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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
Comparative evaluation of binning time. Plot comparing the time taken (in minutes) by DiScrIBinATE (black-solid line), MEGAN (grey-solid line) and SOrt-ITEMS (grey-dotted line) for binning 100000, 200000, 500000 and 1000000 metagenomic reads. The tests were performed on an Intel (R) Xeon CPU workstation (with a 1.86 Ghz processor).
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Figure 3: Comparative evaluation of binning time. Plot comparing the time taken (in minutes) by DiScrIBinATE (black-solid line), MEGAN (grey-solid line) and SOrt-ITEMS (grey-dotted line) for binning 100000, 200000, 500000 and 1000000 metagenomic reads. The tests were performed on an Intel (R) Xeon CPU workstation (with a 1.86 Ghz processor).

Mentions: As shown in Figure 3, DiScrIBinATE takes just half the time (similar to MEGAN), as compared to SOrt-ITEMS, for binning an equivalent number of reads. This is expected since the 'bit-score/distance ratio' approach adopted in DiScRIBinATE is an alignment-free method and involves simple mathematical calculations, as against the orthology step (involving alignment of sequences) used in SOrt-ITEMS.


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

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

Comparative evaluation of binning time. Plot comparing the time taken (in minutes) by DiScrIBinATE (black-solid line), MEGAN (grey-solid line) and SOrt-ITEMS (grey-dotted line) for binning 100000, 200000, 500000 and 1000000 metagenomic reads. The tests were performed on an Intel (R) Xeon CPU workstation (with a 1.86 Ghz processor).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Comparative evaluation of binning time. Plot comparing the time taken (in minutes) by DiScrIBinATE (black-solid line), MEGAN (grey-solid line) and SOrt-ITEMS (grey-dotted line) for binning 100000, 200000, 500000 and 1000000 metagenomic reads. The tests were performed on an Intel (R) Xeon CPU workstation (with a 1.86 Ghz processor).
Mentions: As shown in Figure 3, DiScrIBinATE takes just half the time (similar to MEGAN), as compared to SOrt-ITEMS, for binning an equivalent number of reads. This is expected since the 'bit-score/distance ratio' approach adopted in DiScRIBinATE is an alignment-free method and involves simple mathematical calculations, as against the orthology step (involving alignment of sequences) used in SOrt-ITEMS.

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