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Accurate reconstruction of microbial strains from metagenomic sequencing using representative reference genomes

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Zhou, Zhemin, Luhmann, Nina, Alikhan, Nabil-Fareed, Quince, Christopher and Achtman, Mark (2018) Accurate reconstruction of microbial strains from metagenomic sequencing using representative reference genomes. In: RECOMB 2018 : International Conference on Research in Computational Molecular Biology, Paris, 21-24 Apr 2018. Published in: Lecture Notes in Computer Science, 10812 pp. 225-240. ISBN 9783319899299. ISSN 0302-9743. doi:10.1007/978-3-319-89929-9_15

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Official URL: http://dx.doi.org/10.1007/978-3-319-89929-9_15

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Abstract

Exploring the genetic diversity of microbes within the environment through metagenomic sequencing first requires classifying these reads into taxonomic groups. Current methods compare these sequencing data with existing biased and limited reference databases. Several recent evaluation studies demonstrate that current methods either lack sufficient sensitivity for species-level assignments or suffer from false positives, overestimating the number of species in the metagenome. Both are especially problematic for the identification of low-abundance microbial species, e. g. detecting pathogens in ancient metagenomic samples. We present a new method, SPARSE, which improves taxonomic assignments of metagenomic reads. SPARSE balances existing biased reference databases by grouping reference genomes into similarity-based hierarchical clusters, implemented as an efficient incremental data structure. SPARSE assigns reads to these clusters using a probabilistic model, which specifically penalizes non-specific mappings of reads from unknown sources and hence reduces false-positive assignments. Our evaluation on simulated datasets from two recent evaluation studies demonstrated the improved precision of SPARSE in comparison to other methods for species-level classification. In a third simulation, our method successfully differentiated multiple co-existing Escherichia coli strains from the same sample. In real archaeological datasets, SPARSE identified ancient pathogens with ≤0.02% abundance, consistent with published findings that required additional sequencing data. In these datasets, other methods either missed targeted pathogens or reported non-existent ones.

Item Type: Conference Item (Paper)
Subjects: Q Science > QH Natural history > QH426 Genetics
Divisions: Faculty of Medicine > Warwick Medical School > Biomedical Sciences
Faculty of Medicine > Warwick Medical School > Biomedical Sciences > Microbiology & Infection
Faculty of Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH): Metagenomics -- Software, Escherichia coli -- Genetic aspects
Series Name: Lecture Notes in Computer Science
Journal or Publication Title: Lecture Notes in Computer Science
Publisher: Springer
ISBN: 9783319899299
ISSN: 0302-9743
Book Title: Research in Computational Molecular Biology
Official Date: 18 April 2018
Dates:
DateEvent
18 April 2018Published
30 January 2018Accepted
Volume: 10812
Page Range: pp. 225-240
DOI: 10.1007/978-3-319-89929-9_15
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
202792/Z/16/ZWellcome Trusthttp://dx.doi.org/10.13039/100004440
BB/L020319/1Biotechnology and Biological Sciences Research Councilhttp://dx.doi.org/10.13039/501100000268
Conference Paper Type: Paper
Title of Event: RECOMB 2018 : International Conference on Research in Computational Molecular Biology
Type of Event: Conference
Location of Event: Paris
Date(s) of Event: 21-24 Apr 2018
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