416-421<span style="font-size:11.0pt;mso-bidi-font-size:
9.0pt;font-family:" times="" new="" roman","serif";mso-fareast-font-family:"times="" roman";="" letter-spacing:-.1pt;mso-ansi-language:en-gb;mso-fareast-language:en-us;="" mso-bidi-language:ar-sa"="" lang="EN-GB">Although non-coding RNA (ncRNA) genes do not encode
proteins, they play vital roles in cells by producing functionally important
RNAs. In this paper, we present a novel method for predicting ncRNA genes based
on compositional features extracted directly from gene sequences. Our method
consists of two Support Vector Machine (SVM) models β Codon model which uses
codon usage features derived from ncRNA genes and protein-coding genes and Kmer
model which utilizes features of nucleotide and dinucleotide frequency
extracted respectively from ncRNA genes and randomly chosen genome sequences.
The 10-fold cross-validation accuracy for the two models is found to be 92% and
91%, respectively. Thus, we could make an automatic prediction of ncRNA genes
in one genome without manual filtration of protein-coding genes. After applying
our method in Sulfolobus solfataricus
genome, 25 prediction results have been generated according to 25 cut-off
pairs. We have also applied the approach in E.
coli and found our results comparable to those of previous studies. In
general, our method enables automatic identification of ncRNA genes in newly
sequenced prokaryotic genomes. Datasets and program code used in this work are
available at http://cobi.uestc.edu.cn/resource/SS_ncRNA/</span