An improved method for identification of small non-coding RNAs in bacteria using support vector machine

Bacterial small non-coding RNAs (sRNAs) are not translated into proteins, but act as functional RNAs. They are involved in diverse biological processes like virulence, stress response and quorum sensing. Several high-throughput techniques have enabled identification of sRNAs in bacteria, but experimental detection remains a challenge and grossly incomplete for most species. Thus, there is a need to develop computational tools to predict bacterial sRNAs. Here, we propose a computational method to identify sRNAs in bacteria using support vector machine (SVM) classifier. The primary sequence and secondary structure features of experimentally-validated sRNAs of Salmonella Typhimurium LT2 (SLT2) was used to build the optimal SVM model. We found that a tri-nucleotide composition feature of sRNAs achieved an accuracy of 88.35% for SLT2. We validated the SVM model also on the experimentally-detected sRNAs of E. coli and Salmonella Typhi. The proposed model had robustly attained an accuracy of 81.25% and 88.82% for E. coli K-12 and S. Typhi Ty2, respectively. We confirmed that this method significantly improved the identification of sRNAs in bacteria. Furthermore, we used a sliding window-based method and identified sRNAs from complete genomes of SLT2, S. Typhi Ty2 and E. coli K-12 with sensitivities of 89.09%, 83.33% and 67.39%, respectively.