Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach.

Journal: BMC genomics
PMID: 28830349

Abstract

BACKGROUND: Small RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in the discovery of bacterial sRNAs. However, it is generally believed that the currently identified sRNAs constitute a limited subset of the bacterial sRNA repertoire. In several cases, sRNAs belonging to a specific class are already known and the challenge is to identify additional sRNAs belonging to the same class. In such cases, machine-learning approaches can be used to predict novel sRNAs in a given class.

Authors

  • Carl Tony Fakhry
    Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, MA, USA.
  • Prajna Kulkarni
    Department of Physics, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, MA, USA.
  • Ping Chen
    Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan 430060, China.
  • Rahul Kulkarni
    Department of Physics, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, MA, USA.
  • Kourosh Zarringhalam
    Department of Mathematics, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, MA, USA. kourosh.zarringhalam@umb.edu.

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