Mahony Shaun, McInerney James O, Smith Terry J, Golden Aaron
National Centre for Biomedical Engineering Science, NUI, Galway, Galway, Ireland.
BMC Bioinformatics. 2004 Mar 5;5:23. doi: 10.1186/1471-2105-5-23.
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.
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.
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.
许多当前的基因预测方法仅使用一种模型来表示基因组中的蛋白质编码区域，因此不太可能预测具有非典型序列组成的基因的位置。未来基因发现的改进可能会涉及开发能够充分处理基因组内组成变异的方法。
这项工作探索了一种基于自组织映射的新的基因预测方法，该方法能够自动识别基因组内的多个基因模型。当前的实现版本名为RescueNet，它使用相对同义密码子使用情况作为蛋白质编码潜力的指标。
虽然其原始准确率可能低于其他方法，但RescueNet始终能识别出其他方法无法识别的一些基因，因此基因预测软件开发人员和基因组注释团队都应该对它感兴趣。建议将RescueNet与其他基因预测方法结合使用或作为其补充。
