Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection.

ABSTRACT: Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning meth

SUBMITTER: Yoo R 

PROVIDER: S-EPMC9044949 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature