miBLAST: scalable evaluation of a batch of nucleotide sequence queries with BLAST - PubMed (original) (raw)

miBLAST: scalable evaluation of a batch of nucleotide sequence queries with BLAST

You Jung Kim et al. Nucleic Acids Res. 2005.

Abstract

A common task in many modern bioinformatics applications is to match a set of nucleotide query sequences against a large sequence dataset. Existing tools, such as BLAST, are designed to evaluate a single query at a time and can be unacceptably slow when the number of sequences in the query set is large. In this paper, we present a new algorithm, called miBLAST, that evaluates such batch workloads efficiently. At the core, miBLAST employs a q-gram filtering and an index join for efficiently detecting similarity between the query sequences and database sequences. This set-oriented technique, which indexes both the query and the database sets, results in substantial performance improvements over existing methods. Our results show that miBLAST is significantly faster than BLAST in many cases. For example, miBLAST aligned 247 965 oligonucleotide sequences in the Affymetrix probe set against the Human UniGene in 1.26 days, compared with 27.27 days with BLAST (an improvement by a factor of 22). The relative performance of miBLAST increases for larger word sizes; however, it decreases for longer queries. miBLAST employs the familiar BLAST statistical model and output format, guaranteeing the same accuracy as BLAST and facilitating a seamless transition for existing BLAST users.

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Figures

Figure 1

Figure 1

This graph shows the relative speedup of each method compared with naive BLAST, for various workload sizes using a word size of 11. (a) Affymetrix (25 bp). (b) Illumina (70 bp).

Figure 2

Figure 2

This graph shows the effect of the BLAST word size parameter on the query performance for each method, plotted as relative speedup over naive BLAST. The batch size used in this experiment is 4000 queries. miBLAST uses an index word size of 11 and uses a sliding-window filtering method for query word sizes between 14 and 23. (a) Affymetrix (25 bp). (b) Illumina (70 bp).

Figure 3

Figure 3

This graph shows the relative speedup to naive BLAST for various query lengths, using a word size of 11. Queries are drawn from the EST human dataset, and each batch has 1000 queries.

Figure 4

Figure 4

This graph shows the execution time of each method for various word sizes using a batch of 1000 queries from the Affymetrix probe set (25 bp).

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References

    1. Lachance P.E., Chaudhuri A. Microaarry analysis of developmental plasticity in monkey primary visual cortex. J. Neurochem. 2004;88:1455–1469. - PubMed
    1. Uddin M., Wildman D.F., Liu G., Xu W., Johnson R.W., Hof P.R., Kapatos G., Grossman L.I., Goodman M. Sister grouping of chimpanzees and humans as revealed by genome-wide phylogentic analysis of brain gene expression profiles. Proc. Natl Acad. Sci. USA. 2004;101:2957–2962. - PMC - PubMed
    1. Altschul S.F., Gish W., Miller W., Myers E.W., Lipman D.J. Basic local alignment search tool. J. Mol. Biol. 1990;215:403–410. - PubMed
    1. Altschul S., Gish W. Local alignment statistics. Meth. Enzymol. 1996;266:460–480. - PubMed
    1. Kent W.J. BLAT-the BLAST-like alignment tool. Genome Res. 2002;12:656–664. - PMC - PubMed

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