Evidence map›Paper›PMID 32310745›Full record

ArticleMicrobial genomics2020

prewas: data pre-processing for more informative bacterial GWAS.

Katie Saund, Zena Lapp, Stephanie N Thiede, Ali Pirani, Evan S Snitkin

Open access · goldAbstract read
In one paragraph

Article in Microbial genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-weighted citation impact, top 27% of its field
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

7 citing papers in PubMed, 28 citations in OpenAlex.

  1. Genomic analysis reveals two dominant strains ofApplied and environmental microbiology · 2025
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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 at 1 institution in 1 country.

Katie SaundDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA.
Zena LappDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Stephanie N ThiedeDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA.
Ali PiraniDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA.
Evan S SnitkinDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA.
University of Michigan · US

Funding

University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
PREDOCTORAL TRAINING IN GENETICST32GM007544 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MORAN, JOHN V. · 1985 to 2022
$11.3M
Systems biology of Clostridium difficile infectionU01AI124255 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SCHLOSS, PATRICK DAVID, YOUNG, VINCENT B · 2016 to 2020
$9.8M
Molecular Mechanisms of Microbial Pathogenesis Training ProgramT32AI007528 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CARRUTHERS, VERNON BRUCE · 1998 to 2024
$6.8M
NHGRI NIH HHS T32 HG000040NIAID NIH HHS T32 AI007528NIAID NIH HHS U01 AI124255NIGMS NIH HHS T32 GM007544
6 · The paper itself

Abstract

While variant identification pipelines are becoming increasingly standardized, less attention has been paid to the pre-processing of variants prior to their use in bacterial genome-wide association studies (bGWAS). Three nuances of variant pre-processing that impact downstream identification of genetic associations include the separation of variants at multiallelic sites, separation of variants in overlapping genes, and referencing of variants relative to ancestral alleles. Here we demonstrate the importance of these variant pre-processing steps on diverse bacterial genomic datasets and present prewas, an R package, that standardizes the pre-processing of multiallelic sites, overlapping genes, and reference alleles before bGWAS. This package facilitates improved reproducibility and interpretability of bGWAS results. prewas enables users to extract maximal information from bGWAS by implementing multi-line representation for multiallelic sites and variants in overlapping genes. prewas outputs a binary SNP matrix that can be used for SNP-based bGWAS and will prevent the masking of minor alleles during bGWAS analysis. The optional binary gene matrix output can be used for gene-based bGWAS, which will enable users to maximize the power and evolutionary interpretability of their bGWAS studies. prewas is available for download from GitHub.

Indexed as

BacteriaComputational BiologyDatabases, GeneticDrug Resistance, BacterialEvolution, MolecularGenetic Association StudiesGenome, BacterialHumansPolymorphism, Single NucleotideSoftwaredata pre-processingGWASmultiallelic locioverlapping genesreference allelesoftware

Identifiers

PMID32310745
PMCPMC7371116
OpenAlexW3016356967

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.