BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus

Area of Science:

• Microbiology
• Computational Biology
• Genomics

Background:

• Limited understanding of regulatory elements in Mycobacterium abscessus.
• Need to elucidate transcriptomic regulation for survival in hostile environments.

Purpose of the Study:

• To computationally infer the gene regulatory network (GRN) for Mycobacterium abscessus.
• To develop a novel statistical modeling approach for GRN inference.

Main Methods:

• Proposed BayesIan gene regulatory Networks inferreD via gene coExpression and compaRative genomics (BINDER).
• Integrated experimental coexpression data (RNA-seq) with genomic conservation properties.
• Utilized a hierarchical Bayesian framework combining primary data (M. abscessus) and auxiliary data (M. tuberculosis ChIP-seq).

Main Results:

• Implemented BINDER on 167,280 regulator-target pairs.
• Identified 54 high-probability regulator-target pairs involving 5 transcription factors.

Conclusions:

• Inferred regulatory interactions offer insights into transcriptional control in M. abscessus and Mycobacteriaceae.
• The BINDER framework is broadly applicable for GRN inference integrating diverse data sources.