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This article is part of the supplement: Ninth International Conference on Bioinformatics (InCoB2010): Bioinformatics

Open Access Proceedings

DiScRIBinATE: a rapid method for accurate taxonomic classification of metagenomic sequences

Tarini Shankar Ghosh, Monzoorul Haque M and Sharmila S Mande*

Author Affiliations

Bio-Sciences Division, Innovation Labs, Tata Consultancy Services, 1 Software Units Layout, Hyderabad 500 081, Andhra Pradesh, India

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BMC Bioinformatics 2010, 11(Suppl 7):S14  doi:10.1186/1471-2105-11-S7-S14

Published: 15 October 2010

Abstract

Background

In metagenomic sequence data, majority of sequences/reads originate from new or partially characterized genomes, the corresponding sequences of which are absent in existing reference databases. Since taxonomic assignment of reads is based on their similarity to sequences from known organisms, the presence of reads originating from new organisms poses a major challenge to taxonomic binning methods. The recently published SOrt-ITEMS algorithm uses an elaborate work-flow to assign reads originating from hitherto unknown genomes with significant accuracy and specificity. Nevertheless, a significant proportion of reads still get misclassified. Besides, the use of an alignment-based orthology step (for improving the specificity of assignments) increases the total binning time of SOrt-ITEMS.

Results

In this paper, we introduce a rapid binning approach called DiScRIBinATE (

    Di
stance
    Sc
ore
    R
atio for
    I
mproved
    Bin
ning
    A
nd
    T
axonomic
    E
stimation). DiScRIBinATE replaces the orthology approach of SOrt-ITEMS with a quicker 'alignment-free' approach. We demonstrate that incorporating this approach reduces binning time by half without any loss in the specificity and accuracy of assignments. Besides, a novel reclassification strategy incorporated in DiScRIBinATE results in reducing the overall misclassification rate to around 3 - 7%. This misclassification rate is 1.5 - 3 times lower as compared to that by SOrt-ITEMS, and 3 - 30 times lower as compared to that by MEGAN.

Conclusions

A significant reduction in binning time, coupled with a superior assignment accuracy (as compared to existing binning methods), indicates the immense applicability of the proposed algorithm in rapidly mapping the taxonomic diversity of large metagenomic samples with high accuracy and specificity.

Availability

The program is available on request from the authors.