Managing and monitoring a pandemic : showcasing a practical approach for the genomic surveillance of SARS-CoV-2

GND
1240919565
ORCID
0009-0000-7264-9165
Affiliation
Institute for Infectious Diseases and Infection Control, Jena University Hospital
Jundzill, Mateusz;
GND
1240918852
ORCID
0000-0002-2103-167X
Affiliation
Institute for Infectious Diseases and Infection Control, Jena University Hospital
Spott, Riccardo;
GND
1240919166
Affiliation
Institute for Infectious Diseases and Infection Control, Jena University Hospital
Lohde, Mara;
Affiliation
Methodology and Research Infrastructure, Genome Competence Center (MF1), Robert Koch Institute, Seestraße 10, Berlin 13353
Hölzer, Martin;
Affiliation
Institute of Medical Microbiology and Virology, University Hospital Leipzig, Johannisallee 30, Leipzig 04103
Viehweger, Adrian;
GND
1164030272
Affiliation
Institute for Infectious Diseases and Infection Control, Jena University Hospital
Brandt, Christian

With the rapidly growing amount of biological data, powerful but also flexible data management and visualization systems are of increasingly crucial importance. The COVID-19 pandemic has more than highlighted this need and the challenges scientists are facing. Here, we provide an example and a step-by-step template for non-IT personnel to easily implement an intuitive, interactive data management solution to manage and visualize the high influx of biological samples and associated metadata in a laboratory setting. Our approach is illustrated with the genomic surveillance for SARS-CoV-2 in Germany, covering over 11 600 internal and 130 000 external samples from multiple datasets. We compare three data management options used in laboratories: (i) simple, yet error-prone and inefficient spreadsheets, (ii) complex and long-to-implement laboratory information management systems and (iii) high-performance database management systems. We highlight the advantages and pitfalls of each option and outline why a document-oriented NoSQL option via MongoDB Atlas can be a suitable solution for many labs. Our example can be treated as a template and easily adapted to allow scientists to focus on their core work and not on complex data administration.

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