Evidence map›Paper›PMID 40794845›Full record

ArticleBioinformatics (Oxford, England)2025

StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities.

Kerim Heber, Shuchang Tian, Daniela Betancurt-Anzola, Heejung Koo, Jordan E Bisanz

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Kerim HeberDepartment of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, United States.
Shuchang TianDepartment of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, United States.
Daniela Betancurt-AnzolaDepartment of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, United States.
Heejung KooDepartment of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, United States.
Jordan E BisanzDepartment of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, United States.ORCID 0000-0002-8649-1706

Funding

Decoding Microbial Diversity in the Human Gut MicrobiomeR35GM151045 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI Jordan Adam Bisanz · 2023 to 2026
$1.5M
Diet-microbe interactions modulating host energy balanceR00AI147165 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI BISANZ, JORDAN ADAM · 2021 to 2022
$498k
NIAID NIH HHS R00 AI147165NIGMS NIH HHS R35 GM151045
6 · The paper itself

Abstract

motivationSynthetic microbial communities offer an opportunity to conduct reductionist research in tractable model systems. However, deriving abundances of highly related strains within these communities is currently unreliable. 16S rRNA gene sequencing does not resolve abundance at the strain level and other methods such as quantitative polymerase chain reaction (qPCR) scale poorly and are resource prohibitive for complex communities. We present StrainR2, which utilizes shotgun metagenomic sequencing to provide high accuracy strain-level abundances for all members of a synthetic community, provided their genomes.

resultsBoth in silico, and using sequencing data derived from gnotobiotic mice colonized with a synthetic fecal microbiota, StrainR2 resolves strain abundances with greater accuracy and efficiency than other tools utilizing shotgun metagenomic sequencing reads. We demonstrate that StrainR2's accuracy is comparable to that of qPCR on a subset of strains resolved using absolute quantification. AVAILABILITY AND IMPLEMENTATION: Software is available at GitHub and implemented in C, R, and Bash. Software is supported on Linux and MacOS, with packages available on Bioconda or as a Docker container. The source code at the time of publication is also available on figshare at the doi: 10.6084/m9.figshare.29420780.

Indexed as

MetagenomicsMicrobiotaSoftwareAnimalsFecesHigh-Throughput Nucleotide SequencingMetagenomeMiceRNA, Ribosomal, 16SSequence Analysis, DNARNA, Ribosomal, 16S

Identifiers

PMID40794845
PMCPMC12377904

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LicenceCC BY
Read underepoch 390

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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.