Evidence map›Paper›PMID 35605437›Full record

ArticleEpidemics2022

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chaoran ChenDepartment of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland.
Sarah NadeauDepartment of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland.
Ivan TopolskyDepartment of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland.
Niko BeerenwinkelDepartment of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland.
Tanja StadlerDepartment 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19GenomicsPandemicsComputational BiologyHumansMolecular Sequence DataSARS-CoV-2SwitzerlandData infrastructureGenomic epidemiologyMicroservicesRelational databaseSARS-CoV-2

Identifiers

PMID35605437
PMCPMC9107180

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.