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dc.contributor.authorMahony, Shaun
dc.contributor.authorMcInerney, James O
dc.contributor.authorSmith, Terry J
dc.contributor.authorGolden, Aaron
dc.date.accessioned2018-08-24T08:25:32Z
dc.date.available2018-08-24T08:25:32Z
dc.date.issued2004-01-01
dc.identifier.citationMahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron (2004). . BMC Bioinformatics 5 ,
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/10379/9479
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.
dc.publisherSpringer Nature
dc.relation.ispartofBMC Bioinformatics
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectcomplete genome sequence
dc.subjecthorizontally transferred genes
dc.subjectsynonymous codon usage
dc.subjectborrelia-burgdorferi
dc.subjectbacterial genome
dc.subjectbase composition
dc.subjectselection
dc.subjectidentification
dc.subjectarabidopsis
dc.subjectannotation
dc.title
dc.typeArticle
dc.identifier.doi10.1186/1471-2105-5-23
dc.local.publishedsourcehttps://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-5-23?site=bmcbioinformatics.biomedcentral.com
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland