MetaCache: context-aware classification of metagenomic reads using minhashing

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Andre Mueller
  • Christian Hundt
  • Andreas Hildebrandt
  • Thomas Hankeln
  • Bertil Schmidt
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000417004100008&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1093/bioinformatics/btx520
eISSN
1460-2059
Externe Identifier
  • Clarivate Analytics Document Solution ID: FO6UQ
  • PubMed Identifier: 28961782
ISSN
1367-4803
Ausgabe der Veröffentlichung
23
Zeitschrift
BIOINFORMATICS
Paginierung
3740 - 3748
Datum der Veröffentlichung
2017
Status
Published
Titel
MetaCache: context-aware classification of metagenomic reads using minhashing
Sub types
  • Article
Ausgabe der Zeitschrift
33

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Abstract
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>MetaCache is open source software written in C ++ and can be downloaded at http://github.com/muellan/metacache.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>
Autoren
  • André Müller
  • Christian Hundt
  • Andreas Hildebrandt
  • Thomas Hankeln
  • Bertil Schmidt
DOI
10.1093/bioinformatics/btx520
Editoren
  • Inanc Birol
eISSN
1367-4811
ISSN
1367-4803
Ausgabe der Veröffentlichung
23
Zeitschrift
Bioinformatics
Sprache
en
Online publication date
2017
Paginierung
3740 - 3748
Datum der Veröffentlichung
2017
Status
Published
Herausgeber
Oxford University Press (OUP)
Herausgeber URL
http://dx.doi.org/10.1093/bioinformatics/btx520
Datum der Datenerfassung
2023
Titel
MetaCache: context-aware classification of metagenomic reads using minhashing
Ausgabe der Zeitschrift
33

Datenquelle: Crossref

Abstract
<h4>Motivation</h4>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.<h4>Results</h4>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.<h4>Availability and implementation</h4>MetaCache is open source software written in C ++ and can be downloaded at http://github.com/muellan/metacache.<h4>Contact</h4>bertil.schmidt@uni-mainz.de.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.
Addresses
  • Department of Computer Science.
Autoren
  • André Müller
  • Christian Hundt
  • Andreas Hildebrandt
  • Thomas Hankeln
  • Bertil Schmidt
DOI
10.1093/bioinformatics/btx520
eISSN
1367-4811
Externe Identifier
  • PubMed Identifier: 28961782
Funding acknowledgements
  • CSM:
  • Deutsche Forschungsgemeinschaft:
  • DFG:
Open access
false
ISSN
1367-4803
Ausgabe der Veröffentlichung
23
Zeitschrift
Bioinformatics (Oxford, England)
Schlüsselwörter
  • Humans
  • Sequence Analysis, DNA
  • Algorithms
  • Software
  • Metagenomics
  • High-Throughput Nucleotide Sequencing
Sprache
eng
Medium
Print
Paginierung
3740 - 3748
Datum der Veröffentlichung
2017
Status
Published
Datum der Datenerfassung
2017
Titel
MetaCache: context-aware classification of metagenomic reads using minhashing.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
33

Datenquelle: Europe PubMed Central

Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: MetaCache is open source software written in C ++ and can be downloaded at http://github.com/muellan/metacache. CONTACT: bertil.schmidt@uni-mainz.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Date of acceptance
2017
Autoren
  • André Müller
  • Christian Hundt
  • Andreas Hildebrandt
  • Thomas Hankeln
  • Bertil Schmidt
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/28961782
DOI
10.1093/bioinformatics/btx520
eISSN
1367-4811
Ausgabe der Veröffentlichung
23
Zeitschrift
Bioinformatics
Schlüsselwörter
  • Algorithms
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Metagenomics
  • Sequence Analysis, DNA
  • Software
Sprache
eng
Country
England
Paginierung
3740 - 3748
PII
4083578
Datum der Veröffentlichung
2017
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2018
Titel
MetaCache: context-aware classification of metagenomic reads using minhashing.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
33

Datenquelle: PubMed

Autoren
  • André Müller
  • Christian Hundt
  • Andreas Hildebrandt
  • Thomas Hankeln
  • Bertil Schmidt
Zeitschrift
Bioinform.
Artikelnummer
23
Paginierung
3740 - 3748
Datum der Veröffentlichung
2017
Titel
MetaCache: context-aware classification of metagenomic reads using minhashing.
Ausgabe der Zeitschrift
33

Datenquelle: DBLP

Autoren
  • André Müller
  • Christian Hundt
  • Andreas Hildebrandt
  • Thomas Hankeln
  • Bertil Schmidt
Zeitschrift
Bioinformatics
Artikelnummer
23
Paginierung
3740 - 3748
Datum der Veröffentlichung
2017
Herausgeber
Oxford University Press
Datum der Datenerfassung
2020
Titel
MetaCache: context-aware classification of metagenomic reads using minhashing
Sub types
  • article
Ausgabe der Zeitschrift
33

Datenquelle: Manual

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