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

Ghosh TS, Monzoorul Haque M, Mande SS - BMC Bioinformatics (2010)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bio-Sciences Division, Innovation Labs, Tata Consultancy Services, 1 Software Units Layout, Hyderabad 500 081, Andhra Pradesh, India. tarini@atc.tcs.com

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 (Distance Score Ratio for Improved Binning And Taxonomic Estimation). 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.

Show MeSH
The reclassification steps of the DiScRIBinATE algorithm. Flowchart illustrating the reclassification steps followed by DiScRIBinATE (Step 4).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2957682&req=5

Figure 2: The reclassification steps of the DiScRIBinATE algorithm. Flowchart illustrating the reclassification steps followed by DiScRIBinATE (Step 4).

Mentions: Once the taxa names corresponding to the hits are substituted with the corresponding taxa names occurring at their respective TLs, reads with only one hit are assigned to the substituted taxon/clade corresponding to the hit. If a read has two hits, it is assigned to the LCA of the taxa/clades corresponding to these two hits. However, for reads with three or more hits, the following steps are performed for the final assignment of the read. The flowchart illustrating the first three steps of the 'DiScRIBinATE' work-flow is given in Figure 1 and that of the last step (step 4) is given in Figure 2.


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

Ghosh TS, Monzoorul Haque M, Mande SS - BMC Bioinformatics (2010)

The reclassification steps of the DiScRIBinATE algorithm. Flowchart illustrating the reclassification steps followed by DiScRIBinATE (Step 4).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2957682&req=5

Figure 2: The reclassification steps of the DiScRIBinATE algorithm. Flowchart illustrating the reclassification steps followed by DiScRIBinATE (Step 4).
Mentions: Once the taxa names corresponding to the hits are substituted with the corresponding taxa names occurring at their respective TLs, reads with only one hit are assigned to the substituted taxon/clade corresponding to the hit. If a read has two hits, it is assigned to the LCA of the taxa/clades corresponding to these two hits. However, for reads with three or more hits, the following steps are performed for the final assignment of the read. The flowchart illustrating the first three steps of the 'DiScRIBinATE' work-flow is given in Figure 1 and that of the last step (step 4) is given in Figure 2.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bio-Sciences Division, Innovation Labs, Tata Consultancy Services, 1 Software Units Layout, Hyderabad 500 081, Andhra Pradesh, India. tarini@atc.tcs.com

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 (Distance Score Ratio for Improved Binning And Taxonomic Estimation). 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.

Show MeSH