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.

SUMMARY: Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) generates local accumulations of sequencing reads on the genome ("peaks"), which correspond to specific protein-DNA interactions or chromatin modifications. Peaks are detected by considering their total area above a background signal, usually neglecting their shapes, which instead may convey additional biological information. We present FunChIP, an R/Bioconductor package for clustering peaks according to a functional representation of their shapes: after approximating their profiles with cubic B-splines, FunChIP minimizes their functional distance and classifies the peaks applying a k-mean alignment and clustering algorithm. The whole pipeline is user-friendly and provides visualization functions for a quick inspection of the results. An application to the transcription factor Myc in 3T9 murine fibroblasts shows that clusters of peaks with different shapes are associated with different genomic locations and different transcriptional regulatory activity. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and is available under Artistic Licence 2.0 from the Bioconductor website (http://bioconductor.org/packages/FunChIP). CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.