Advancing genomic epidemiology by addressing the bioinformatics bottleneck: Challenges, design principles, and a Swiss example

Chaoran Chen; Sarah Nadeau; Ivan Topolsky; Niko Beerenwinkel; Tanja Stadler

doi:10.1016/j.epidem.2022.100576

Advancing genomic epidemiology by addressing the bioinformatics bottleneck: Challenges, design principles, and a Swiss example

Epidemics. 2022 Jun:39:100576. doi: 10.1016/j.epidem.2022.100576. Epub 2022 May 14.

Authors

Chaoran Chen¹, Sarah Nadeau¹, Ivan Topolsky¹, Niko Beerenwinkel¹, Tanja Stadler²

Affiliations

¹ Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland.
² Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland. Electronic address: tanja.stadler@bsse.ethz.ch.

Abstract

The SARS-CoV-2 pandemic led to a huge increase in global pathogen genome sequencing efforts, and the resulting data are becoming increasingly important to detect variants of concern, monitor outbreaks, and quantify transmission dynamics. However, this rapid up-scaling in data generation brought with it many IT infrastructure challenges. In this paper, we report about developing an improved system for genomic epidemiology. We (i) highlight key challenges that were exacerbated by the pandemic situation, (ii) provide data infrastructure design principles to address them, and (iii) give an implementation example developed by the Swiss SARS-CoV-2 Sequencing Consortium (S3C) in response to the COVID-19 pandemic. Finally, we discuss remaining challenges to data infrastructure for genomic epidemiology. Improving these infrastructures will help better detect, monitor, and respond to future public health threats.

Keywords: Data infrastructure; Genomic epidemiology; Microservices; Relational database; SARS-CoV-2.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19* / epidemiology
Computational Biology / statistics & numerical data*
Computational Biology / trends
Genomics*
Humans
Molecular Sequence Data
Pandemics*
SARS-CoV-2 / genetics*
Switzerland / epidemiology