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2020-07-13Zeitschriftenartikel
ganon: precise metagenomics classification against large and up-to-date sets of reference sequences
dc.contributor.authorPiro, Victor C.
dc.contributor.authorDadi, Temesgen H.
dc.contributor.authorSeiler, Enrico
dc.contributor.authorReinert, Knut
dc.contributor.authorRenard, Bernhard Y.
dc.date.accessioned2024-01-04T15:20:16Z
dc.date.available2024-01-04T15:20:16Z
dc.date.issued2020-07-13none
dc.identifier.other10.1093/bioinformatics/btaa458
dc.identifier.urihttp://edoc.rki.de/176904/11447
dc.description.abstractMotivation The exponential growth of assembled genome sequences greatly benefits metagenomics studies. However, currently available methods struggle to manage the increasing amount of sequences and their frequent updates. Indexing the current RefSeq can take days and hundreds of GB of memory on large servers. Few methods address these issues thus far, and even though many can theoretically handle large amounts of references, time/memory requirements are prohibitive in practice. As a result, many studies that require sequence classification use often outdated and almost never truly up-to-date indices. Results Motivated by those limitations, we created ganon, a k-mer-based read classification tool that uses Interleaved Bloom Filters in conjunction with a taxonomic clustering and a k-mer counting/filtering scheme. Ganon provides an efficient method for indexing references, keeping them updated. It requires <55 min to index the complete RefSeq of bacteria, archaea, fungi and viruses. The tool can further keep these indices up-to-date in a fraction of the time necessary to create them. Ganon makes it possible to query against very large reference sets and therefore it classifies significantly more reads and identifies more species than similar methods. When classifying a high-complexity CAMI challenge dataset against complete genomes from RefSeq, ganon shows strongly increased precision with equal or better sensitivity compared with state-of-the-art tools. With the same dataset against the complete RefSeq, ganon improved the F1-score by 65% at the genus level. It supports taxonomy- and assembly-level classification, multiple indices and hierarchical classification.eng
dc.language.isoengnone
dc.publisherRobert Koch-Institut
dc.rights(CC BY 3.0 DE) Namensnennung 3.0 Deutschlandger
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/de/
dc.subject.ddc610 Medizin und Gesundheitnone
dc.titleganon: precise metagenomics classification against large and up-to-date sets of reference sequencesnone
dc.typearticle
dc.identifier.urnurn:nbn:de:0257-176904/11447-9
dc.type.versionpublishedVersionnone
local.edoc.container-titleBioinformaticsnone
local.edoc.container-issn1367-4811none
local.edoc.pages9none
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-urlhttps://academic.oup.com/bioinformaticsnone
local.edoc.container-publisher-nameOxford University Pressnone
local.edoc.container-volume36none
local.edoc.container-issueS1none
local.edoc.container-reportyear2020none
local.edoc.container-firstpagei12none
local.edoc.container-lastpagei20none
dc.description.versionPeer Reviewednone

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