Erschienen in:
Bioinformatics, 36 (2020) 4, Seite 1007-1013
Sprache:
Englisch
DOI:
10.1093/bioinformatics/btz687
ISSN:
1367-4803;
1367-4811
Entstehung:
Anmerkungen:
Beschreibung:
Abstract Motivation Genome regulatory networks have different layers and ways to modulate cellular processes, such as cell differentiation, proliferation, and adaptation to external stimuli. Transcription factors and other chromatin-associated proteins act as combinatorial protein complexes that control gene transcription. Thus, identifying functional interaction networks among these proteins is a fundamental task to understand the genome regulation framework. Results We developed a novel approach to infer interactions among transcription factors in user-selected genomic regions, by combining the computation of association rules and of a novel Importance Index on ChIP-seq datasets. The hallmark of our method is the definition of the Importance Index, which provides a relevance measure of the interaction among transcription factors found associated in the computed rules. Examples on synthetic data explain the index use and potential. A straightforward pre-processing pipeline enables the easy extraction of input data for our approach from any set of ChIP-seq experiments. Applications on ENCODE ChIP-seq data prove that our approach can reliably detect interactions between transcription factors, including known interactions that validate our approach. Availability and implementation A R/Bioconductor package implementing our association rules and Importance Index-based method is available at http://bioconductor.org/packages/release/bioc/html/TFARM.html. Supplementary information Supplementary data are available at Bioinformatics online.