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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\n                    <jats:underline>ChIP<\/jats:underline>\n                    -seq data-driven\n                    <jats:underline>G<\/jats:underline>\n                    ibbs\n                    <jats:underline>S<\/jats:underline>\n                    ampler to infer\n                    <jats:underline>M<\/jats:underline>\n                    odules (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.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1009203","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T13:55:25Z","timestamp":1626962125000},"page":"e1009203","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3782-9029","authenticated-orcid":true,"given":"Xi","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0086-5755","authenticated-orcid":true,"given":"Andrew 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