MetaCache: context-aware classification of metagenomic reads using minhashing.
Identifieur interne : 000B35 ( PubMed/Curation ); précédent : 000B34; suivant : 000B36MetaCache: context-aware classification of metagenomic reads using minhashing.
Auteurs : André Müller ; Christian Hundt ; Andreas Hildebrandt ; Thomas Hankeln [Allemagne] ; Bertil SchmidtSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2017.
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Abstract
Metagenomic shotgun sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification, i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes corresponding software tools suffer from either long runtimes, large memory requirements or low accuracy.
DOI: 10.1093/bioinformatics/btx520
PubMed: 28961782
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André Müller<affiliation><nlm:affiliation>Department of Computer Science.</nlm:affiliation>
<wicri:noCountry code="no comma">Department of Computer Science.</wicri:noCountry>
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<affiliation><nlm:affiliation>Department of Computer Science.</nlm:affiliation>
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<affiliation><nlm:affiliation>Department of Computer Science.</nlm:affiliation>
<wicri:noCountry code="no comma">Department of Computer Science.</wicri:noCountry>
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<affiliation><nlm:affiliation>Department of Computer Science.</nlm:affiliation>
<wicri:noCountry code="no comma">Department of Computer Science.</wicri:noCountry>
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<front><div type="abstract" xml:lang="en">Metagenomic shotgun sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification, i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes corresponding software tools suffer from either long runtimes, large memory requirements or low accuracy.</div>
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<Abstract><AbstractText Label="Motivation" NlmCategory="UNASSIGNED">Metagenomic shotgun sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification, i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes corresponding software tools suffer from either long runtimes, large memory requirements or low accuracy.</AbstractText>
<AbstractText Label="Results" NlmCategory="UNASSIGNED">We introduce MetaCache-a novel software for read classification using the big data technique minhashing. Our approach performs context-aware classification of reads by computing representative subsamples of k-mers within both, probed reads and locally constrained regions of the reference genomes. As a result, MetaCache consumes significantly less memory compared to the state-of-the-art read classifiers Kraken and CLARK while achieving highly competitive sensitivity and precision at comparable speed. For example, using NCBI RefSeq draft and completed genomes with a total length of around 140 billion bases as reference, MetaCache's database consumes only 62 GB of memory while both Kraken and CLARK fail to construct their respective databases on a workstation with 512 GB RAM. Our experimental results further show that classification accuracy continuously improves when increasing the amount of utilized reference genome data.</AbstractText>
<AbstractText Label="Availability and implementation" NlmCategory="UNASSIGNED">MetaCache is open source software written in C ++ and can be downloaded at http://github.com/muellan/metacache.</AbstractText>
<AbstractText Label="Contact" NlmCategory="UNASSIGNED">bertil.schmidt@uni-mainz.de.</AbstractText>
<AbstractText Label="Supplementary information" NlmCategory="UNASSIGNED">Supplementary data are available at Bioinformatics online.</AbstractText>
<CopyrightInformation>© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com</CopyrightInformation>
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