Prediction of small non-coding RNA in bacterial genomes using support vector machines

Authors:
Tzu-Hao Chang;Li-Ching Wu;Jun-Hong Lin;Hsien-Da Huang;Baw-Jhiune Liu;Kuang-Fu Cheng;Jorng-Tzong Horng
Affiliations:
Department of Computer Science and Information Engineering, National Central University, Taiwan;Institute of Systems Biology and Bioinformatics, National Central University, Taiwan;Department of Computer Science and Information Engineering, National Central University, Taiwan;Department of Biological Science and Technology, Institute of Bioinformatics, National Chiao-Tung University, Taiwan;Department of Computer Science and Information Engineering, Yuan Ze University, Taiwan;Biostatistics Center and Department of Public Health, and Graduate Institute of Statistics, China Medical University, Taiwan;Department of Computer Science and Information Engineering, National Central University, Taiwan and Institute of Systems Biology and Bioinformatics, National Central University, Taiwan and Departm ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Small non-coding RNA genes have been shown to play important regulatory roles in a variety of cellular processes, but prediction of non-coding RNA genes is a great challenge, using either an experimental or a computational approach, due to the characteristics of sRNAs, which are that sRNAs are small in size, are not translated into proteins and show variable stability. Most known sRNAs have been identified in Escherichia coli and have been shown to be conserved in closely related organisms. We have developed an integrative approach that searches highly conserved intergenic regions among related bacterial genomes for combinations of characteristics that have been extracted from known E. coli sRNA genes. Support vector machines (SVM) were then used with these characteristics to predict novel sRNA genes.