Devising Isolation Forest-Based Method to Investigate the sRNAome of Mycobacterium tuberculosis Using sRNA-seq Data.

ABSTRACT: Small non-coding RNAs (sRNAs) regulate the synthesis of virulence factors and other pathogenic traits, which enables the bacteria to survive and proliferate after host infection. While high-throughput sequencing data have proved useful in identifying sRNAs from the intergenic regions (IGRs) of the genome, it remains a challenge to present a complete genome-wide map of the expression of the sRNAs. Moreover, existing methodologies necessitate multiple dependencies for executing their algorithm and also lack a targeted approach for the de novo sRNA identification. We developed an Isolation Forest algorithm-based method and the tool Prediction Of sRNAs using Isolation Forest for the de novo identification of sRNAs from available bacterial sRNA-seq data (http://posif.ibab.ac.in/).

SUBMITTER: Maity U 

PROVIDER: S-EPMC11292719 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature