Mahony, Shaun and McInerney, James O. and Smith, Terry J. and Golden, Aaron (2004) Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models. BMC Bioinformatics, 5 (1). p. 23.

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Abstract

Background: Many current gene prediction methods use only one model to represent proteincoding 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 geneprediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

Item Type:	Article
Additional Information:	© 2004 Mahony et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
Keywords:	Gene prediction; Self-Organizing Map; automatic generation; multiple gene models;
Academic Unit:	Faculty of Science and Engineering > Biology
Item ID:	413
Depositing User:	Dr. James McInerney
Date Deposited:	04 Oct 2006
Journal or Publication Title:	BMC Bioinformatics
Publisher:	BioMed Central Ltd
Refereed:	Yes
URI:	http://www.biomedcentral.com/1471-2105/5...
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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