Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection-Reference-Cited by-同舟云学术

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

Published:2022-04-27 Issue:2 Volume:7 Page:
ISSN:2379-5042
Container-title:mSphere
language:en
Short-container-title:mSphere

Author:

Yoo Reo¹^ORCID,Rychel Kevin¹,Poudel Saugat¹,Al-bulushi Tahani¹,Yuan Yuan¹,Chauhan Siddharth¹,Lamoureux Cameron¹,Palsson Bernhard O.¹²,Sastry Anand¹

Affiliation:

1. Department of Bioengineering, University of California San Diego, La Jolla, California, USA

2. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark

Abstract

Mycobacterium tuberculosis H37Rv is one of the world's most impactful pathogens, and a large part of the success of the organism relies on the differential expression of its genes to adapt to its environment. The expression of the organism's genes is driven primarily by its transcriptional regulatory network, and most research on the TRN focuses on identifying and quantifying clusters of coregulated genes known as regulons.

Publisher

American Society for Microbiology

Subject

Molecular Biology,Microbiology

Link

https://journals.asm.org/doi/pdf/10.1128/msphere.00033-22

Reference80 articles.

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