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Article: Accurate annotation of metagenomic data without species-level reference

TitleAccurate annotation of metagenomic data without species-level reference
Authors
KeywordsAccurate
Binning
Fast annotation
Metagenomic data analysis
Issue Date2017
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijdmb
Citation
International Journal of Data Mining and Bioinformatics, 2017, v. 19 n. 4, p. 283-297 How to Cite?
AbstractTaxonomic annotation is a critical first step for analysis of metagenomic data. Despite a lot of tools being developed, the accuracy is still not satisfactory, in particular, when a close species-level reference does not exist in the database. In this paper, we propose a novel annotation tool, MetaAnnotator, to annotate metagenomic reads, which outperforms all existing tools significantly when only genus-level references exist in the database. From our experiments, MetaAnnotator can assign 87.5% reads correctly (67.5% reads are assigned to the exact genus) with only 8.5% reads wrongly assigned. The best existing tool (MetaCluster-TA) can only achieve 73.4% correct read assignment (with only 50.9% reads assigned to the exact genus and 22.6% reads wrongly assigned). The speed of MetaAnnotator is also the second faster (1 hour for 20 million reads). The core concepts behind MetaAnnotator includes: (i) we only consider exact k-mers in coding regions of the references as they should be more significant and accurate; (ii) to assign reads to taxonomy nodes, we construct genome and taxonomy specific probabilistic models from the reference database; and (iii) using the BWT data structure to speed up the k-mer matching process.
Persistent Identifierhttp://hdl.handle.net/10722/260852
ISSN
2017 Impact Factor: 0.652
2015 SCImago Journal Rankings: 0.289

 

DC FieldValueLanguage
dc.contributor.authorYao, H-
dc.contributor.authorLam, TW-
dc.contributor.authorTing, HF-
dc.contributor.authorYiu, SM-
dc.contributor.authorWang, YD-
dc.contributor.authorLiu, B-
dc.date.accessioned2018-09-14T08:48:31Z-
dc.date.available2018-09-14T08:48:31Z-
dc.date.issued2017-
dc.identifier.citationInternational Journal of Data Mining and Bioinformatics, 2017, v. 19 n. 4, p. 283-297-
dc.identifier.issn1748-5673-
dc.identifier.urihttp://hdl.handle.net/10722/260852-
dc.description.abstractTaxonomic annotation is a critical first step for analysis of metagenomic data. Despite a lot of tools being developed, the accuracy is still not satisfactory, in particular, when a close species-level reference does not exist in the database. In this paper, we propose a novel annotation tool, MetaAnnotator, to annotate metagenomic reads, which outperforms all existing tools significantly when only genus-level references exist in the database. From our experiments, MetaAnnotator can assign 87.5% reads correctly (67.5% reads are assigned to the exact genus) with only 8.5% reads wrongly assigned. The best existing tool (MetaCluster-TA) can only achieve 73.4% correct read assignment (with only 50.9% reads assigned to the exact genus and 22.6% reads wrongly assigned). The speed of MetaAnnotator is also the second faster (1 hour for 20 million reads). The core concepts behind MetaAnnotator includes: (i) we only consider exact k-mers in coding regions of the references as they should be more significant and accurate; (ii) to assign reads to taxonomy nodes, we construct genome and taxonomy specific probabilistic models from the reference database; and (iii) using the BWT data structure to speed up the k-mer matching process.-
dc.languageeng-
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijdmb-
dc.relation.ispartofInternational Journal of Data Mining and Bioinformatics-
dc.rightsInternational Journal of Data Mining and Bioinformatics. Copyright © Inderscience Publishers.-
dc.subjectAccurate-
dc.subjectBinning-
dc.subjectFast annotation-
dc.subjectMetagenomic data analysis-
dc.titleAccurate annotation of metagenomic data without species-level reference-
dc.typeArticle-
dc.identifier.emailLam, TW: twlam@cs.hku.hk-
dc.identifier.emailTing, HF: hfting@cs.hku.hk-
dc.identifier.emailYiu, SM: smyiu@cs.hku.hk-
dc.identifier.authorityLam, TW=rp00135-
dc.identifier.authorityTing, HF=rp00177-
dc.identifier.authorityYiu, SM=rp00207-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1504/IJDMB.2017.091354-
dc.identifier.scopuseid_2-s2.0-85046281894-
dc.identifier.hkuros290695-
dc.identifier.volume19-
dc.identifier.issue4-
dc.identifier.spage283-
dc.identifier.epage297-
dc.publisher.placeUnited Kingdom-

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