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dc.contributor.authorMcInerney, James Oen
dc.contributor.authorSmith, Terryen
dc.contributor.authorMahony, Shaunen
dc.contributor.authorGolden, Aaronen
dc.identifier.citationMahony, S. McInerney, J.O., Smith, T.J., Golden, A. (2004). Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models. BMC Bioinformatics 2004, 5:23-32.en
dc.description.abstractBackground: Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. Results: This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. Conclusions: While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.en
dc.publisherBioMed Centralen
dc.subjectHorizontally Transferred Genesen
dc.subjectSynonymous Codon Usageen
dc.subjectComplete Genome Sequenceen
dc.subjectBacterial Genomeen
dc.subjectBase Compositionen
dc.subject.lcshBacterial genomesen
dc.subject.lcshBacterial geneticsen
dc.titleGene prediction using the Self-Organizing Map: automatic generation of multiple gene modelsen

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