
akademischen 
Grad 
doctor 
rerum 
naturalium 
(Dr. 
rer. 
nat.) 
beworben 
und 
weder 
fruher 
noch 
gegenwartig 
die 
Eronung 
eines 
Verfahrens 
zum 
Erwerb 
des 
o.g. 
akademischen 
Grades 
an 
einer 
anderen 
Hochschule 
beantragt. 


Berlin, 
24. 
November 
2008 


..................................... 


(Ralf 
H. 
Bortfeldt) 


127 



128 
Erklarung 



Glossary 


A 


Acceptor: 
3. 
splice 
site, 
that 
marks 
the 
intron 
end 
during 
the 
splicing 
process. 
This 
boundary 
is 
involved 
in 
the 
second 
step 
of 
splicing 
where 
the 
downstream 
exon 
accepts“ 
the 
upstream 
exon 
when 
it 
is 
handed 


”
in 
for 
ligation 
to 
the 
downstream 
exon 
by 
the 
spliceosome 


ALN: 
Dynamic 
programming 
algorithm 
for 
identifying 
gene 
structures 
by 
aligning 
protein 
sequences 
or 
protein 
homology 
proles 
against 
codon-encoded 
DNA. 
Splicing 
signals, 
long 
gaps, 
coding 
potential 
and 
frameshift 
errors 
are 
considered 
in 
nding 
optimal 
matches 
to 
the 
reference 
sequence 


AltExtron: 
Collection 
of 
manually 
curated 
and 
experimentally 
conrmed 
alternatively 
spliced 
exons, 
based 
on 
annotated 
GenBank 
sequences 
and 
together 
with 
!AltExtron 
part 
of 
the 
Alternative 
Transcript 
Diversity 
Consortium 
a 
the 
European 
Molecular 
Biological 
Laboratory 
(EMBL) 


!http://www.ebi.ac.uk/asd/altextron 
AltSplice: 
Production 
and 
annotation 
pipeline 
for 
computationally 
derived 
alternative 
splice 
events 
based 
on 
the 
Ensembl 
genome 
annotation 
project 
!http://www.ebi.ac.uk/asd/altsplice/ 


B 


Bipartite 
Graph: 
A 
graph 
G 
= 
fV, 
E} 
whose 
nodes 
can 
be 
split 
into 
disjoint 
subsets 
A, 
B, 
such 
that 
no 
edges 
exist 
between 
elements 
of 
the 
same 
set. 
(V 
= 
set 
of 
vertices 
or 
nodes 
and 
E 
= 
set 
of 
edges). 
If 
fv, 
wg. 
E 
then 
holds 
either 
v 
. 
A 
. 
w 
. 
B 
or 
v 
. 
B 
. 
w 
. 
A. 
fA, 
B} 
is 
called 
bipartition 
of 
G. 


BLAST: 
Basic 
local 
alignment 
search 
tool. 
Generic 
term 
for 
a 
collection 
of 
programs 
which 
implement 
an 
alogrithm 
for 
comparing 
a 
new 
DNA 
or 
protein 
sequence 
against 
a 
database 
of 
known 
sequences. 
The 
algorithm 
returns 
a 
set 
of 
optimal 
local 
alignments 
given 
some 
specied 
parameter 
(e.g. 
gap 
penalties, 
seed 
word 
length) 
and 
their 
signicance 
as 
determined 
by 
the 
chance 
to 
nd 
the 
query 
sequence 
in 
a 
database 
of 
random 
sequences. 
WU-BLAST 
is 
one 
implementation 
of 
the 
BLAST 
alogrithm 
provided 
by 
the 
University 
of 
Washington 
!http://blast.wustl.edu/ 


Cajal 
Body: 
Organelle 
of 
the 
nucleus, 
0.1-2.0 
micrometer 
in 
diameter. 
Cajal 
bodies 
are 
considered 
as 
sites 
of 
assembly 
and/or 
modication 
of 
the 
transcription 
machinery 
and 
splicing 
factors 


cDNA: 
Copy 
DNA. 
Doublestranded 
DNA 
prepared 
from 
reversed 
transcribed 
RNA. 
cDNA 
is 
more 
stable 
than 
RNA 
and 
hence 
suited 
to 
study 
the 
modications 
a 
transcript 
is 
subjected 
to 
during 
the 
RNA 
maturation 
process 


CDS: 
Coding 
sequence, 
part 
of 
the 
mature 
messenger 
RNA 
which 
encodes 
the 
primary 
amino 
acid 
sequence 
of 
a 
protein 


129 



130 
Glossary 


css: 
constitutive 
splice 
site, 
splice 
site, 

anking 
an 
exon 
for 
which 
at 
a 
given 
stage 
of 
transcript 
knowledge 
no 
alternative 
splicing 
is 
observed 


D 


dbEST: 
Division 
of 
GenBank 
that 
stores 
. 
EST 
with 
some 
basic 
annotations, 
presently 
(release 
August 
2008) 
hosting 
more 
than 
1,600 
organisms, 
with 
human 
(8,137,901 
EST) 
and 
mouse 
(4,850,258 
EST) 
being 
the 
most 
frequently 
represented 
species 
!http://www.ncbi.nlm.nih.gov/dbEST 


Donor: 
5. 
splice 
site 
marking 
the 
exon-intron 
boundary 
during 
initiation 
of 
the 
splicing 
process 
and 
commits 
the 
upstream 
exon 
during 
initiation 
of 
spliceosome 
assembly 


E 


Ensembl: 
Genome 
Browser 
of 
the 
European 
Molecularbiological 
Laboratory 
(EMBL) 
and 
the 
European 
Bioinformatics 
Institute 
(EBI), 
based 
on 
a 
gene 
modelling 
pipeline 
that 
integrates 
sequence 
data 
from 
primary 
sources 
of 
more 
than 
~ 
40 
genomes 
(release 
47, 
July 
2008). 
Especially 
suited 
for 
bioinformatic 
analysis 
due 
to 
fast 
access 
of 
sequences 
and 
their 
annotations 
via 
a 
Perl 
programming 
interface 


!http://www.ensembl.org 
EST: 
Expressed 
Sequence 
Tag; 
short 
cDNA 
sequence 
read 
of 
400-600 
nt 
length, 
consitituing 
parts 
of 
a 
mRNA. 
EST 
are 
often 
prepared 
from 
total 
RNA 
after 
cell 
breakdown, 
reverse 
transcription, 
cloning 
and 
sequencing. 
Oligo 
deoxy-thymine 
primer 
or 
poly-dT 
beads 
are 
used 
for 
selectively 
enriching 
messenger 
RNA 
from 
cell 
extracts, 
hence 
EST 
data 
may 
represent 
only 
a 
minor 
fraction 
of 
the 
total 
cellular 
RNA 
among 
which 
mRNA 
makes 
up 
for 
5% 
in 
eucaryotic 
cells 


EvidenceViewer: 
Graphical 
visualisation 
of 
biological 
evidence 
supporting 
a 
particular 
gene 
model. 
Linked 
to 
the 
genomic 
map 
viewer 
of 
GenBank, 
displaying 
all 
RefSeq 
models, 
annotated 
known 
or 
potential 
transcripts 
of 
a 
gene, 
which 
align 
to 
the 
area 
of 
interest 


!http://www.ncbi.nlm.nih.gov/sutils/static/evvdoc.html 
Exon: 
pre-mRNA 
part 
which 
is 
spliced 
into 
the 
mature 
messenger 
RNA. 
As 
determined 
by 
the 
human 
genome 
project, 
the 
mean 
exon 
length 
of 
human 
protein 
coding 
genes 
is 
145 
bp 
(median 
122 
bp), 
see 
also 


G 


GenBank: 
Primary 
database 
of 
publicly 
available 
nucleotide 
sequences 
and 
their 
protein 
translations 
with 
basic 
annotations, 
maintained 
at 
the 
National 
Center 
of 
Biotechnology 
Information, 
Bethesda 
USA. 
GenBank 
stores 
sequences 
produced 
by 
laboratories 
worldwide 
and 
contained 
with 
release 
166 
(June 
2008) 
88,554,578 
sequences 
of 
more 
than 
100,000 
organisms 
!http://www.ncbi.nlm.nih.gov/Genbank/ 


GeneMine: 
Software 
pipeline 
for 
automated 
analysis 
of 
DNA 
and 
protein 
sequences 
by 
integrating 
information 
of 
dierent 
biological 
web 
resources 
and 
making 
them 
available 
for 
mining 
in 
a 
local 
database 


!http://bioinformatics.ucla.edu/genemine 
GENOA: 
Genome 
annotation 
pipeline. 
Performs 
spliced 
alignments 
of 
repeat-masked 
cDNAs 
(BLASTN) 
against 
genomic 
sequence 
to 
nd 
signicant 
loci 
of 
transcription. 
Subsequently, 
cDNAs 
and 
ESTs 
are 
stringently 
(re)aligned 
to 
these 
loci 
by 
the 
algorithms 
mRNAvsGen 
and 
Sim4 
respectively, 
to 
infer 
high 
quality 
exons 
!http://genes.mit.edu/genoa 


Intron: 
pre-mRNA 
part 
which 
is 
spliced 
out 
and 
hence 
missing 
in 
the 
mature 
messenger 
RNA. 
As 
determined 
by 
the 
human 
genome 
project, 
the 
mean 
intron 
length 
of 
human 
protein 
coding 
genes 
is 
3,365 
bp 
(median 
1,023 
bp), 
see 
also 



M 


MegaBlast: 
Speed 
optimised 
alignment 
program 
for 
only 
slightly 
diering 
nucleotide 
sequences. 
By 
using 
a 
"greedy” 
algorithm 
it 
is 
about 
10 
times 
faster 
compared 
to 
BLASTN, 
when 
used 
with 
larger 
word 
sizes 
of 
the 
seed 
alignments 
(over 
16 
nucleotides) 
and 
consequently 
can 
better 
handle 
longer 
sequences 


!http://www.ncbi.nlm.nih.gov/blast/megablast.shtml 
R 


RefSeq: 
Non-redundant, 
manually 
curated 
database 
of 
sequences 
of 
more 
than 
5,000 
dierent 
taxa 
(release 
30 
-Juli 
2008) 
including 
genomic 
DNA, 
transcripts, 
and 
proteins 
with 
stable 
identiers. 
RefSeq 
entries 
are 
derived 
from 
publicly 
available 
archives 
of 
primary 
research 
data 
and 
as 
such 
provide 
a 
synthesis 
of 
information 
!http://www.ncbi.nlm.nih.gov/RefSeq/ 


RS 
domain: 
. 
SR 
proteins 


rss: 
reference 
splice 
site, 
synonym 
for 
constitutive 
splice 
site 
due 
to 
the 
fact 
that 
constitutive 
splice 
sites 
may 
be 
involved 
in 
alternative 
splicing, 
which 
has 
just 
not 
been 
veried 
yet 


S 


Sim4: 
Program 
designed 
for 
rapid 
aligning 
spliced 
transcripts 
against 
genomic 
DNA 
of 
sizes 
>100 
kB. 
The 
algorithms 
nds 
matching 
seeds 
(12-mers) 
and 
extends 
them 
to 
gap-free 
genomic 
HSPs, 
which 
are 
subsequently 
assembled 
to 
an 
exon 
core 
chain 
by 
dynamic 
programming, 
favouring 
HSP 
positions 
at 
GT..AG 
or 
CT..AC 
intron 
boundaries. 


SQL 
storage 
types: 
Dene 
the 
data 
types 
each 
eld 
in 
a 
SQL 
table 
can 
hold. 
This 
is 
an 
important 
issue 
in 
design 
of 
biological 
databases, 
as 
it 
is 
beforehand 
often 
unknown 
how 
large 
certain 
values 
can 
become 
during 
analysis. 
For 
example 
a 
eld 
which 
stores 
intron 
sequences 
(string 
type) 
is 
critically 
initialized 
with 
the 
MySQL 
data 
type 
TEXT 
as 
this 
allows 
only 
the 
storage 
of 
values 
of 
maximal 
L 
=216 
+2 
bytes 
(1 
string 
character 
= 
1 
byte). 


SR 
proteins 
Family 
of 
splicing 
factors 
that 
share 
a 
modular 
structure 
consisting 
of 
one 
or 
two 
copies 
of 
an 
N-terminal 
RRM 
(RNA-recognition 
motif) 
followed 
by 
a 
C-terminal 
domain 
rich 
in 
alternating 
serine 
and 
arginine 
residues, 
termed 
the 
RS 
domain 


T 


TBLASTN: 
Runtime 
intensive 
BLAST 
due 
to 
aligning 
a 
query 
protein 
sequence 
against 
a 
dynamically 
translated 
nucleotide 
sequence 
database 
resulting 
in 
a 
six-fold 
increased 
sequence 
search 
space 
because 
of 
three 
possible 
reading 
frames 
on 
forward 
and 
reverse 
strand 
!http://blast.ncbi.nlm.nih.gov/blast/ 


U 


UCSC 
Browser: 
Via 
a 
webbrowser 
accessible 
graphical 
representation 
of 
many 
eukaryotic 
genome 
sequences 
and 
their 
annotation 
maintained 
at 
the 
University 
of 
California 
Santa 
Cruz. 
Additionally, 
a 
table 
browser 
allows 
direct 
access 
to 
the 
underlying 
database 
!http://genome.ucsc.edu/ 


UniGene: 
Database 
of 
Unied 
clusters 
of 
ESTs 
and 
full-length 
mRNA 
sequences. 
Transcripts 
are 
clustered 
in 
a 
non-redundant 
and 
gene-oriented 
way 
and 
annotated 
with 
related 
information 
(e.g. 
tissue 
type). 
The 
latest 
version 
of 
the 
human 
taxon 
(#214, 
June 
2008) 
contains 
122,958 
clusters 
representing 
more 
than 


6.9 
million 
transcripts 
!http://www.ncbi.nlm.nih.gov/UniGene 

132 
Glossary 



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dierent 
aspects 
of 
spliceosome 
activation. 
Proc 
Natl 
Acad 
Sci 
U 
S 
A 
99(14), 
9145{9149 
Jul 
(2002). 
[279] 
Brenner, 
T. 
J. 
& 
Guthrie, 
C. 
Assembly 
of 
Snu114 
into 
U5 
snRNP 
requires 
Prp8 
and 
a 
functional 
GTPase 
domain. 
RNA 
12(5), 
862{871 
May 
(2006). 
[280] 
Raghunathan, 
P. 
L. 
& 
Guthrie, 
C. 
RNA 
unwinding 
in 
U4/U6 
snRNPs 
requires 
ATP 
hydrolysis 
and 
the 
DEIH-box 
splicing 
factor 
Brr2. 
Curr 
Biol 
8(15), 
847{855 
Jul 
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[281] 
Chung, 
S., 
McLean, 
M. 
R. 
& 
Rymond, 
B. 
C. 
Yeast 
ortholog 
of 
the 
Drosophila 
crooked 
neck 
protein 
promotes 
spliceosome 
assembly 
through 
stable 
U4/U6.U5 
snRNP 
addition. 
RNA 
5(8), 
1042{1054 
Aug 
(1999). 
[282] 
Chen, 
C.-H. 
et 
al. 
Functional 
and 
physical 
interactions 
between 
components 
of 
the 
Prp19p-associated 
complex. 
Nucleic 
Acids 
Res 
30(4), 
1029{1037 
Feb 
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[283] 
Chan, 
S.-P., 
Kao, 
D.-I., 
Tsai, 
W.-Y. 
& 
Cheng, 
S.-C. 
The 
Prp19p-associated 
complex 
in 
spliceosome 
activation. 
Science 
302(5643), 
279{282 
Oct 
(2003). 

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[284] 
Query, 
C. 
C. 
& 
Konarska, 
M. 
M. 
Splicing 
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revisited. 
Nat 
Struct 
Mol 
Biol 
13(6), 
472{474 
Jun 
(2006). 
[285] 
Zhou, 
Z. 
& 
Reed, 
R. 
Human 
homologs 
of 
yeast 
prp16 
and 
prp17 
reveal 
conservation 
of 
the 
mechanism 
for 
catalytic 
step 
II 
of 
pre-mRNA 
splicing. 
EMBO 
J 
17(7), 
2095{2106 
Apr 
(1998). 
[286] 
Schwer, 
B. 
& 
Gross, 
C. 
H. 
Prp22, 
a 
DExH-box 
RNA 
helicase, 
plays 
two 
distinct 
roles 
in 
yeast 
pre-mRNA 
splicing. 
EMBO 
J 
17(7), 
2086{2094 
Apr 
(1998). 
[287] 
Aronova, 
A., 
Backova, 
D., 
Crotti, 
L. 
B., 
Horowitz, 
D. 
S. 
& 
Schwer, 
B. 
Functional 
interactions 
between 
Prp8, 
Prp18, 
Slu7, 
and 
U5 
snRNA 
during 
the 
second 
step 
of 
pre-mRNA 
splicing. 
RNA 
13(9), 
1437{1444 
Sep 
(2007). 
[288] 
James, 
S.-A., 
Turner, 
W. 
& 
Schwer, 
B. 
How 
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and 
Prp18 
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in 
the 
second 
step 
of 
yeast 
pre-mRNA 
splicing. 
RNA 
8(8), 
1068{1077 
Aug 
(2002). 

Danksagung 


Das 
Material 
der 
vorliegenden 
Arbeit 
wurde 
zwischen 
Oktober 
2003 
und 
Marz 
2008 
am 
Lehrstuhl 
fur 
Bioinformatik 
der 
biologisch-parmazeutischen 
Fakultatin 
der 
Friedrich-Schiller 
Universitat 
Jena 
erstellt. 
Mein 
erster 
Dank 
geht 
daher 
an 
Professor 
Stefan 
Schuster, 
der 
mir 
in 
dieser 
Zeit 
sehr 
gute 
Arbeitsbedingungen 
ermoglicht 
hat, 
stets 
mit 
Rat 
und 
Tat 
zur 
Seite 
stand 
und 
mir 
mit 
seinem 
mathematischen 
Verstandnis 
der 
Biologie 
eine 
unerschop
iche 
Quelle 
neuer 
Sichtweisen 
bot. 


Zu 
diesem 
Arbeitsumfeld 
gehorten 
auch 
meine 
engsten 
Kollegen 
Beate 
Knoke, 
Jorn 
Behre, 
Dimitar 
Kenanov, 
Swetlana 
Nikolajewa, 
Kathrin 
Schowtka 
und 
die 
Doktoren 
Ina 
Wei, 
Axel 
von 
Kamp, 
Michael 
Pachkov 
und 
Anja 
Schroter 
denen 
ich 
fur 
die 
angenehme 
Arbeitsatmosphare 
danken 
mochte. 
Beate 
Knoke 
und 
Doktor 
Heike 
Pospisil 
gilt 
hierbei 
mein 
besonderer 
Dank 
fur 
das 
Korrekturlesen 
und 
Feedback 
zum 
ersten 
Kapitel. 
Meinen 
Kollegen 
vom 
FLI 
Jena, 
Stefanie 
Schindler, 
Karol 
Szafranski 
und 
Matthias 
Platzer 
mochte 
fur 
die 
gute 
Zusammenarbeit 
und 
die 
experimentelle 
Unterstutzung, 
sowie 
fur 
die 
interessanten 
Diskussionrunden 
danken. 


Meinen 
warmsten 
Dank 
fur 
viele 
fachlich 
interessante 
Diskussionen 
und 
die 
Eronung 
zahlreicher 
neuer 
Perspektiven 
in 
Bezug 
auf 
meine 
Arbeit 
mochte 
ich 
Doktor 
Dirk 
Holste 
in 
Wien 
ausprechen. 
Von 
ihm 
konnte 
ich 
viel 
zum 
Alternativen 
Spleien 
und 
der 
systematischen 
Analyse 
desselben 
lernen. 


Seit 
vielen 
Jahren 
begleitet 
mich 
Prof. 
Ina 
Koch 
von 
der 
TFH 
Berlin 
in 
der 
Bioniformatik. 
Fur 
ihr 
jederzeit 
oenes 
Ohr 
und 
die 
vielen 
wertvollen 
Hinweise 
zur 
Petri 
Netz 
Theorie 
im 
dritten 
Kapitel, 
mochte 
ich 
Ihr 
meinen 
herzlichsten 
Dank 
aussprechen. 


Wenn 
ich 
etwas 
Wertvolles 
aus 
Jena 
mitnehme, 
so 
ist 
es 
die 
Liebe 
einer 
jungen 
Frau. 
Ihre 
Besonnenheit 
und 
Rationalitat 
hat 
mich 
vor 
mancher 
Verzei
ung 
gerettet 
und 
ohne 
ihren 
motivierenden 
Zuspruch 
ware 
diese 
Arbeit 
gegen 
meine 
eigenen 
kritischen 
Anspruche 
nicht 
zum 
Abschluß 
gekommen. 
Fur 
ihre 
Liebe 
und 
aufopferungsvolle 
Unterstutzung, 
nicht 
nur 
in 
Form 
geduldigen 
Korrekturlesens 
und 
re
ektierender 
Diskussionen, 
bin 
ich 
meinem 
Schatz 
Berit 
zu 
tiefstem 
Dank 
verp
ichtet. 


Allen 
meinen 
Freunden 
danke 
ich 
dafur, 
dass 
sie 
trotz 
der 
wenigen 
Zeit, 
die 
in 
den 
Jahren 
der 
Arbeit 
an 
der 
Dissertation 
blieb, 
zu 
mir 
gehalten 
und 
mich 
hin 
wieder 
ermahnt 
haben, 
das 
normale 
Leben 
nicht 
aus 
den 
Augen 
zu 
verlieren. 


Meinen 
Eltern, 
die 
mir 
das 
Studium 
der 
Biotechnologie 
und 
Bioinformatik 
ermoglichten, 
aber 
auch 
meiner 
engsten 
Familie, 
mochte 
ich 
meinen 
abschlieenden 
Dank 
widmen 
-fur 
alle 
Geduld, 
Liebe 
und 
bedingunslose 
Unterstutzung, 
fur 
die 
Moglichkeit 
in 
Bergfelde 
hin 
und 
wieder 
abschalten“ 
zu 
konnen 
und 
fur 
den 
uner


"

schutterlichen 
Glauben 
an 
eine 
Zeit 
nach 
der 
Promotion. 


147 



148 
Appendix 



Appendix 
A 
Supplements 
to 
Chapter 
1 


A.1 
The 
EASED 
Database 
Scheme 
149 



Tabl
e 
A.1
: 
Descriptio
n 
o
f 
table
s 
withi
n 
th
e 
Ease
d 
databas
e 
tha
t 
wher
e 
use
d 
fo
r 
th
e 
validatio
n 
analyses
. 
Colum
tw
summarize
th
mai
aspect
s 
o
f 
th
e 
dat
a 
eac
h 
tabl
e 
hold
s 
an
d 
colum
n 
thre
e 
list
s 
th
e 
relation
s 
eac
h 
tabl
e 
share
s 
wit
h 
othe
r 
tables
. 
A 
respectiv
diagra
i
give
i
Figur
e 
A.
1 


Tabl
e 
Nam
e 
Descriptio
n 
Relation
(t
othe
tables
genes 
basic 
description 
of 
each 
gene: 
Ensemb
l 
ID, 
chromosome 
and 
position 
therein, 
genomic 
sequence 
(slice)
, 
genes 
features 
(1:n)
trans 
(1:n) 
number 
of 
assigned 
Ensemb
l 
transcripts 
genes 
features 
cross 
referencing 
gene 
identier 
to 
other 
databases 
genes 
(n:1) 
trans 
basic 
description 
of 
Ensemb
l 
transcripts 
matching 
a 
gene: 
transcript 
sequence, 
CDS 
start 
(end) 
within 
genes 
(n:1)
exons 
(1:n)
protein 
features 
(1:n)
genomic 
sequence 
slice, 
CDS 
start 
(end) 
within 
transcript 
sequence, 
strand 
orientation, 
number 
of 
ss 
trans 
features 
(1:n)
asfs 
unique 
(1:n)
hsps 
pairs 
(introns)
, 
number 
of 
matching 
alternative 
ESTs,number 
of 
matching 
constitutive 
ESTs 
(1:n)
, 
as 
hsps 
(1:n)
trans 
tissue 
(1:n) 
trans 
features 
cross 
referencing 
transcript 
identier 
to 
other 
databases 
trans 
(n:1) 
trans 
tissue 
referencing 
numbers 
of 
alternative 
(constitutive) 
ESTs 
grouped 
by 
tissue 
types 
that 
coincide 
with 
an 
trans 
(n:1)
est 
tissue 
(m:1) 
Ensemb
l 
transcript 
protein 
features 
cross 
references 
to 
functional 
domain 
databases, 
annotation 
of 
domain 
start 
(end) 
in 
the 
protein 
sequence 
trans 
(n:1) 
exons 
basic 
description 
of 
Ensemb
l 
exons 
making 
up 
a 
complete 
transcript: 
start 
(end) 
witin 
genomic 
sequence, 
exon 
end 
positions 
within 
CDS 
asfs 
unique 
basic 
description 
of 
AS 
events: 
type 
classication 
a-
h 
(see 
Figure 
1.2)
, 
length, 
quality, 
position 
of 
donor 
trans 
(n:1)
asfs 
tissue 
(1:n)
asfs 
ce 
(1:n)
(acceptor) 
site 
within 
transcript 
and 
genomic 
sequence, 
donor 
(acceptor) 
dinucleotides, 
number 
of 
further 
asfs 
ae 
(1:n) 
alternative 
(constitutive) 
ESTs 
coinciding 
with 
the 
AS 
event 
asfs 
tissue 
referencing 
numbers 
of 
alternative 
(constitutive) 
ESTs 
grouped 
by 
tissue 
types 
that 
coincide 
with 
an 
as 
asfs 
ae 
(n:1)
est 
tissue 
(m:1) 
event 
asfs 
ae 
extended 
description 
of 
AS 
events: 
EST 
ID, 
type 
(insert, 
skip)
, 
quality, 
start 
(end) 
of 
alternative 
part 
asfs 
unique 
(n:1)
hsps, 
asfs 
tissue 
(1:n)
ests 
within 
EST 
sequence 
(skip/insert 
length)
, 
strand 
orientation, 
average 
EST 
identity, 
overlap, 
reference 
(n:1) 
to 
the 

anking 
hsp's 
asfs 
ce 
extended 
description 
of 
reference 
splice 
events: 
EST 
ID, 
quality, 
start 
(end) 
of 
constitutive 
part 
within 
asfs 
unique 
(n:1)
hsps, 
asfs 
tissue 
(1:n)
ests 
EST 
sequence, 
spanning 
the 
alternative 
part 
(hence 
representing 
the 
exonic 
part 
of 
the 
Ensemb
l 
tran(
n:1) 
script 
as 
hsps 
as 
table 
asf
s 
a
e 
but 
with 
referene 
to 
Ensemb
l 
transcript 
ID 
trans 
(n:1)
ests 
(n:1) 
hsps 
basic 
description 
of 
EST:mRNA 
(Ensemb
l 
transcript) 
alignments: 
identity, 
mismatches, 
gaps, 
start 
trans 
(n:1)
ests 
(n:1)
asfs 
ae 
(end) 
position 
within 
query 
(EST) 
sequence, 
start 
(end) 
within 
subject 
(mRNA) 
sequence, 
evalue, 
bitscore 
ests 
tissue 
description 
of 
available 
tissue 
type 
asfs 
tissue 
(1:m)
trans 
tissue 
(1:m) 
ests 
library 
besic 
description 
of 
EST 
libraries: 
UniGen
e 
library 
ID, 
library 
name-and 
description, 
reference 
to 
tissue 
ests 
(1:n)
ests 
tissue 
(1:1) 
ID, 
developmental 
stage-and 
disease 
status 
annotation 


The 
EASED 
Database 
Scheme 



1:n1:11:1n:11:nm:11:n1:m1:nn:11:nn:11:n1:n1:n1:n1:n1:nn:1n:11:n1:ntrans_tissuetrans_id: VARCHAR(20) (FK)
tissue_id: INTEGER (FK)
type_2: ENUM('ce','ae')
anz: INT(10)
asfs_tissueasf_id: INTEGER(10) (FK)
tissue_id: INTEGER (FK)
type_2: ENUM('ce','ae')
anz: INT(10)
trans_featurestrans_id: VARCHAR(20) (FK)
types: VARCHAR(32)
acc_id: VARCHAR(64)
trans_idxtrans_idtype_idxtypesacc_idxacc_idtranstrans_id: VARCHAR(20)
gene_id: VARCHAR(20) (FK)
version: SMALLINT(5)
ext_name: VARCHAR(32)
description: TINYTEXTseq: MEDIUMTEXTprotein_seq: TEXTstrand: TINYINT(4)
dna_cds_start: INTEGER(10)
dna_cds_end: INTEGER(10)
cds_start: SMALLINT(5)
cds_end: SMALLINT(5)
nae: INTEGER(10)
nce: INTEGER(10)
nss: SMALLINT(5)
ae_cancer: SMALLINT(5)
ae_health: SMALLINT(5)
ae_disease: SMALLINT(5)
ae_adult: SMALLINT(5)
ae_embryo: SMALLINT(5)
ae_newborn: SMALLINT(5)
ae_juvenile: SMALLINT(5)
ae_pooled: SMALLINT(5)
ce_cancer: SMALLINT(5)
ce_health: SMALLINT(5)
ce_disease: SMALLINT(5)
ce_adult: SMALLINT(5)
ce_embryo: SMALLINT(5)
ce_newborn: SMALLINT(5)
ce_juvenile: SMALLINT(5)
ce_pooled: SMALLINT(5)
ext_name_idxext_namegene_idxgene_idnae_idxnaenss_idxnssprotein_featurestrans_id: VARCHAR(20) (FK)
name: VARCHAR(32)
annot: TINYINT(4)
types: VARCHAR(32)
prot_start: INTEGER(10)
prot_end: INTEGER(10)
name_idxnametypes_idxtypestrans_idxtrans_idhspshsp_id: INTEGER(10)
trans_id: VARCHAR(20) (FK)
est_id: VARCHAR(8) (FK)
identity: FLOATlength: SMALLINT(5)
mismatch: SMALLINT(5)
gaps: SMALLINT(5)
qb: SMALLINT(5)
qe: SMALLINT(5)
sb: SMALLINT(5)
se: SMALLINT(5)
evalue: FLOATbitscore: FLOATtrans_idxtrans_idest_idxest_idgenes_featuresgene_id: VARCHAR(20) (FK)
acc_id: VARCHAR(64)
types: VARCHAR(32)
gene_idxgene_idtype_idxtypesacc_idxacc_idgenesgene_id: VARCHAR(20)
version: TINYINT(3)
ext_name: VARCHAR(32)
description: TINYTEXTtaxon_id: SMALLINT(5)
chr: VARCHAR(16)
chr_start: INTEGER(10)
chr_end: INTEGER(10)
seq: LONGTEXTtrans_count: SMALLINT(5)
ext_name_idxext_namechr_idxchrexonstrans_id: VARCHAR(20) (FK)
exon_id: CHAR(20)
dna_start: INTEGER(10)
dna_end: INTEGER(10)
cds_end: INTEGER(10)
ests_tissuetissue_id: INTEGERtissue: SMALLINT(5)
ests_librarylib_id: INTEGER(10)
name: VARCHAR(128)
description: TEXTtissue_id: INTEGER (FK)
webtissue_id: SMALLINT(5)
development: ENUMdisease: ENUM('cancer','health','disease','...
webtissue_id_idxwebtissue_idtissue_idxtissue_idestsest_id: VARCHAR(8)
version: TINYINT(4)
gi_id: INTEGER(11)
length: SMALLINT(5)
seq: TEXTlib_id: INTEGER(10) (FK)
gi_idxgi_idlib_idxlib_idasfs_uniqueasf_id: INTEGER(10)
trans_id: VARCHAR(20) (FK)
types: SMALLINT(6)
length: INTEGER(10)
quality: ENUM('1','2','3')
donor_mrna_pos: MEDIUMINT(8)
donor_dna_pos: INTEGER(10)
donor_exon: SMALLINT(5)
donor_dinucleotide: CHAR(2)
acceptor_mrna_pos: MEDIUMINT(8)
acceptor_dna_pos: INTEGER(10)
acceptor_exon: SMALLINT(5)
acceptor_dinucleotide: CHAR(2)
nae: INTEGER(10)
nce: INTEGER(10)
ae_cancer: SMALLINT(5)
ae_health: SMALLINT(5)
ae_disease: SMALLINT(5)
ae_adult: SMALLINT(5)
ae_embryo: SMALLINT(5)
ae_newborn: SMALLINT(5)
ae_juvenile: SMALLINT(5)
ae_pooled: SMALLINT(5)
ce_cancer: SMALLINT(5)
ce_health: SMALLINT(5)
ce_disease: SMALLINT(5)
ce_adult: SMALLINT(5)
ce_embryo: SMALLINT(5)
ce_newborn: SMALLINT(5)
ce_juvenile: SMALLINT(5)
ce_pooled: SMALLINT(5)
ae_lib_anz: SMALLINT(5)
ce_lib_anz: SMALLINT(5)
cds_type: SMALLINT(6)
typestypesqualityqualitynaenaencencetrans_idxtrans_idasfs_ceasf_id: INTEGER(10) (FK)
est_id: VARCHAR(8) (FK)
quality: TINYINT(4)
cds_start: MEDIUMINT(8)
cds_end: MEDIUMINT(8)
est_strand: DECIMAL(1,0)
est_identity: DECIMAL(5,2)
hsp_id: INTEGER(10) (FK)
hsp_id_idxhsp_idasfs_aeasf_id: INTEGER(10) (FK)
est_id: VARCHAR(8) (FK)
types: ENUM('ins','skip')
quality: ENUM('1','2','3')
cds_start: MEDIUMINT(8)
cds_end: MEDIUMINT(8)
gap_length: SMALLINT(5)
overlap: SMALLINT(6)
est_strand: ENUM('-1','1')
est_identity: TINYINT(3)
hsp1_id: INTEGER(10)
hsp2_id: INTEGER(10)
as_hspsas_hsp_id: INTEGER(10)
trans_id: VARCHAR(20) (FK)
est_id: VARCHAR(8) (FK)
quality: ENUM('1','2','3','4')
types: ENUM('skip','ins')
cds_start: INTEGER(11)
cds_end: INTEGER(11)
gap_length: INTEGER(11)
overlap: INTEGER(11)
est_strand: ENUM('-1','1')
est_identity: TINYINT(3)
hsp1_id: INTEGER(11)
hsp2_id: INTEGER(11)
trans_idxtrans_idest_idxest_id1
Appendix 
151 


Figur
e 
A.1
: 
Entit
y 
relationshi
p 
diagra
m 
o
f 
th
e 
EASE
D 
databas
e 
(v19_Gene-an
d 
Transcrip
t 
ID
s 
ar
e 
base
o
th
Ensemb
freez
o


34a)
. 


Decembe
r 
200
3 



The 
EASED 
Database 
Scheme 



Appendix 
B 
Supplements 
to 
Chapter 
2 


B.1 
Sim4 
Alignment 
Errors 
153 



Sim4 
Alignment 
Errors 


Table 
B.1: 
A 
general 
problem 
of 
aligning 
cDNAs 
and 
ESTs 
against 
genomic 
DNA 
is 
to 
correctly 
map 
transcript 
sequences. 
Posttranscriptional 
modications 
and 
in 
vitro 
trans
cription 
errors 
(1-4% 
in 
ESTs 
(256)) 
may 
aect 
alignment 
programs 
and 
to 
output 
genomic 
coordinates 
of 
erroneously 
aligned 
transcript 
blocks. 
Subsequently, 
this 
may 
introduce 
a 
bias 
in 
exon 
datasets 
derived 
from 
such 
alignment 
approaches. 
Generally, 
Sim4 
is 
a 
very 
fast 
and 
capable 
alignment 
algorithm 
that 
has 
been 
successfully 
used 
for 
almost 
a 
decade 
(91). 
However, 
a 
recent 
comparison 
of 
alignment 
programs 
revealed 
apparent 
weaknesses 
in 
the 
correct 
alignment 
of 
mismatches 
or 
insertions/deletion 
near 
the 
splice 
junction 
(264). 
Splicing 
analysis 
is 
crucially 
depending 
on 
correct 
alignments 
of 
transcribed 
sequences, 
esp
ecially 
near 
alternative 
splice 
sites: 
Thus, 
the 
two 
best-performing 
programs 
according 
to 
a 
report 
by 
Zhang 
and 
Gish 
(264) 
(EXALIN 
and 
BLAT 
) 
were 
applied 
for 
validating 
initial 
SIM4 
alignments. 
In 
order 
to 
demonstrate 
the 
ltering 
capabilities 
of 
EXALIN, 
the 
following 
examples 
of 
an 
erroneous 
and 
correct 
A5E4 
splice 
site 
prediction 
are 
shown 
for 
illustration 
below 


(a) 
Erroneous 
SIM4 
alignment:Filtering 
alignments 
for 
canonical 
/GT 
and 
AG/ 
splice 
sites, 
as 
freq
uently 
applied 
to 
prepare 
data 
sets 
of 
alternatively 
spliced 
exons, 
is 
not 
sucient 
to 
eectively 
remove 
false-positive 
alignments. 
(Incorrect 
aligned 
nucleotides 
are 
marked 
in 
red. 
DISTAL 
SPLICE 
SITE 


Sequence 
SIM4 
(True 
positives) 
EXALIN 
(True 
positives) 
ID 
BI037972 
CAGAAGCCAAAATG 
AGGTTGAAGGCTGC 
CAGAAGCCAAAATG 
AGGTTGAAGGCTGC 
||||||||||||||>>> 
>>>|||||||||||||| 
||||||||||||||<<<<<< 
<<<<<<|||||||||||||| 
ENSG146592 
CAGAAGCCAAAATGGTA 
CAGAGGTTGAAGGCTGC 
CAGAAGCCAAAATGGTAAGT 
TTTCAGAGGTTGAAGGCTGC 
PROXIMAL 
SPLICE 
SITE 
Sequence 
SIM4 
(False 
positives) 
EXALIN 
(True 
negatives) 
ID 
BQ367677 
AAATGAGTTGGAAG 
GCTGCATTGACTCA 
CAGAAGCCAAAATG 
A-GTTGGAAGGCTG 
|||||-|-|--||->>> 
>>>|||||||||||||| 
||||||||||||||<<<<<< 
<<<<<<| 
||| 
|||||||| 
ENSG146592 
AAATG 
G 
T 
AA 
GTA 
AAGGCTGCATTGACTCA 
CAGAAGCCAAAATGGTAAGT 
TTTCAGAGGTT-GAAGGCTG 


(b) 
Correct 
SIM4 
alignment: 
The 
4 
exon 
extension 
is 
marked 
in 
bold 
letters. 
DISTAL 
SPLICE 
SITE 


Sequence 
ID 
SIM4 
(True 
positives) 
EXALIN 
(True 
positives) 
BF871212 
ENSG102878 
ACTTCATCCAGTCG 
||||||||||||||>>> 
ACTTCATCCAGTCGGTA 
GAAGAGAAGATGGA 
>>>|||||||||||||| 
AAGGAAGAGAAGATGGA 
ACTTCATCCAGTCG 
||||||||||||||<<<<<< 
ACTTCATCCAGTCGGTAGGT 
GAAGAGAAGATGGA 
<<<<<<|||||||||||||| 
TAAAAGGAAGAGAAGATGGA 
PROXIMAL 
SPLICE 
SITE 
Sequence 
ID 
SIM4 
(True 
positives) 
EXALIN 
(True 
positives) 
AW841572 
ENSG102878 
CATCCAGTCGGTAG 
||||||||||||||>>> 
CATCCAGTCGGTAGGTT 
GAAGAGAAGATGGA 
>>>|||||||||||||| 
AAGGAAGAGAAGATGGA 
CATCCAGTCGGTAG 
||||||||||||||>>>>>> 
CATCCAGTCGGTAGGTTTGT 
GAAGAGAAGATGGA 
>>>>>>|||||||||||||| 
TAAAAGGAAGAGAAGATGGA 



Appendix 
155 


B.2 
A5E4 
Splice 
Site 
Scores 
in 
M. 
musculus 
Proximal5´ssDistal5´ssDp
A5EdP
A5E-40-30-20-10010-30-20-10010ProximalMajor-40-30-20-10010-30-20-10010DistalMajorAB
Figure 
B.1: 
Scatter 
plots 
of 
50ss 
scores 
of 
competitive 
and 
tandem 
donors 
extracted 
from 
mouse 
M.musculus. 
(A) 
and 
(B) 
show 
the 
individual 
and 
mean 
scores 
(the 
latter 
is 
marked 
by 
solid/dashed 
lines) 
for 
P4 
and 
D4 
splicing 
exons, 
respectively. 
Scattered 
data 
and 
mean 
scores 
are 
in 
accord 
with 
human 
P4 
and 
D4 
splicing 
exons. 



Base 
Composition 
of 
Tandem 
Splice 
Sites 


B.3 
Base 
Composition 
of 
Tandem 
Splice 
Sites 
Table 
B.2: 
Weblogo 
(95) 
representations 
of 
human 
A5Es 
and 
A3Es 
with 
exon 
extensions 
E 
=3, 
4, 
· 
, 
20 
nucleotides, 
in 
a 
sequence 
windows 
20nt 
up-and 
downstream 
of 
the 
alternative 
sequence 
region. 
The 
middle 
column 
shows 
for 
A5Es 
the 
high 
degeneracy 
of 
splice 
sites, 
while 
the 
last 
column 
shows 
for 
A3Es 
the 
degeneracy 
of 
the 
pyrimidine-tract 
that 
is 
increasingly 
less 
present 
with 
increasing 
extension 
for 
the 
proximal 
30ss, 
and 
more 
present 
for 
E> 
12 
nucleotides 
for 
the 
distal 
30ss. 


Exon 
Extension 
Alternative 
5. 
Splice 
Site 
Alternative 
3. 
Splice 
site 
[nt] 
Exons 
Exons 


weblogo.berkeley.edu012bits5¢
12345678910111213141516171819TA20TAG21G22CT23TGA24CTGA25G26CT27TGA28CTA29ATCG30CGT31323334353637383940414243443¢
weblogo.berkeley.edu012bits5¢
1T23AGCT4ACT5CT6GACT7AGCT8AGCT9AGCT10GACT11GACT12GACT13AGCT14GACT15GACT16GCT1718ATC19A20G21CAG22TAC23A24G25TGA26AT2728293031323
34353637383940414243443¢
weblogo.berkeley.edu012bits5¢
12345678910111213141516171819TA20TAG21G22CT23TGA24CTGA25G26CT27TGA28CTA29ATCG30CGT31323334353637383940414243443¢
weblogo.berkeley.edu012bits5¢
1T23AGCT4ACT5CT6GACT7AGCT8AGCT9AGCT10GACT11GACT12GACT13AGCT14GACT15GACT16GCT1718ATC19A20G21CAG22TAC23A24G25TGA26AT2728293031323
34353637383940414243443¢
weblogo.berkeley.edu012bits5¢
123456G7891011A12A13141516171819CA20ATG21G22CT23TCGA24GCTA25TCAG26G27CT28CGA29CGTA30TG31ACT323334AT35363738394041T42GCT4344T453¢
weblogo.berkeley.edu012bits5¢
1ACT2AGCT3GCT4GACT5AGCT6AGCT7GCT8AGCT9AGCT10AGCT11GACT12GACT13GACT14AGCT15GACT16GACT17TC18GATC19A20G2122TA23GTAC24A25G26AGC27289303132333435363738394041424344453¢
weblogo.berkeley.edu012bits5¢
123G45678910GCA11121314AGC151617ACG18GCA19TGCA20ATG21G22CT23CTGA24TGCA25TGA26CATG27G28T29CGA30CTGA31ACTG32CGAT3334CT35363738394
4142ATC43T4445463¢
weblogo.berkeley.edu012bits5¢
1GCAT2345T6ACT7GACT8T9GACT10AGCT11GACT12AGCT13GACT14GACT15AGCT16AGCT17CT18ATC19A20G21CTG22GCAT2324ATC25A26G27TAG2829G30313233345363738394041G42434445463¢
weblogo.berkeley.edu012bits5¢
1234TA5CAG6C7891011G12CAG13141516CTA171819GCTA20TAG21G22T23TCGA2425GCA26GTA27TAG28G29T30CGA31GA32TACG33343536373839404142GAT43C
G44CAG4546473¢
weblogo.berkeley.edu012bits5¢
1T23GCT4AT5CAGT6GT7ACT89ACT10GACT11GCT12AGCT13GCT14GACT15ACT16GTC17GACT18TC19A20G2122GAT23T24A25GATC26A27G28GA2930313233GCA343563738AT3940414243444546473¢
weblogo.berkeley.edu012bits5¢
12ACG34CAG5678TGC9GAC10GCT111213AGCT1415TA161718CA19CTA20CTAG21G22CT23TCGA2425TAG2627TGCA28TG29G30T31AG32TCGA33TACG3435363738A3
40414243TC4445T4647483¢
weblogo.berkeley.edu012bits5¢
12TC34GACT5ACT6CT7GCT8CT9GCT10GACT11ACT12ACT13ACT14GACT15AGCT16GCT17GCT18ATC19A20G21CG22CAT2324CAT25AGT26AGTC27A28G29TAG30GCA31233343536TGA3738CAG394041424344CGA454647483¢
weblogo.berkeley.edu012bits5¢
123GAC456789101112131415161718GAC19TCGA20TAG21G22CT23CGA24GTCA25TAG2627A28GCTA29TAG30G31CT32CTGA33CTGA34CATG35AGT3637GT38394041
T4243444546T4748493¢
weblogo.berkeley.edu012bits5¢
1GACT2GCT34ACT5CT6GT7AGCT8GCT9GACT10GACT11GACT12GACT13ACT14ACT15ACT16AGCT17AGCT18TC19A20G21G22GCAT232425GCAT2627TAC28A29G30AG31
CA32TG3334CAT35CTAG36CG37TGA3839404142CA4344G45464748493¢
weblogo.berkeley.edu012bits5¢
12TGAC3456789CG10TGC1112G131415161718AC19CTGA20TCAG21G22CT23TGA24TCA25TCAG262728GCA29TGA30TAG31G32T33TGA34GCTA35ACG36GCT37GA383
40ATC4142434445TCG464748AGT49CGT50ATG3¢
weblogo.berkeley.edu012bits5¢
12345GACT6AGT78ACT9T10ACT11ACT12GCAT13ATC14GCT15GCT16GACT1718ACT19A20G21TGA22GAT23T242526T27AGC28GTAC29A30G31A32333435TG3637383
40TGCA41GCTA42ACG43444546474849503¢
weblogo.berkeley.edu012bits5¢
12345678CG9A101112ATC131415161718TAC19TCGA20TAG21G22T23TGA24GCTA25CTAG26272829C30CTGA31TAG32G33T34TGA35CGTA36CTAG37CAGT38A3940T
G4142CT4344CT454647484950513¢
weblogo.berkeley.edu012bits5¢
1GACT23456ACT7TC8ACT910CAT11CT1213GTC1415ACT16ACT17CAGT18GATC19A20G2122232425GCT26272829C30A31G32CGA33343536TAC37AGT3839404142G
TA43444546GA47G484950513¢
weblogo.berkeley.edu012bits5¢
123456789101112C13141516A1718GAC19GTCA20ATG21G22CT23CGA24TGCA25CATG2627282930C31TCGA32CATG33G34T35CGA36GCTA37TCAG38ACGT39TGA404
4243G44TG454647484950CTG51523¢
weblogo.berkeley.edu012bits5¢
1TC23CAT4CT5CAT6CT7CT89T10GCAT1112T13ACT14ACT15GTC16GACT17ATC18GATC19A20G2122GACT2324GCT25GACT26CT27GCT28ACT2930GATC31A32G33AG3
T353637A3839404142434445464748495051523¢
weblogo.berkeley.edu012bits5¢
12TAG3456AGC7891011ACT12131415TCA161718TAGC1920CAG21G22CT23TAG24252627TCGA28293031AC32CA33TAG34G35T36TGA37TCA38AG3940414243TCG4
4546CAGT474849505152533¢
weblogo.berkeley.edu012bits5¢
123456CTA78910CAT111213CTA14CT15ACT1617CT1819A20G2122GAT23CAT24GCT25GCT26AT2728GCT29CT3031TAC32A33G34TGA3536373839404142TAG43445GA46GTA474849505152533¢
3nt 


4nt 


5nt 


6nt 


7nt 


8nt 


9nt 


10nt 


11nt 


12nt 


13nt 


continued 
next 
page 



Appendix 
157 


Table 
B.2: 
Weblogo 
(95) 
representations 
of 
human 
A5Es 
and 
A3Es 
with 
exon 
extensions 
E 
=3, 
4, 
· 
, 
20 
nucleotides, 
in 
a 
sequence 
windows 
20nt 
up-and 
downstream 
of 
the 
alternative 
sequence 
region. 
The 
middle 
column 
shows 
for 
A5Es 
the 
high 
degeneracy 
of 
splice 
sites, 
while 
the 
last 
column 
shows 
for 
A3Es 
the 
degeneracy 
of 
the 
pyrimidine-tract 
that 
is 
increasingly 
less 
present 
with 
increasing 
extension 
for 
the 
proximal 
30ss, 
and 
more 
present 
for 
E> 
12 
nucleotides 
for 
the 
distal 
30ss. 


Exon 
Extension 
Alternative 
5. 
Splice 
Site 
Alternative 
3. 
Splice 
site 
[nt] 
Exons 
Exons 


weblogo.berkeley.edu012bits5¢
123TAC4GA5CAG6GCA78910111213141516A171819GTA20TG21G22T23TGA24CTA25TAG2627GAT2829ATG3031TG32GA33GTA34TAG35G36CT37TCGA3839ATCG40C
T41CGT42AT43CAT444546GAT474849GCT50T51TA5253543¢
weblogo.berkeley.edu012bits5¢
1CGT2ACT3GCT45GACT6ATC7ACT8CAGT9GCAT10GACT11ACT12ACT131415TAC16ATC17TCA18ATC19A20G212223GCT2425GACT26TC27CAT28CAGT29GCT30GCT313
ATC33A34G35ACG36GCT3738ACT394041G424344454647484950515253543¢
weblogo.berkeley.edu012bits5¢
123456789101112131415161718A19TCA20CATG21G22CT23TAG24TCGA25CTAG26T27282930313233CA34GTCA35ATG36G37T38TCGA39GCTA40CATG41CAT42GA4
44454647T48CT49T5051525354553¢
weblogo.berkeley.edu012bits5¢
123456ACT78ACT9T1011CAT12CT13141516CAT1718ATC19A20G2122AGCT23GCT24ACT25GACT26CGT27CT28GACT29GCT30AGCT31ACT3233GATC34A35G36CTAG3
3839404142434445464748495051525354553¢
weblogo.berkeley.edu012bits5¢
1GC23GA4GCT567G8G91011AG12TGAC13141516171819TCA20CAG21G22CT23CAG24AC252627282930313233ATG3435TGA36ATG37G38T39CGA40CTA41CAG42AGC
4344AGT45464748G49TACG505152AGT535455ATG563¢
weblogo.berkeley.edu012bits5¢
1TAC23TAC4TC5AGT6TAC78AC9TAC101112ATC1314TCA15GTAC16TCA17CT18ATC19A20G212223C24GACT25AGT2627ATC28ACT2930ACT31ACT32GTC33TAC34GTA
35A36G37ACG383940AC41CTA4243TAGC44TGA4546TGAC474849GA5051AC52535455563¢
weblogo.berkeley.edu012bits5¢
1234ATG5TAG6GTA789ATC10TCA11A121314151617TA18AC19GCA20TAG21G22CT2324GCA25GCA2627TGCA2829TCA3031C32CA3334TCAG35GCA36TGA37TAG38G3
T40TGA41GTA42ATG43T44A45464748TA495051CGA52TG53ATC54A5556573¢
weblogo.berkeley.edu012bits5¢
12345CT67GCT89GACT10GTC11GCT12ACGT1314ATC15TAC16GCT1718TAC19A20G21222324GTC25ACT26T27GACT28GACT29GACT30TC31GACT32GTC33GCT34AGC3
AGTC36A37G38CGA39GCT40414243AG44454647484950GAC5152TGA53CAG5455G56573¢
weblogo.berkeley.edu012bits5¢
123GCA45678GCA9A101112131415CGT16AC171819TCGA20ATG21G22CT23TGA24TGCA25TAG26272829GTC303132333435TC36CA37GTA38CAG39G40CT41TGA42T
CA43TAG44TAG454647TAC48495051525354TGC555657ACG583¢
weblogo.berkeley.edu012bits5¢
123T4CGT5TC6ATC78910CAT11CT12ACT1314A15GTC16ACT1718ATC19A20G2122232425ACT2627ATC28CT29GACT30CT31GCT32GTC33ATC34AGCT3536GTC37A38
39TAG4041424344454647ACG48495051525354555657583¢
weblogo.berkeley.edu012bits5¢
1AC2ATC3456789CGA10CA11TGC1213141516171819GA20TAG21G22CT2324GCA2526272829303132AGC333435GCA36ATC37AC38TGCA39ATG40G41T42GA43GCTA4TCAG45GTA46474849TA5051525354555657CT58ACT593¢
weblogo.berkeley.edu012bits5¢
123CT4CT567ACT89CT10GCT11CT12ACT13TC14GCT15GACT1617TA18ATC19A20G21TC2223CT24252627ACT28ATC29AGCT30GCT31ACT32T33GTC34TGAC35GACT3
CGT37GATC38A39G40CAG414243GCA4445C46GAT4748T49505152535455C5657CTG58TAG59TGCA3¢
weblogo.berkeley.edu012bits5¢
1GC2TAC3TG4567AG891011121314151617181920CTG21G22CT23CGA242526TA27282930GAC3132333435TCG3637TGC38AGC39TGCA40AG41G42CT434445TAG46748CGA49505152CAG53GC5455CG56575859603¢
weblogo.berkeley.edu012bits5¢
1CGAT2T34ACT567GACT8GCT9CAT10GACT11CAT12CAT13CAT1415GCAT16GACT1718ATC19A20G212223TC2425GACT26CT27T28ACT2930ACT31AGCT3233GCAT343
CT36ACT3738ATC39A40G41AG424344454647484950515253545556CGA57AC58G59GA60CTA3¢
14nt 


15nt 


16nt 


17nt 


18nt 


19nt 


20nt 



Base 
Composition 
of 
Tandem 
Splice 
Sites 



Appendix 
C 
Supplements 
to 
Chapter 
3 


C.1 
Description 
of 
Reactions 
of 
the 
Spliceosomal 
Model 
Network 
159 



Tabl
e 
C.1
: 
Reaction
s 
(transitions
) 
involve
d 
i
n 
spliceosom
e 
assembl
y 
(ac
t 
= 
activated
, 
as
s 
= 
assigned
, 
bd
g 
= 
binding
con
converted
di
= 
dimerized
, 
hydro
l 
= 
hydolized
, 
inhi
b 
= 
inhibited
, 
matu
r 
= 
maturated
, 
phosp
h 
= 
phosphorylated
, 
sta
b 
= 
szabilized
st
stemloop
uw
unwound
, 
tri-snRN
P 
= 
U4/U5/U
6 
subcomplex
) 


Descriptio
n 
ID 
Labe
l 
Reactio
n 
Reference
E 
Comple
x 
Reaction
s 
Assembly 
of 
U1 
speci
c 
factors 
with 
U1 
core 
components 
1 
t136.U
1 
matu
r 
U1C 
+ 
U1A 
+ 
U170K 
U1snRNA 
U1 
(185) 
Base 
pairing 
between 
U1 
5
. 
end 
of 
the 
U1 
snRNA 
and 
strong 
intron 
50ss 
2 
t41.U
1 
5s
s 
bd
g 
U1 
+ 
5ss 
. 
U1 
5ss 
(265, 
168) 
(consensus 
donor) 
via 
U1C:5
. 
ss 
contacts 
Interaction 
of 
phosphorylated 
ASF/SF2 
(regiospeci
c 
phosporylation 
by 
3 
t143.AS
F 
phosp
h 
ASF 
SF2 
+ 
ATP 
+ 
SRPK1 
ASFp 
(266, 
267) 
SRPK1) 
with 
U170K 
enhances 
recognition 
of 
weak 
50ss. 
Also 
TIA1 
SRPK1 
+ 
ADP 
promotes 
binding 
of 
U1 
snRNP 
to 
weak 
5
. 
ss 
which 
may 
happen 
in 
concert 
or 
independent 
of 
ASF/SF2. 
4 
t141.ASF
p 
e
x 
bd
g 
ASFp 
+ 
5ss 
ex 
. 
ASFp 
ex 
(86, 
87) 
5 
t12.ASF
p 
U170
K 
bd
g 
ASFp 
ex 
+ 
U1 
+ 
5ss 
. 
ASFp 
U1 
5ss 
(268, 
169) 
6 
t9.U1
C 
TIA
1 
bdg
1 
ASFp 
U1 
5ss 
+ 
TIA1 
int 
U1 
5ss 
(242, 
243, 
170) 
LUC7 
links 
Cap-Binding-Complex 
(CBC) 
to 
the 
U1 
snRNP 
via 
interaction 
with 
U2AF65 
when 
splicing 
the 
50terminal 
exon 
7 
t137.U
1 
CB
C 
bd
g 
CBC 
+ 
U1 
+ 
LUC7 
+ 
U2AF 
U1 
CBC 
(230) 
8 
t10.U
1 
CB
C 
5s
s 
bd
g 
U1 
CBC 
+ 
5ss 
. 
U1 
5ss 
(269) 
TIA1 
binds 
near 
5
. 
ss 
and 
stabilizes 
U1 
snRNP 
recruitment 
via 
direct 
interaction 
with 
U1C 
when 
splicing 
weak 
50ss 
or 
in 
absence 
of 
cap 
on 
5
. 
9 
t131.TIA
1 
5s
s 
in
t 
bd
g 
TIA1 
+ 
5ss 
int 
. 
TIA1 
int 
(242, 
243) 
exon 
1
0 
t30.U1
C 
TIA
1 
bdg
2 
U1 
+ 
5ss 
+ 
TIA1 
int 
. 
U1 
5ss 
(170) 
U2AF65 
dimerizes 
with 
U2AF35. 
U2AF35 
recognizes 
and 
binds 
30ss 
1
1 
t6.U2A
F 
di
m 
U2AF65 
+ 
U2AF35 
. 
U2AF 
(270) 
(particular 
for 
weak 
PPT)
. 
Cooperative 
binding 
of 
U2AF65 
to 
the 
PPT 
and 
SF1(BBP) 
to 
the 
branch 
point. 
Interaction 
between 
SF1 
and 
U2AF65 
after 
1
2 
t0.U2AF3
5 
3s
s 
bd
g 
U2AF 
+ 
3ss 
+ 
PPT 
. 
U2AF 
PPT 
3ss 
(165, 
174, 
U2AF35 
recognized 
the 
30ss 
270) 
1
3 
t7.SF
1 
BP
S 
bd
g 
SF1 
+ 
BPS 
. 
SF1 
BPS 
14 
t14.SF
1 
U2A
F 
bd
g 
SF1 
BPS 
+ 
U2AF 
PPT 
3ss 
SF1 
U2AF 
(163, 
174) 
U1snRNP 
denes 
50ss 
and 
U2AF65 
binding 
to 
PPT, 
U2AF65 
is 
anchored 
as 
1
5 
t59.U
1 
SF
1 
U2A
F 
bd
g 
U1 
SF1 
+ 
U2AF 
PPT 
3ss 
U1 
SF1 
U2AF 
(271) 
heterodimer 
via 
U2AF35 
at 
the 
30ss 
FBP11/Prp40 
bridges 
U1 
and 
SF1 
U2AF 
and 
establishes 
cooperativity 
between 
5
. 
and 
30ss 
recognition. 
SC35 
can 
also 
be 
involved 
in 
bridging 
5
. 
and 
30ss 
via 
interactions 
of 
U170K 
and 
U2AF35 
1
6 
t58.U
1 
SF
1 
bd
g 
U1 
5ss 
+ 
SF1 
BPS 
+ 
FBP11 
U1 
SF1 
(
complex) 
(175, 
271) 
1
7 
t17.U170
K 
U2AF3
5 
bd
g 
U1 
5ss 
+ 
SC35 
+ 
SF1 
U2AF 
FBP11 
(268, 
272) 
U1 
SF2 
U2AF 
continued 
next 
page 


Description 
of 
Reactions 
of 
the 
Spliceosomal 
Model 
Network 



Tabl
e 
C.1
: 
Reaction
s 
(transitions
) 
involve
d 
i
n 
spliceosom
e 
assembl
y 
(ac
t 
= 
activated
, 
as
s 
= 
assigned
, 
bd
g 
= 
binding
con
converted
di
= 
dimerized
, 
hydro
l 
= 
hydolized
, 
inhi
b 
= 
inhibited
, 
matu
r 
= 
maturated
, 
phosp
h 
= 
phosphorylated
, 
sta
b 
= 
szabilized
st
stemloop
uw
unwound, 
tri-snRN
P 
= 
U4/U5/U
6 
subcomplex
) 


Descriptio
n 
ID 
Labe
l 
Reactio
n 
Reference
U1 
and 
U2 
become 
ATP-independent 
bridged 
by 
DEAD-box 
protein 
Prp5. 
Subsequently 
U1 
interacts 
with 
50ss 
and 
U2 
with 
U2AF. 
Within 
the 
bridged 
complex 
U1/SF1 
interactions 
still 
occur. 
1
8 
t27.U
1 
Prp
5 
U
2 
bd
g 
U1 
+ 
U2 
+ 
Prp5 
. 
U1 
Prp5 
U2 
(181) 
1
9 
t28.U
1 
5s
s 
U
2 
U2A
F 
bd
g 
5ss 
+ 
U1 
Prp5 
U2 
SF1 
U2AF 
U1U2 
bridge 
SC35 
activates 
50ss, 
independent 
of 
functional 
U1 
snRNP 
2
0 
t16.U
1 
inde
p 
5s
s 
ac
t 
SC35 
+ 
5ss 
+SF1 
U2AF 
U2AF 
SC35 
5ss 
(171, 
173, 
244) 
PTB 
binds 
to 
PPT, 
outcompeting 
U2AF 
and 
preventing 
E 
complex 
assembly 
and, 
thus, 
splicing 
of 
tissue 
speci
c 
exons 
(e.g. 
c-src 
exon 
N1) 
2
1 
t61.PT
B 
PT
T 
bd
g 
PTB 
+ 
PPT 
+ 
U1 
5ss 
PTB 
PPT 
(6, 
273) 
2
2 
t62.
E 
comple
x 
inhi
b 
PTB 
PPT 
. 
U1 
5ss 
+ 
U1 
5ss 
block 
A 
Comple
x 
Reaction
s 
Assembly 
of 
U2 
subunit 
SF3b 
(components 
shared 
with 
U12 
snRNP) 
2
3 
t111.SF3
b 
di
m 
SF3b10 
+ 
SF3b14a 
+ 
SF3b14b 
SF3b49 
SF3b125 
+ 
SF3b130 
+ 
SF3b145 
SF3b155 
ATP 
. 
SF3b 
+ 
ADP 
(274, 
182) 
Binding 
of 
subunit 
SF3b 
to 
core 
U2 
snRNP 
2
4 
t21.15
S 
U
2 
matu
r 
12S 
U2 
core 
+ 
SF3b 
+ 
U2A 
U2B 
15S 
U2 
(275) 
Dissociation 
of 
SF3b 
DEAD-box 
protein 
SF3b125 
2
5 
t13.17
S 
U
2 
matur
2 
15S 
U2 
+ 
SF3a 
. 
SF3b125 
U2 
(182) 
Assembly 
of 
U2 
subunit 
SF3a 
2
6 
t104.SF3
a 
di
m 
SF3a60 
+ 
SF3a66 
+ 
SF3a120 
SF3a 
(275) 
Binding 
of 
subunit 
SF3a 
to 
15S 
U2 
snRNP 
2
7 
t22.17
S 
U
2 
matur
1 
15S 
U2 
+ 
SF3a 
+ 
ATP 
SF3b125 
U2 
+ADP 
(182) 
U2 
snRNP 
component 
SF3b155 
binds 
to 
both 
sides 
of 
the 
BPS 
and 
interacts 
with 
U2AF65, 
after 
U2AF 
binding 
to 
the 
PPT. 
hPrp43 
was 
found 
in 
17S 
U2 
complex 
and 
proposed 
to 
facilitate 
U2 
formation 
by 
associating 
with 
premRNA 
as 
part 
of 
the 
17S 
U2 
snRNP. 
hPrp43 
was 
found 
to 
be 
activated 
in 
post-spliceosomal 
complex 
formation. 
28 
t8.SF3b15
5 
U2AF6
5 
bd
g 
U2AF 
PPT 
3ss 
+ 
U2 
+hPrp43 
U2 
PPT 
(179, 
182) 
Prp5 
replaces 
Prp9 
(SF3a60) 
within 
U2 
snRNP 
subcomplex 
and 
promotes 
opening 
of 
the 
U2 
snRNA 
stemloop 
IIa, 
thus 
increasing 
U2 
sensitivity 
for 
the 
branch 
point 
sequence 
(BPS) 
2
9 
t23.unwind
2 
U
2 
stl
2 
Prp5 
+ 
ATP 
+ 
U2 
PPT 
ADP 
U2 
remod1 
+ 
SF3a60 
(165, 
183, 
181) 
Speci
c 
activation 
of 
ATPase 
UAP56 
by 
U2AF65 
3
0 
t57.UAP5
6 
U2AF6
5 
bdg
1 
UAP56 
+U2 
remod1 
. 
U2 
UAP56 
ass 
(184, 
165) 
3
1 
t25.UAP5
6 
U2AF6
5 
bdg
2 
UAP56 
+U1U2 
remod 
U2 
UAP56 
ass 


continued 
next 
page 


Appendix 
161 



Tabl
e 
C.1
: 
Reaction
s 
(transitions
) 
involve
d 
i
n 
spliceosom
e 
assembl
y 
(ac
t 
= 
activated
, 
as
s 
= 
assigned
, 
bd
g 
= 
binding
con
converted
di
= 
dimerized
, 
hydro
l 
= 
hydolized
, 
inhi
b 
= 
inhibited
, 
matu
r 
= 
maturated
, 
phosp
h 
= 
phosphorylated
, 
sta
b 
= 
szabilized
st
stemloop
uw
unwound
, 
tri-snRN
P 
= 
U4/U5/U
6 
subcomplex
) 


Descriptio
n 
ID 
Labe
l 
Reactio
n 
Reference
Action 
of 
Prp5 
within 
bridged 
U1U2 
complex. 
UAP56 
catalyzes 
stable 
transition 
from 
E 
to 
A 
complex 
by 
release 
of 
SF1 
and 
binding 
of 
strutural 
rearranged 
U2 
to 
the 
branch 
point 
3
2 
3
3 
t55.unwind
1 
U
2 
stl
2 
t29.U1U
2 
BP
S 
bd
g 
U1U2 
bridge 
+ 
ATP 
SF3a60 
ADP 
U1U2 
remod 
U1U2 
UAP56 
ass 
+ 
ATP 
SF3a60 
SF1 
U2AF 
+ 
ADP 
+ 
U1U2 
BPS 
(
complex) 
(165, 
181) 
ATP 
dependent 
(DEAD 
box 
helicase/unwindase 
action) 
release 
of 
SF1 
from 
BPS 
and 
binding 
of 
U2 
to 
the 
branch 
point 
sequence 
3
4 
t24.U
2 
BP
S 
bdg
1 
U2 
UAP56 
ass 
+ 
ATP 
U1 
SF1 
U2AF 
SF1 
+ 
U2AF 
+ 
ADP 
U1U2 
BPS 
(
complex) 
(165, 
163),p345 
3
5 
t31.U
2 
BP
S 
bdg
2 
U2AF 
SC35 
5ss 
+ 
U2 
UAP56 
ass 
SF1 
U2AF 
+ 
U2 
BPS 
U2AF 
independent 
but 
U1 
dependent 
5ss 
recognition 
(SC35 
und 
Prp5 
supported) 
3
6 
3
7 
t56.U
1 
SC3
5 
SF
1 
bd
g 
t152.U
2 
SC3
5 
bd
g 
SC35 
+ 
U1 
5ss 
+ 
SF1 
BPS 
U1 
SC35 
SF1 
U1 
SC35 
SF1 
+ 
U2 
. 
U1 
SC35 
U2 
(244) 
3
8 
t35.unwind
3 
U
2 
stl
2 
Prp5 
+ 
ATP 
+ 
U1 
SC35 
U2 
!SF3a60 
ADP 
+ 
U2 
remod2 
3
9 
t155.U
2 
BP
S 
bdg
3 
UAP56 
+ 
ATP 
+ 
U2 
remod2 
U1U2 
BPS 
B 
Comple
x 
Reaction
s 
In 
presence 
of 
Snu13(15.5K)
, 
Prp31(61K) 
interacts 
with 
U4 
and 
U4/U6 
snRNA 
duplex, 
but 
not 
with 
U6 
snRNA 
alone. 
Sm 
proteins 
form 
the 
U4 
snRNP 
core, 
while 
Sm 
like 
proteins 
(Lsm) 
form 
the 
core 
of 
U6 
snRNP 
4
0 
4
1 
t147.U
4 
matu
r 
150.U
6 
matu
r 
U4snRNA 
+ 
Sm 
+ 
Snu13 
U4 
U6snRNA 
+ 
Lsm 
. 
U6 
(246, 
186) 
(187) 
Prp24 
ass 
anneals 
U4 
and 
U6 
snRNA 
to 
form 
snRNA 
duplex 
within 
the 
U4/U6 
snRNP 
subcomplex. 
U4 
or 
U4/U6 
binds 
Prp31 
in 
the 
presence 
of 
Snu13 
4
2 
t47.U
4 
U
6 
bd
g 
U6 
+ 
U4 
+ 
Prp31 
+ 
Prp24 
U4U6 
complex 
(165, 
189, 
276) 
Prp3 
and 
Prp4 
interact 
directly 
with 
each 
other; 
CypH, 
Prp4 
and 
Prp3 
form 
a 
stable 
RNA 
free 
trimeric 
subcomplex. 
43 
t127.Prp
3 
Prp
4 
bd
g 
Prp3+ 
Prp4 
. 
P3P4 
dim 
(277, 
246, 
186) 
4
4 
t126.Cyp
H 
P3P4di
m 
bd
g 
P3P4 
dim 
+ 
CypH 
. 
CypH 
trimer 
Prp8, 
Brr2 
and 
Snu114p 
interact 
directly 
with 
each 
other. 
Prp8 
interacts 
also 
with 
Snu114 
(human 
ortholog 
= 
U5.40K)
. 
Brr2p, 
and 
Prp8p 
copurify, 
along 
with 
Snu114 
as 
an 
RNA-free 
heterotetrameric 
complex. 
U5 
snRNA 
associates 
with 
several 
U5 
speci
c 
proteins 
including 
the 
DExD/
H 
box 
helicase 
Prp28. 
4
5 
t118.Prp
8 
trime
r 
4
6 
t119.U
5 
aux 
bd
g 
continued 
next 
page 
U5 
Prp8 
+ 
U5 
Snu114 
pre 
U5 
trimer 
pre 
U5 
trimer 
+ 
U5 
40K 
pre 
U5 
heteromer 
+U5 
Brr2 
Prp28 
(193, 
186) 


Description 
of 
Reactions 
of 
the 
Spliceosomal 
Model 
Network 



Tabl
e 
C.1
: 
Reaction
s 
(transitions
) 
involve
d 
i
n 
spliceosom
e 
assembl
y 
(ac
t 
= 
activated
, 
as
s 
= 
assigned
, 
bd
g 
= 
binding
con
converted
di
= 
dimerized
, 
hydro
l 
= 
hydolized
, 
inhi
b 
= 
inhibited
, 
matu
r 
= 
maturated
, 
phosp
h 
= 
phosphorylated
, 
sta
b 
= 
szabilized
st
stemloop
uw
unwound, 
tri-snRN
P 
= 
U4/U5/U
6 
subcomplex
) 


Descriptio
n 
ID 
Labe
l 
Reactio
n 
Reference
4
7 
t123.U
5 
matu
r 
Sm 
+ 
U5snRNA 
+ 
hDib1 
+hLin1 
Prp28 
U5 
aux 
tetramer 
. 
20S 
U5 
Prp3 
(CypH 
trimer) 
interacts 
with 
Prp24 
(recycling 
factor) 
prior 
to 
U4/U6 
snRNA 
unwinding, 
specifying 
Prp24. 
Prp6 
function 
as 
bridging 
factor 
between 
human 
U5 
snRNP 
and 
U4/U6 
snRNPs. 
Prp38 
is 
suggested 
to 
promote 
a 
conformational 
change 
within 
the 
spliceosome 
needed 
to 
expose 
the 
U4/U6 
helices, 
for 
later 
release 
of 
U4. 
4
8 
t53.U4U
6 
U
5 
bd
g 
Prp38 
+ 
hPrp6 
+ 
CypH 
trimer 
20S 
U5 
U4U5U6 
conf1 
(247, 
276, 
186) 
Snu66 
(U5.110K) 
interacts 
with 
hPrp3, 
hPrp6 
and 
hBrr2, 
contributing 
to 
bridging 
the 
snRNPs 
in 
the 
tri-snRNP 
4
9 
t48.U4U6U
5 
sta
b 
U4U6U5 
conf1 
+ 
hSad1 
hSnu66 
tris 
27K 
. 
hLin1 
+ 
U4U6U5 
conf2 
(186) 
Speci
c 
activation 
of 
DExD/
H 
box 
helicase 
Brr2 
by 
Snu114 
and 
GTP 
hydrolyzation, 
preparing 
unwinding 
of 
U4/U6 
duplex 
5
0 
t46.Snu11
4 
Brr
2 
ac
t 
U4U6U5 
conf2 
+ 
GTP 
Brr2 
ass 
GDP 
(165, 
192, 
193) 
Activated 
(specically 
directed) 
helicase 
Brr2 
catalyzes 
unwinding 
of 
U4/U6 
stemloop 
II, 
involving 
Prp8. 
Prp19 
is 
required 
for 
tri-snRNP 
formation 
(suggested 
regulatory 
role 
in 
U4/U6 
unwinding) 
5
1 
t52.U4U
6 
uw
d 
Brr2 
ass 
+ 
Prp19 
+ 
ATP 
CypH 
trimer 
U4U6U5 
conf3 
+ 
ADP 
(278, 
151, 
194) 
Prp28 
destabilizes 
U1C, 
preparing 
U1 
dissociation. 
Prp5 
is 
not 
required 
anymore 
(see 
text)
. 
Additionally, 
Prp8 
governs 
activities 
of 
the 
kinases 
Prp28 
(and 
Brr2)
. 
5
2 
t34.U1
C 
dis
s 
Prp28 
+ 
U1U2 
BPS 
ATP 
U5 
Prp8 
U1C 
+ 
ADP 
+ 
U1 
5ss 
destab 
Prp5 
(191, 
192, 
279) 
Interaction 
of 
Prp19 
with 
FBP11 
and 
U2AF65 
(both 
present 
in 
A 
complex) 
stabilizes 
tri-snRNP 
addition, 
while 
U1 
and 
U4 
are 
released 
from 
tri-snRNP 
complex. 
Dib1 
(U5.15K) 
and 
Prp28 
(U5.100K) 
are 
absent 
in 
B-complexes 
after 
U4/U6 
dissociation. 
This 
step 
is 
assumed 
to 
be 
similar 
in 
the 
U1 
independent 
spliceosomal 
assembly 
pathway. 
5
3 
5
4 
t33.U
6 
5s
s 
bdg
1 
t32.U
6 
5s
s 
bdg
2 
U4U6U5 
conf3 
(Prp19 
present)
U1 
5ss 
destab 
(FBP11, 
U1 
SF1 
U2AF 
present) 
. 
U2U6U5 
5ss 
hDib1 
Prp28 
Prp38 
+U1 
+ 
U4 
U4U6U5 
conf3 
+ 
U2 
BPS 
U2U6U5 
5ss 
hDib1 
+ 
Prp28 
+ 
Prp38 
U4 
(280, 
281, 
195, 
163, 
282) 
(142) 
In 
human, 
Prp19 
is 
stably 
associated 
with 
several 
proteins, 
forming 
the 
heteromeric 
NTC 
complex 
5
5 
t65.Prp1
9 
sta
b 
NTC 
heteromer 
+ 
Prp19 
14S 
NTC 
Prp19 
(194) 
NTC 
complex 
subsequently 
acts 
to 
U4 
dissociation 
stabilizing 
association 
of 
U5 
and 
U6 
with 
the 
activated 
spliceosome. 
NTC 
destabilizes 
U6-associated 
Lsm 
proteins, 
but 
promotes 
U6:50ss 
interactions 
during 
activation 
for 
1st 
step 
catalysis; 
Prp31 
and 
Prp3 
are 
destabilized 
during 
the 
catalytic 
activation 
step. 
5
6 
t68.
B 
comp
l 
ac
t 
U2U6U5 
5ss 
+ 
SKIP 
14S 
NTC 
Prp19 
Prp31 
+ 
Prp3 
+ 
U2 
3ss 
U6 
5ss 
U5 
(45S 
activated 
spliceosome) 
+ 
free 
Lsm 
(151, 
283) 
Binding 
of 
Prp2 
to 
the 
spliceosome 
requires 
prior 
binding 
of 
Spp2. 
5
7 
t70.Spp
2 
Prp
2 
ac
t 
continued 
next 
page 
Spp2 
+ 
Prp2 
. 
Prp2 
ass 
(165) 


Appendix 
163 



Tabl
e 
C.1
: 
Reaction
s 
(transitions
) 
involve
d 
i
n 
spliceosom
e 
assembl
y 
(ac
t 
= 
activated
, 
as
s 
= 
assigned
, 
bd
g 
= 
binding
con
converted
di
= 
dimerized
, 
hydro
l 
= 
hydolized
, 
inhi
b 
= 
inhibited
, 
matu
r 
= 
maturated
, 
phosp
h 
= 
phosphorylated
, 
sta
b 
= 
szabilized
st
stemloop
uw
unwound
, 
tri-snRN
P 
= 
U4/U5/U
6 
subcomplex
) 


Descriptio
n 
ID 
Labe
l 
Reactio
n 
Reference
Opening 
of 
RNA 
at 
50ss 
and 
ligation 
of 
intron 
5
. 
end 
to 
branch 
point 
adenin 
(nucleophilic 
attack)
. 
5
8 
t69.1s
t 
cata
l 
ste
p 
U2 
3ss 
U6 
5ss 
U5 
+ 
Prp2 
ass 
U2 
3ss 
U6 
5ssfree 
U5 
ADP 
ATP 
(284, 
154) 
C 
Comple
x 
Reaction
s 
Conformational 
rearrangements 
by 
ATP 
hydrolysis 
by 
Prp16, 
requiring 
a 
clear 
branch 
point 
signal. 
DDX35 
and 
Hsp73 
occur 
together 
with 
Prp17 
in 
Ccomplex, 
but 
not 
in 
45S 
activated 
spliceosomes 
(
B 
complex)
. 
Critical 
step 
due 
to 
kinetic 
proofreading 
by 
Prp16 
and 
essential 
for 
submitting 
optimal 
substrates 
into 
the 
2nd 
step 
of 
splicing 
or 
suboptimal 
splicing 
substrates 
into 
a 
discard 
pathway. 
Many 
SR 
proteins 
are 
not 
observed 
anymore 
in 
puried 
complexes 
of 
this 
stage. 
5
9 
t93.U
2 
3s
s 
U
6 
5s
s 
U
5 
remodPrp16 
+ 
U2 
3ss 
U6 
5ssfree 
U5 
ATP 
Hsp73 
+ 
DDX35 
. 
U2 
5ss 
U6 
U5 
conf1 
ADP 
+ 
ASF 
SF2 
+ 
SC35 
(165, 
151, 
197) 
Proper 
kinetic 
speed 
of 
Prp16 
in 
ATP 
hydrolyzation 
and 
conformational 
transition 
to 
lariat 
intermediate, 
resulting 
in 
positioning 
50ss 
near 
downstream 
exon, 
enabling 
30ss 
recognition 
by 
U5snRNA 
and 
Prp8. 
Four 
SF3b 
proteins 
are 
not 
detected 
in 
C-complex. 
6
0 
t101.Prp1
6 
remo
d 
ste
p 
U2 
5ss 
U6 
U5 
conf1 
. 
SF3b49 
SF3b14a 
SF3b14b 
+ 
SF3b10 
+ 
U2 
5ss 
U6 
U5 
conf2 
(285, 
151, 
197) 
DExD/
H 
box 
ATPase 
Prp22p 
binds 
to 
the 
intron 
just 
upstream 
of 
the 
30ss 
to 
promote 
exon 
ligation. 
Prp22 
proofreads 
exon 
ligation 
by 
sensing 
aberrant 
substrates 
before 
the 
second 
transesterication. 
Hydrolyzation 
of 
ATP 
serves 
as 
timer 
to 
reject 
intermediates 
after 
a 
certain 
time, 
thus, 
favoring 
only 
fast 
substrates 
to 
be 
spliced. 
Prp22 
binds 
additional 
factors, 
such 
as 
Slu7 
and 
Prp8, 
which 
are 
essential 
for 
splicing 
delity 
and 
may 
regulate 
the 
ATP 
dependent 
activityof 
Prp22. 
6
1 
t92.U2U6U
5 
3s
s 
remo
d 
Prp22 
+ 
Prp17 
+ 
Prp18 
U2 
5ss 
U6 
U5 
conf2 
+ 
ATP 
U2U6U5 
3ss 
+ 
ADP 
Slu7 
Prp16 
(165, 
286, 
198, 
287) 
2nd 
catalytic 
step 
by 
opening 
the 
substrate 
RNA 
at 
the 
30ss 
and 
ligation 
of 
free 
exon 
ends 
along 
with 
release 
of 
the 
catenated 
exons 
and 
a 
post-splicing 
complex 
(containing 
the 
lariat, 
composed 
of 
intron 
and 
associated 
RNPs)
. 
6
2 
t94.2n
d 
cata
l 
ste
p 
U2U6U5 
3ss 
. 
post 
splsom 
complex 
spliced 
mRNA 
(165) 
DExD/
H 
helicase 
Prp43 
replaces 
Prp22 
and 
promotes 
release 
and 
dissassembly 
of 
factors 
from 
the 
postspliceosomal 
complex. 
Prp6 
is 
modeled 
to 
leave 
at 
this 
stage 
in 
order 
to 
rejoin 
another 
round 
of 
tri-snRNP 
assembly. 
63 
t95.intro
n 
releas
e 
post 
splsom 
complex 
+ 
Prp43 
ass 
ATP 
U2U6 
lariat 
+ 
ADP 
+ 
hPrp6 
35S 
U5 
(288, 
241, 
193) 
Brr2 
mediated 
U2 
release 
separating 
U2 
snRNA 
from 
intron, 
during 
spliceosome 
disassembly. 
Brr2 
is 
still 
part 
of 
the 
postspliceosomal 
complex, 
hence 
not 
separately 
modeled. 
GTP 
is 
required 
for 
the 
Snu114-GTP 
state, 
which 
regulates 
Brr2 
helicase 
activity. 
6
4 
t77.U2 
releas
e 
GTP 
+ 
U2U6U5 
lariat 
U2 
GDP 
U6 
intron 
(192, 
193) 


continued 
next 
page 


Description 
of 
Reactions 
of 
the 
Spliceosomal 
Model 
Network 



Tabl
e 
C.1
: 
Reaction
s 
(transitions
) 
involve
d 
i
n 
spliceosom
e 
assembl
y 
(ac
t 
= 
activated
, 
as
s 
= 
assigned
, 
bd
g 
= 
binding
con
converted
di
= 
dimerized
, 
hydro
l 
= 
hydolized
, 
inhi
b 
= 
inhibited
, 
matu
r 
= 
maturated
, 
phosp
h 
= 
phosphorylated
, 
sta
b 
= 
szabilized
st
stemloop
uw
unwound, 
tri-snRN
P 
= 
U4/U5/U
6 
subcomplex
) 


Descriptio
n 
ID 
Labe
l 
Reactio
n 
Reference
35S 
U5 
snRNP 
is 
converted 
into 
a 
20S 
particle. 
This 
conversion 
involves 
the 
release 
of 
the 
Prp19 
(NTC) 
complex 
and 
reassociation 
of 
Prp6, 
Prp28 
and 
hDib1. 
6
5 
t85.35
S 
U
5 
con
v 
Prp28 
+ 
hDib1 
+ 
35S 
U5 
14S 
NTC 
Prp19 
+ 
SKIP 
+ 
20S 
U5 
(151) 
Negative 
outcome 
of 
kinetic 
proofreading 
pathway: 
suboptimal 
splicing 
substrates 
slow 
conformational 
rearrangement 
powered 
by 
ATP 
hydolysis 
by 
Prp16 
and 
activate 
a 
discard 
pathway. 
6
6 
t100.prematur
e 
AT
P 
hydrolU2 
5ss 
U6 
U5 
conf1 
ATP 
U2 
3ss 
U6 
5m 
discard 
Prp16 
ADP 
(165, 
197) 
Spp382p-dependent 
activity 
in 
the 
discard 
pathway 
of 
impaired 
spliceosomes 
(in 
presence 
of 
defective 
RNA 
substrates). 
Additionally, 
this 
activity 
can 
be 
stimulated 
byhPrp43, 
leading 
to 
dissociation 
and 
reorganization 
of 
the 
spliceosome. 
6
7 
t96.Spp38
2 
hPrp4
3 
ac
t 
hPrp43 
+ 
Spp382 
. 
Prp43 
ass 
(241) 


Appendix 
165 



Application 
of 
the 
Integrated 
Net 
Analyszer 


C.2 
Application 
of 
the 
Integrated 
Net 
Analyszer 
Table 
C.2: 
Analysis 
sequence 
in 
the 
program 
INA, 
applied 
to 
the 
presented 
PN 
model. 
Abbrev.: 
A,S,Q 
= 
primary 
command 
switches; 
W,V,F,E 
= 
secondary 
command 
switches; 
Y/N 
= 
yes/no 
decision, 
e/f 
= 
exit/format 
decision. 


Analysis 
Decision 
ParameterFunction 


A 
Analyse 
a 
Petri 
net 
pnt 
le 
(name 
input 
le*.pnt) 
Y 
Reset 
current 
name 
options? 
W(V) 
Transitions 
(place) 
names 
to 
be 
written 
N 
Print 
static 
con
icts? 
S 
Compute 
a 
basis 
for 
all 
semipositive 
P/T 
invariants 
Y 
Check 
current 
options? 
T/P 
Computation 
of 
T/P 
invariants 
F 
Outputformat: 
print 
non-zero 
entries 
with 
names 
(#) 
N 
Skip 
a 
certain 
number 
of 
lines? 
N 
Set 
reduction 
options? 
(f) 
Results 
by 
other 
format? 
N 
Reset 
reduction 
options? 
Y 
Change 
output 
format 
(E) 
? 
E 
Print 
non-zero 
entries 
only 
(name 
output 
le*.res) 
(e) 
Results 
by 
other 
format? 
Q 
Quit 
analysis 
Q 
Quit 
program 
Y 
forget 
the 
net? 
Y 
Save 
the 
commands 
OPTIONS.ina 
/ 
Rename 
SESSION.
ina? 
(name 
output 
le 
*.ina) 



Appendix 
167 


C.3 
T-Invariants 
Computed 
for 
the 
Spliceosomal 
Assembly 
Network 

Tabl
e 
C.3
: 
Descriptio
n 
o
f 
trivia
l 
an
d 
non-trivialT-invariants
, 
describin
g 
signalin
g 
pathway
s 
durin
g 
th
e 
proces
o
spliceosom
assembl


I
D 
#
t 
Transition
s 
Biologica
l 
Interpretatio
n 
1 
2 
t102, 
t116 
Prp28 
(DDX23) 
in
ux 
and 
eux 
(trivial) 
2 
2 
t39, 
t117 
HDib1(U5.15K) 
in
ux 
and 
eux 
(trivial) 
3 
5 
t2, 
t60-t63 
PTB 
inhibition 
of 
branchsite, 
outcompeting 
U2AF(65) 
and 
blocking 
of 
E 
complex 
formation(PTB 
is 
itself 
alternatively 
spliced) 
4 
2 
t156, 
t157 
SF1 
(BBP) 
in
ux 
and 
eux 
(trivial) 
5 
2 
t37, 
t38 
Prp5 
(DDX46) 
in
ux 
and 
eux 
(trivial) 
6 
2 
t153, 
t154 
SC35 
(SFRS2,SRp30b) 
in
ux 
and 
eux 
(trivial) 
7 
5 
t36, 
t146-t149 
U4 
snRNP 
maturation 
and 
decay 
8 
2 
t78, 
t112 
SF3b49 
(SAP49) 
in
ux 
and 
eux 
(trivial) 
9 
2 
t80, 
t108 
SF3b14a 
(SAP14) 
in
ux 
and 
eux 
(trivial) 
10 
2 
t79, 
t115 
SF3b14b 
(PHF5A) 
in
ux 
and 
eux 
(trivial) 
11 
2 
t74, 
t107 
SF3b10 
in
ux 
and 
eux 
(trivial) 
12 
2 
t103, 
t140 
ASF/SF2 
(SFRS2A, 
SRp30a) 
in
ux 
and 
eux 
(trivial) 
13 
91 
t0-t8, 
t11, 
t13, 
t14, 
t16, 
t18-t21, 
, 
t23, 
t26, 
t31, 
t32, 
t37, 
t42, 
t44-t54, 
t57, 
t64, 
U1 
independent 
50ss 
activation, 
early 
SF3b125 
action 
t68-t72, 
t76, 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t129, 
(t13.17
S 
U
2 
matur2
) 
in 
U2 
maturation 
t144, 
t145, 
t148, 
t150, 
t151, 
t158-t160 
14 
92 
t0-t8, 
t11, 
t14, 
t16, 
t18-t23, 
t26, 
t31, 
t32, 
t37, 
t42, 
t44-t54, 
t57, 
t64, 
t68-t72, 
t76, 
As 
i13 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
snRNP 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t109-t111, 
t113, 
t114, 
t116-t129, 
t144, 
t145, 
maturation 
t148, 
t150, 
t151, 
t158-t160 
15 
105 
t0-t9, 
t11-t13, 
t15, 
t18-t21, 
t23, 
t24, 
t26, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57-t59, 
t64, 
SRp 
(ASF/SF2) 
supported 
50ss 
recognition 
with 
U1C-TIA1 
stabilit68-
t72, 
t76, 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t104-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t136, 
sation, 
FBP11 
(PRP40) 
mediated 
cross-intron 
bridging 
to 
SF1, 
early 
t141-t145, 
t148, 
t150, 
t151, 
t153, 
t158-t160 
SF3b125 
action 
(t13.17
S 
U
2 
matur
in 
U2 
maturation 
16 
106 
t0-t9, 
t11, 
t12, 
t15, 
t18-t24, 
t26, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57-t59, 
t64, 
t68-t72, 
t76, 
As 
i15, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t104-t106, 
t109-t111, 
t113, 
t114, 
t116-t136, 
t141-t145, 
t148, 
t150, 
t151, 
t153, 
t158-t160 
17 
104 
t0-t9, 
t11-t15, 
t17-t21, 
t23, 
t24, 
t26, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57, 
t64, 
t68-t72, 
t76, 
SRp 
(ASF/SF2, 
SC35) 
supported 
50ss 
recognition 
with 
U1C-TIA1 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t104-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t136, 
t141-t145, 
stabilization, 
FBP11 
mediated 
cross-intron 
bridging 
to 
SF1-U2AF 
t148, 
t150, 
t151, 
t158-t160 
heteromer, 
early 
SF3b125 
action 
(t13.17
U
matur
in 
U2 
maturation 
18 
105 
t0-t9, 
t11, 
t12, 
t14, 
t15, 
t17-t24, 
t26, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57, 
t64, 
t68-t72, 
t76, 
As 
i17, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t104-t106, 
t109-t111, 
t113, 
t114, 
t116-t136, 
t141-t145, 
snRNP 
maturation 
t148, 
t150, 
t151, 
t158-t160 
19 
93 
t3, 
t7, 
t9, 
t11-t13, 
t18-t21, 
t26, 
t33-t35, 
t40, 
t42, 
t44-t54, 
t56, 
t64, 
t68-t72, 
t76, 
t77, 
SRp 
(ASF/SF2,SC35) 
supported 
50ss 
recognition, 
U2AF 
independent 
t81, 
t82, 
t84-t98, 
t101, 
t104-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t136, 
t141-t145, 
t148, 
E/A-complex 
assembly 
early 
SF3b125 
action 
(t13) 
in 
U2 
maturation 
t150-t152, 
t155, 
t160 
20 
94 
t3, 
t7, 
t9, 
t11, 
t12, 
t18-t22, 
t26, 
t33-t35, 
t40, 
t42, 
t44-t54, 
t56, 
t64, 
t68-t72, 
t76, 
t77, 
As 
i19, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t81, 
t82, 
t84-t98, 
t101, 
t104-t106, 
t109-t111, 
t113, 
t114, 
t116-t136, 
t141-t145, 
t148, 
snRNP 
maturation 
t150-t152, 
t155, 
t160 
continued 
next 
page 


T-Invariants 
Computed 
for 
the 
Spliceosomal 
Assembly 
Network 



Tabl
e 
C.3
: 
Descriptio
n 
o
f 
trivia
l 
an
d 
non-trivialT-invariants
, 
describin
g 
signalin
g 
pathway
s 
durin
g 
th
e 
proces
o
spliceosom
assembl


I
D 
#t 
Transition
s 
Biologica
l 
Interpretatio
n 
21 
93 
t3-t7, 
t10, 
t11, 
t13, 
t18-t21, 
t26, 
t33-t35, 
t40, 
t42, 
t44-t54, 
t56, 
t64, 
t68-t72, 
t76, 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t129, 
t133-t139, 
t144, 
5
. 
terminal 
exon 
50ss 
recognition 
via 
U1-CBC 
interaction 
(with 
U2AF)
, 
SC35 
supported 
50ss 
recognition, 
but 
U2AF 
independent 
(!
t145, 
t148, 
t150-t152, 
t155, 
t160 
E-/A-complex 
assembly, 
early 
SF3b125 
action 
(t13) 
in 
U2 
maturation 
22 
94 
t3-t7, 
t10, 
t11, 
t18-t22, 
t26, 
t33-t35, 
t40, 
t42, 
t44-t54, 
t56, 
t64, 
t68-t72, 
t76, 
t77, 
t81, 
As 
i21, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t82, 
t84-t98, 
t101, 
t103-t106, 
t109-t111, 
t113, 
t114, 
t116-t129, 
t133-t139, 
t144, 
t145, 
snRNP 
maturation 
23 
101 
t148, 
t150-t152, 
t155, 
t160 
t0-t8, 
t10, 
t11, 
t13-t15, 
t17-t21, 
t23, 
t24, 
t26, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57, 
t64, 
t68-t72, 
t76, 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t129, 
5
. 
terminal 
exon 
50ss 
recognition 
via 
U1-CBC 
interaction 
(with 
U2AF)
, 
SC35 
supported 
50ss 
recognition, 
FBP11 
(PRP40) 
mediated 
t133-t139, 
t144, 
t145, 
t148, 
t150, 
t151, 
t158-t160 
cross-intron 
bridging 
to 
SF1-U2AF 
heteromer, 
early 
SF3b125 
action 
(t13) 
in 
U2 
maturation 
24 
102 
t0-t8, 
t10, 
t11, 
t14, 
t15, 
t17-t24, 
t26, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57, 
t64, 
t68-t72, 
t76, 
As 
i23, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t109-t111, 
t113, 
t114, 
t116-t129, 
t133-t139, 
snRNP 
maturation 
25 
102 
t144, 
t145, 
t148, 
t150, 
t151, 
t158-t160 
t0-t8, 
t10, 
t11, 
t13, 
t15, 
t18-t21, 
t23, 
t24, 
t26, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57-t59, 
t64, 
5
. 
terminal 
exon 
50ss 
recognition 
via 
U1-CBC 
interaction 
(with 
t68-t72, 
t76, 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t129, 
U2AF)
, 
FBP11 
mediated 
cross-intron 
bridging 
between 
U1 
and 
SF1, 
t133-t139, 
t144, 
t145, 
t148, 
t150, 
t151, 
t153, 
t158-t160 
early 
SF3b125 
action 
(t13) 
in 
U2 
maturation 
26 
103 
t0-t8, 
t10, 
t11, 
t15, 
t18-t24, 
t26, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57-t59, 
t64, 
t68-t72, 
t76, 
As 
i25, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t109-t111, 
t113, 
t114, 
t116-t129, 
t133-t139, 
snRNP 
maturation 
27 
90 
t144, 
t145, 
t148, 
t150, 
t151, 
t153, 
t158-t160 
t3, 
t7, 
t11, 
t13, 
t18-t21, 
t26, 
t30, 
t33-t35, 
t40, 
t42, 
t44-t54, 
t56, 
t64, 
t68-t72, 
t76, 
Intron 
sided 
50ss 
recognition 
via 
U1C-TIA1 
contacts, 
U2AF 
indepent77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t136, 
t144, 
t145, 
dent 
E/A-complex 
assembly, 
early 
SF3b125 
action 
(t13) 
in 
U2 
matut148, 
t150-t152, 
t155, 
t160 
ration 
28 
91 
t3, 
t7, 
t11, 
t18-t22, 
t26, 
t30, 
t33-t35, 
t40, 
t42, 
t44-t54, 
t56, 
t64, 
t68-t72, 
t76, 
t77, 
As 
i27, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t109-t111, 
t113, 
t114, 
t116-t136, 
t144, 
t145, 
t148, 
snRNP 
maturation 
29 
101 
t150-t152, 
t155, 
t160 
t0-t8, 
t11, 
t13-t15, 
t17-t21, 
t23, 
t24, 
t26, 
t30, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57, 
t64, 
Intron 
sided 
U1 
50ss 
recognition, 
FBP11 
mediated 
cross-intron 
bridgt68-
t72, 
t76, 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t136, 
ing 
to 
SF1-U2AF 
heteromer, 
early 
SF3b125 
action 
(t13) 
in 
U2 
matut144, 
t145, 
t148, 
t150, 
t151, 
t158-t160 
ration 
30 
102 
t0-t8, 
t11, 
t14, 
t15, 
t17-t24, 
t26, 
t30, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57, 
t64, 
t68-t72, 
t76, 
As 
i29, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t109-t111, 
t113, 
t114, 
t116-t136, 
t144, 
t145, 
snRNP 
maturation 
31 
102 
t148, 
t150, 
t151, 
t158-t160 
t0-t8, 
t11, 
t13, 
t15, 
t18-t21, 
t23, 
t24, 
t26, 
t30, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57-t59, 
t64, 
Intron 
sided 
U1 
50ss 
recognition, 
FBP11 
mediated 
cross-intron 
bridgt68-
t72, 
t76, 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t136, 
ing 
between 
U1 
and 
SF1, 
early 
SF3b125 
action 
(t13) 
in 
U2 
maturation 
t144, 
t145, 
t148, 
t150, 
t151, 
t153, 
t158-t160 
32 
103 
t0-t8, 
t11, 
t15, 
t18-t24, 
t26, 
t30, 
t33, 
t34, 
t40, 
t42, 
t44-t54, 
t57-t59, 
t64, 
t68-t72, 
t76, 
As 
i31, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t109-t111, 
t113, 
t114, 
t116-t136, 
t144, 
t145, 
snRNP 
maturation 
33 
87 
t148, 
t150, 
t151, 
t153, 
t158-t160 
t3, 
t7, 
t11, 
t13, 
t18-t21, 
t26, 
t33-t35, 
t40-t42, 
t44-t54, 
t56, 
t64, 
t68-t72, 
t76, 
t77, 
t81, 
Normal 
U1 
50ss 
recognition, 
bridging 
of 
U1 
via 
SC35 
to 
SF1-BP, 
U2AF 
t82, 
t84-t98, 
t101, 
t103-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t129, 
t133-t136, 
t144, 
t145, 
independent 
E/A-complex 
assembly, 
early 
SF3b125 
action 
in 
U2 
matt148, 
t150-t152, 
t155, 
t160 
uration 
continued 
next 
page 


Appendix 
169 



Tabl
e 
C.3
: 
Descriptio
n 
o
f 
trivia
l 
an
d 
non-trivialT-invariants
, 
describin
g 
signalin
g 
pathway
s 
durin
g 
th
e 
proces
o
spliceosom
assembl


I
D 
#
t 
Transition
s 
Biologica
l 
Interpretatio
n 
34 
88 
t3, 
t7, 
t11, 
t18-t22, 
t26, 
t33-t35, 
t40-t42, 
t44-t54, 
t56, 
t64, 
t68-t72, 
t76, 
t77, 
t81, 
t82, 
As 
i32, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t84-t98, 
t101, 
t103-t106, 
t109-t111, 
t113, 
t114, 
t116-t129, 
t133-t136, 
t144, 
t145, 
t148, 
snRNP 
maturation 
35 
98 
t150-t152, 
t155, 
t160 
t0-t8, 
t11, 
t13-t15, 
t17-t21, 
t23, 
t24, 
t26, 
t33, 
t34, 
t40-t42, 
t44-t54, 
t57, 
t64, 
t68Normal 
U1 
50ss 
recognition, 
FBP11 
mediated 
cross-intron 
bridging 
to 
t72, 
t76, 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t129, 
SF1-U2AF 
heteromer, 
early 
SF3b125 
action 
(t13) 
in 
U2 
maturation 
t133-t136, 
t144, 
t145, 
t148, 
t150, 
t151, 
t158-t160 
36 
99 
t0-t8, 
t11, 
t14, 
t15, 
t17-t24, 
t26, 
t33, 
t34, 
t40-t42, 
t44-t54, 
t57, 
t64, 
t68-t72, 
t76, 
t77, 
As 
i35, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t109-t111, 
t113, 
t114, 
t116-t129, 
t133-t136, 
t144, 
snRNP 
maturation 
37 
99 
t145, 
t148, 
t150, 
t151, 
t158-t160 
t0-t8, 
t11, 
t13, 
t15, 
t18-t21, 
t23, 
t24, 
t26, 
t33, 
t34, 
t40-t42, 
t44-t54, 
t57-t59, 
t64, 
Normal 
U1 
50ss 
recognition, 
FBP11 
mediated 
cross-intron 
bridging 
t68-t72, 
t76, 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t110, 
t111, 
t113, 
t114, 
t116-t129, 
between 
U1 
and 
SF1, 
early 
SF3b125 
action 
(t13) 
in 
U2 
maturation 
t133-t136, 
t144, 
t145, 
t148, 
t150, 
t151, 
t153, 
t158-t160 
38 
100 
t0-t8, 
t11, 
t15, 
t18-t24, 
t26, 
t33, 
t34, 
t40-t42, 
t44-t54, 
t57-t59, 
t64, 
t68-t72, 
t76, 
t77, 
As 
i37, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t81, 
t82, 
t84-t98, 
t101, 
t103-t106, 
t109-t111, 
t113, 
t114, 
t116-t129, 
t133-t136, 
t144, 
snRNP 
maturation 
39 
100 
t145, 
t148, 
t150, 
t151, 
t153, 
t158-t160 
t0-t7, 
t11, 
t13, 
t14, 
t18-t21, 
t25-t29, 
t33, 
t34, 
t37, 
t42, 
t44-t55, 
t64, 
t68-t72, 
t76, 
t77, 
t81, 
t82, 
t84-t98, 
t101, 
t103-t108, 
t110-t129, 
t133-t136, 
t144, 
t145, 
t148, 
t150, 
t151, 
U1 
U2 
snRNP 
interaction 
via 
Prp5 
and 
subsequent 
binding 
to 
50ss 
and 
SF-U2AF 
engaged 
BP/30ss, 
early 
SF3b125 
action 
in 
U2 
maturation 
t153, 
t157, 
t160 
40 
101 
t0-t7, 
t11, 
t14, 
t18-t22, 
t25-t29, 
t33, 
t34, 
t37, 
t42, 
t44-t55, 
t64, 
t68-t72, 
t76, 
t77, 
t81, 
As 
i39, 
but 
via 
late 
SF3b125 
action 
(t22.17
U
matur
in 
U2 
t82, 
t84-t98, 
t101, 
t103-t129, 
t133-t136, 
t144, 
t145, 
t148, 
t150, 
t151, 
t153, 
t157, 
t160 
snRNP 
maturation 
41 
4 
t64-t66, 
t83 
NTC-complex 
formation 
42 
2 
t67, 
t75 
SKIP 
(PRPF45) 
in
ux 
and 
eux 
(trivial) 
43 
2 
t43, 
t73 
Prp6 
(U5.102K) 
in
ux 
and 
eux 
(trivial) 
44 
93 
t0-t8,t11,t13,t15,t18-t21,t23,t24,t26,t33,t34,t40-t54,t57-t59,t64-t72,t86,t87,t93,t96Similar 
to 
i37, 
but 
entering 
the 
discard 
pathway 
before 
C-complex 
t100,t103-t108,t110-t116,t118-t129,t133-t136,t144,t145,t148,t150,t151,t153,t158,t159 
stage. 
Hence, 
some 
transitions 
as 
t43.hPrp
in
t65.Prp1
stab
t66.NT
C 
form
, 
t67.SKI
P 
in
, 
t107.SF3b1
in
t108.SF3b14
in
t112.SF3b4
9 
i
n 
or, 
t115.SF3b14
in
which 
leave 
the 
assembly 
process 
after 
the 
critical 
stage 
where 
the 
spliceosome 
can 
turn 
into 
the 
discard 
pathway, 
appear 
in 
this 
T-invariant 
since 
these 
factors 
need 
to 
be 
recylcled 
(drawn 
as 
logical 
places) 
in 
case 
of 
the 
productive 
outcome 
of 
spliceosome 
assembly, 
but 
have 
no 
explicit 
output 
transition 
in 
case 
of 
the 
discard 
pathway 
(this 
applies 
for 
all 
T-invariants 
covering 
the 
discard 
pathway) 


T-Invariants 
Computed 
for 
the 
Spliceosomal 
Assembly 
Network 



