Accurate annotation of metagenomic data without species-level references
by Haobin Yao; T.W. Lam; H.F. Ting; S.M. Yiu; Yadong Wang; Bo Liu
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 19, No. 4, 2017

Abstract: Taxonomic 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.

Online publication date: Fri, 27-Apr-2018

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