Prioritizing bona fide bacterial small RNAs with machine learning classifiers.

ABSTRACT: Bacterial small (sRNAs) are involved in the control of several cellular processes. Hundreds of putative sRNAs have been identified in many bacterial species through RNA sequencing. The existence of putative sRNAs is usually validated by Northern blot analysis. However, the large amount of novel putative sRNAs reported in the literature makes it impractical to validate each of them in the wet lab. In this work, we applied five machine learning approaches to construct twenty models to discriminate bona fide sRNAs from random genomic sequences in five bacterial species. Sequences were represented using seven features including free energy of their predicted secondary structure, their distances to the closest predicted promoter site and Rho-independent terminator, and their distance to the clo

SUBMITTER: Eppenhof EJJ 

PROVIDER: S-EPMC6348098 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature