Yang G, Ma A, Qin ZS, Chen L. Application of topic models to a compendium of ChIP-Seq datasets uncovers recurrent transcriptional regulatory modules.
Bioinformatics 2020;
36:2352-2358. [PMID:
31899481 DOI:
10.1093/bioinformatics/btz975]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 10/29/2019] [Accepted: 12/30/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION
The availability of thousands of genome-wide coupling chromatin immunoprecipitation (ChIP)-Seq datasets across hundreds of transcription factors (TFs) and cell lines provides an unprecedented opportunity to jointly analyze large-scale TF-binding in vivo, making possible the discovery of the potential interaction and cooperation among different TFs. The interacted and cooperated TFs can potentially form a transcriptional regulatory module (TRM) (e.g. co-binding TFs), which helps decipher the combinatorial regulatory mechanisms.
RESULTS
We develop a computational method tfLDA to apply state-of-the-art topic models to multiple ChIP-Seq datasets to decipher the combinatorial binding events of multiple TFs. tfLDA is able to learn high-order combinatorial binding patterns of TFs from multiple ChIP-Seq profiles, interpret and visualize the combinatorial patterns. We apply the tfLDA to two cell lines with a rich collection of TFs and identify combinatorial binding patterns that show well-known TRMs and related TF co-binding events.
AVAILABILITY AND IMPLEMENTATION
A software R package tfLDA is freely available at https://github.com/lichen-lab/tfLDA.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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