StrainGE

a toolkit to track and characterize low-abundance strains in complex microbial communities

Journal article (2022)

Authors

L.R. van Dijk Broad Institute of MIT and Harvard, Pattern Recognition and Bioinformatics - EEMCS
BJ Walker Applied Invention LLC, Broad Institute of MIT and Harvard
T.E.P.M.F. Abeel Broad Institute of MIT and Harvard, Pattern Recognition and Bioinformatics - EEMCS
AM Earl Broad Institute of MIT and Harvard
Timothy J. Straub Harvard T.H. Chan School of Public Health, Broad Institute of MIT and Harvard
Colin J. Worby Broad Institute of MIT and Harvard
Alexandra Grote Broad Institute of MIT and Harvard
Henry L. Schreiber Washington University School of Medicine
C.A. Anyansi Pattern Recognition and Bioinformatics - EEMCS
A. Pickering Tufts University, University of California
Scott J. Hultgren Washington University School of Medicine
A Manson McGuire Broad Institute of MIT and Harvard
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2022 L.R. van Dijk, Bruce J. Walker, Timothy J. Straub, Colin J. Worby, Alexandra Grote, Henry L. Schreiber, C.A. Anyansi, Amy J. Pickering, Scott J. Hultgren, Abigail L. Manson, T.E.P.M.F. Abeel, Ashlee M. Earl
DOI
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https://doi.org/10.1186/s13059-022-02630-0
Microbiome Metagenomics Strain-tracking
More Info
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Published Date

2022

Language

English

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

Human-associated microbial communities comprise not only complex mixtures of bacterial species, but also mixtures of conspecific strains, the implications of which are mostly unknown since strain level dynamics are underexplored due to the difficulties of studying them. We introduce the Strain Genome Explorer (StrainGE) toolkit, which deconvolves strain mixtures and characterizes component strains at the nucleotide level from short-read metagenomic sequencing with higher sensitivity and resolution than other tools. StrainGE is able to identify strains at 0.1x coverage and detect variants for multiple conspecific strains within a sample from coverages as low as 0.5x.