ProViDE: A software tool for accurate estimation of viral diversity in metagenomic samples


Tarini Shankar Ghosh, Monzoorul Haque Mohammed, Dinakar Komanduri, Sharmila Shekhar Mande*


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

Email; *Corresponding author

Article Type




Received March 04, 2011; Accepted March 07, 2011; Published March 26, 2011



Given the absence of universal marker genes in the viral kingdom, researchers typically use BLAST (with stringent E-values) for taxonomic classification of viral metagenomic sequences. Since majority of metagenomic sequences originate from hitherto unknown viral groups, using stringent e-values results in most sequences remaining unclassified. Furthermore, using less stringent e-values results in a high number of incorrect taxonomic assignments. The SOrt-ITEMS algorithm provides an approach to address the above issues. Based on alignment parameters, SOrt-ITEMS follows an elaborate work-flow for assigning reads originating from hitherto unknown archaeal/bacterial genomes. In SOrt-ITEMS, alignment parameter thresholds were generated by observing patterns of sequence divergence within and across various taxonomic groups belonging to bacterial and archaeal kingdoms. However, many taxonomic groups within the viral kingdom lack a typical Linnean-like taxonomic hierarchy. In this paper, we present ProViDE (Program for Viral Diversity Estimation), an algorithm that uses a customized set of alignment parameter thresholds, specifically suited for viral metagenomic sequences. These thresholds capture the pattern of sequence divergence and the non-uniform taxonomic hierarchy observed within/across various taxonomic groups of the viral kingdom. Validation results indicate that the percentage of 'correct' assignments by ProViDE is around 1.7 to 3 times higher than that by the widely used similarity based method MEGAN. The misclassification rate of ProViDE is around 3 to 19% (as compared to 5 to 42% by MEGAN) indicating significantly better assignment accuracy. ProViDE software and a supplementary file (containing supplementary figures and tables referred to in this article) is available for download from 




Ghosh et al. Bioinformation 6(2): 91-94 (2011)

Edited by

TW Tan






Biomedical Informatics



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