A Hybrid Computational Approach for the Prediction of Small Non-coding RNAs from Genome Sequences

Researching the bacterial gene expression is a meaningful way to control and prevent the diseases which caused by bacteria. Recent researches indicate non-coding RNAs (ncRNA / sRNA) perform a variety of critical regulatory functions in bacteria. Since sRNAs have the consistent sequence characteristics, the genome-wide searching for sRNAs, especially the computational method, have become an effective way to predict the non-coding RNAs. This article proposes a hybrid computational approach for prediction of small non-coding RNAs which integrates three critical techniques, secondary structural algorithm, thermo-dynamic stability analysis and sequence conservation prediction. Relying on these computational techniques, our approach was used to search for sRNAs in Streptococcus pyogenes which is one of the most important bacteria for human health. This search led five candidates of sRNA to be predicted as the key components of known regulatory pathways in S. pyogens.