Enhanced Prediction of Small Non-coding RNA in Bacterial Genomes Based on Improved Inter-Nucleotide Distances of Genomes

Abstract

Small non-coding RNA genes have been concerned as an important field of life sciences in recent years. It plays important regulatory roles in cellular processes. However, the prediction of noncoding RNA genes is a great challenge, because non-coding RNAs have a small size, are not translated into proteins and show variable stability. In this paper, we propose an improved inter-nucleotide distances model as sequence characteristics, and combine with support vector machines (SVM) to predict small non-coding RNA in bacterial genomes. The prediction result of the mixed bacterial ncRNA is 95.38%, which shows that our method can effectively predict bacterial ncRNAs. Keywords: Small non-coding RNA, inter-nucleotide distances, prediction, support vector machines, machine learning.