Statistics of local multiple alignments.

Amol Prakash; Martin Tompa

doi:10.1093/bioinformatics/bti1042

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PubMed · 15961477

Statistics of local multiple alignments.

Amol Prakash·Martin Tompa

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Abstract

SUMMARY: BLAST statistics have been shown to be extremely useful for searching for significant similarity hits, for amino acid and nucleotide sequences. Although these statistics are well understood for pairwise comparisons, there has been little success developing statistical scores for multiple alignments. In particular, there is no score for multiple alignment that is well founded and treated as a standard. We extend the BLAST theory to multiple alignments. Following some simple assumptions, we present and justify a significance score for multiple segments of a local multiple alignment. We demonstrate its usefulness in distinguishing high and moderate quality multiple alignments from low quality ones, with supporting experiments on orthologous vertebrate promoter sequences.

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Algorithms Animals Base Sequence Computational Biology Computer Simulation Evolution, Molecular Humans Models, Statistical Molecular Sequence Data Phylogeny Promoter Regions, Genetic Reproducibility of Results Sequence Alignment Sequence Analysis, Protein Software Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

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Amol Prakash, Martin Tompa. 2005. Statistics of local multiple alignments.. https://doi.org/10.1093/bioinformatics%2Fbti1042

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