{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T23:08:34Z","timestamp":1784934514333,"version":"3.55.0"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2017,8,17]],"date-time":"2017-08-17T00:00:00Z","timestamp":1502928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100004807","name":"DFG","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008917","name":"CSM","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008917","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004807","name":"DFG","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <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>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce MetaCache\u2014a 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\u2019s database consumes only 62\u2009GB of memory while both Kraken and CLARK fail to construct their respective databases on a workstation with 512\u2009GB RAM. Our experimental results further show that classification accuracy continuously improves when increasing the amount of utilized reference genome data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>MetaCache is open source software written in C\u2009++\u2009and can be downloaded at http:\/\/github.com\/muellan\/metacache.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx520","type":"journal-article","created":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T11:08:55Z","timestamp":1502708935000},"page":"3740-3748","source":"Crossref","is-referenced-by-count":57,"title":["MetaCache: context-aware classification of metagenomic reads using minhashing"],"prefix":"10.1093","volume":"33","author":[{"given":"Andr\u00e9","family":"M\u00fcller","sequence":"first","affiliation":[{"name":"Department of Computer Science, Johannes Gutenberg University, Mainz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Hundt","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johannes Gutenberg University, Mainz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Hildebrandt","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johannes Gutenberg University, Mainz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Hankeln","sequence":"additional","affiliation":[{"name":"Molecular Genetics and Genome Analysis Group, Department of Biology, Department of Biology, Johannes Gutenberg University, Mainz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bertil","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johannes Gutenberg University, Mainz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,8,17]]},"reference":[{"key":"2023020207003314400_btx520-B1","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1093\/bioinformatics\/btt389","article-title":"Scalable metagenomic taxonomy classification using a reference genome database","volume":"29","author":"Ames","year":"2013","journal-title":"Bioinformatics"},{"key":"2023020207003314400_btx520-B2","doi-asserted-by":"crossref","first-page":"5756","DOI":"10.1073\/pnas.1420815112","article-title":"Continental-scale distributions of dust-associated bacteria and fungi","volume":"112","author":"Barber\u00e1n","year":"2015","journal-title":"PNAS"},{"key":"2023020207003314400_btx520-B3","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/nbt.3238","article-title":"Assembling large genomes with single-molecule sequencing and locality-sensitive hashing","volume":"33","author":"Berlin","year":"2015","journal-title":"Nat. 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