Predicting and classifying short non-coding RNAs using a multiclass evolutionary methodology

High throughput sequencing technologies alongside with advanced bioinformatics methods have uncovered a vast number of short non-coding RNAs. They are split in various categories based on their cellular functionality and their sequential, thermodynamic and structural properties. Existing computational methods emphasize on the prediction of only one type of non-coding RNAs and thus their applicability in full transcriptome studies is limited. Only a few methods so far have been proposed for predicting multiple short non-coding RNAs and they do not cover the most significant classes of them. In the present paper, we introduce a new multiclass method based on the combination of genetic algorithms and support vector machines which is able to distinguish among tRNAs, miRNAs, snoRNAs, rRNAs and other RNA sequences with accuracy over 93%. Finally, the advanced feature selection mechanism of the proposed method uncovers significant characteristics for each one of the studied short non-coding RNAs.

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