title: GISMO--gene identification using a support vector machine for ORF classification.
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.
authors.affiliation: Center for Biotechnology, Bielefeld University (CeBiTec), D-33594 Bielefeld, Germany. lutz.krause@cebitec.uni-bielefeld.de
authors.forename: Lutz
authors.initials: L
authors.lastname: Krause
authors.forename: Alice C
authors.initials: AC
authors.lastname: McHardy
authors.forename: Tim W
authors.initials: TW
authors.lastname: Nattkemper
authors.forename: Alfred
authors.initials: A
authors.lastname: Pühler
authors.forename: Jens
authors.initials: J
authors.lastname: Stoye
authors.forename: Folker
authors.initials: F
authors.lastname: Meyer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019847:Chromosomes, Archaeal; D002876:Chromosomes, Bacterial; D002965:Classification / Q000379:methods; D022761:Gene Transfer, Horizontal; D019766:Genes, Archaeal; D005798:Genes, Bacterial; D023281:Genomics / Q000379:methods*; D016366:Open Reading Frames; D010957:Plasmids / Q000235:genetics; D015203:Reproducibility of Results; D012984:Software