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GISMO--gene identification using a support vector machine for ORF classification.

Lutz Krause·Alice C McHardy·Tim W Nattkemper·Alfred Pühler·Jens Stoye·Folker Meyer
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Abstract

We present the novel prokaryotic gene finder GISMO, which combines searches for protein family domains with composition-based classification based on a support vector machine. GISMO is highly accurate; exhibiting high sensitivity and specificity in gene identification. We found that it performs well for complete prokaryotic chromosomes, irrespective of their GC content, and also for plasmids as short as 10 kb, short genes and for genes with atypical sequence composition. Using GISMO, we found several thousand new predictions for the published genomes that are supported by extrinsic evidence, which strongly suggest that these are very likely biologically active genes. The source code for GISMO is freely available under the GPL license.

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AlgorithmsArtificial IntelligenceChromosomes, ArchaealChromosomes, BacterialClassificationGene Transfer, HorizontalGenes, ArchaealGenes, BacterialGenomicsOpen Reading FramesPlasmidsReproducibility of ResultsSoftwareJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.Validation Study

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Lutz Krause, Alice C McHardy, Tim W Nattkemper, Alfred Pühler, Jens Stoye, Folker Meyer. 2006-12-14. GISMO--gene identification using a support vector machine for ORF classification.. https://doi.org/10.1093/nar%2Fgkl1083

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