ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements

Xi Chen, Andrew F Neuwald, Leena Hilakivi-Clarke, Robert Clarke and Jianhua Xuan

PLOS Computational Biology, 2021, vol. 17, issue 7, 1-22

Abstract: Transcription factors (TFs) often function as a module including both master factors and mediators binding at cis-regulatory regions to modulate nearby gene transcription. ChIP-seq profiling of multiple TFs makes it feasible to infer functional TF modules. However, when inferring TF modules based on co-localization of ChIP-seq peaks, often many weak binding events are missed, especially for mediators, resulting in incomplete identification of modules. To address this problem, we develop a ChIP-seq data-driven Gibbs Sampler to infer Modules (ChIP-GSM) using a Bayesian framework that integrates ChIP-seq profiles of multiple TFs. ChIP-GSM samples read counts of module TFs iteratively to estimate the binding potential of a module to each region and, across all regions, estimates the module abundance. Using inferred module-region probabilistic bindings as feature units, ChIP-GSM then employs logistic regression to predict active regulatory elements. Validation of ChIP-GSM predicted regulatory regions on multiple independent datasets sharing the same context confirms the advantage of using TF modules for predicting regulatory activity. In a case study of K562 cells, we demonstrate that the ChIP-GSM inferred modules form as groups, activate gene expression at different time points, and mediate diverse functional cellular processes. Hence, ChIP-GSM infers biologically meaningful TF modules and improves the prediction accuracy of regulatory region activities.Author summary: Investigating TF binding to different types of regulatory regions can help reveal underlying activation mechanisms. However, accurately inferring modules among a large set of TFs is challenging due to the existence of weak, noisy, and context-sensitive binding signals. To reliably infer TF modules, here we describe ChIP-GSM, a Gibbs sampler built upon a Bayesian framework, that can further predict active regulatory elements. A comparison with other methods demonstrates ChIP-GSM’s improved performance on module identification and active regulatory element prediction. Experimental results demonstrate that TF modules identified by ChIP-GSM are likely mediating distinct cellular functions by activating regulatory regions at different time points.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1009203

DOI: 10.1371/journal.pcbi.1009203

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