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Improvements to pairwise sequence comparison (PASC): a genome-based web tool for virus classification.

ABSTRACT: The number of viral genome sequences in the public databases is increasing dramatically, and these sequences are playing an important role in virus classification. Pairwise sequence comparison is a sequence-based virus classification method. A program using this method calculates the pairwise identities of virus sequences within a virus family and displays their distribution, and visual analysis helps to determine demarcations at different taxonomic levels such as strain, species, genus and subfamily. Subsequent comparison of new sequences against existing ones allows viruses from which the new sequences were derived to be classified. Although this method cannot be used as the only criterion for virus classification in some cases, it is a quantitative method and has many advantages over conventional virus classification methods. It has been applied to several virus families, and there is an increasing interest in using this method for other virus families/groups. The Pairwise Sequence Comparison (PASC) classification tool was created at the National Center for Biotechnology Information. The tool's database stores pairwise identities for complete genomes/segments of 56 virus families/groups. Data in the system are updated every day to reflect changes in virus taxonomy and additions of new virus sequences to the public database. The web interface of the tool ( http://www.ncbi.nlm.nih.gov/sutils/pasc/ ) makes it easy to navigate and perform analyses. Multiple new viral genome sequences can be tested simultaneously with this system to suggest the taxonomic position of virus isolates in a specific family. PASC eliminates potential discrepancies in the results caused by different algorithms and/or different data used by researchers.

SUBMITTER: Bao Y

PROVIDER: S-EPMC4221606 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundConventional pairwise sequence comparison software algorithms are being used to process much larger datasets than they were originally designed for. This can result in processing bottlenecks that limit software capabilities or prevent full use of the available hardware resources. Overcoming the barriers that limit the efficient computational analysis of large biological sequence datasets by retrofitting existing algorithms or by creating new applications represents a major challenge for the bioinformatics community.ResultsWe have developed C libraries for pairwise sequence comparison within diverse architectures, ranging from commodity systems to high performance and cloud computing environments. Exhaustive tests were performed using different datasets of closely- and distantly-related sequences that span from small viral genomes to large mammalian chromosomes. The tests demonstrated that our solution is capable of generating high quality results with a linear-time response and controlled memory consumption, being comparable or faster than the current state-of-the-art methods.ConclusionsWe have addressed the problem of pairwise and all-versus-all comparison of large sequences in general, greatly increasing the limits on input data size. The approach described here is based on a modular out-of-core strategy that uses secondary storage to avoid reaching memory limits during the identification of High-scoring Segment Pairs (HSPs) between the sequences under comparison. Software engineering concepts were applied to avoid intermediate result re-calculation, to minimise the performance impact of input/output (I/O) operations and to modularise the process, thus enhancing application flexibility and extendibility. Our computationally-efficient approach allows tasks such as the massive comparison of complete genomes, evolutionary event detection, the identification of conserved synteny blocks and inter-genome distance calculations to be performed more effectively.

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Improvements to pairwise sequence comparison (PASC): a genome-based web tool for virus classification.

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