Modular combinatorial binding among human trans-acting factors reveals direct and indirect factor binding
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12864_2016_Article_3434.pdf
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Author(s)
Guo, Yuchun
Gifford, David K
Date Issued
January 2017
Journal
BMC Genomics
Publisher
BioMed Central
Citation
Guo, Yuchun, and David K. Gifford. “Modular Combinatorial Binding among Human Trans-Acting Factors Reveals Direct and Indirect Factor Binding.” BMC Genomics 18.1 (2017): n. pag.
Version
Final published version
Abstract
Background
The combinatorial binding of trans-acting factors (TFs) to the DNA is critical to the spatial and temporal specificity of gene regulation. For certain regulatory regions, more than one regulatory module (set of TFs that bind together) are combined to achieve context-specific gene regulation. However, previous approaches are limited to either pairwise TF co-association analysis or assuming that only one module is used in each regulatory region.
Results
We present a new computational approach that models the modular organization of TF combinatorial binding. Our method learns compact and coherent regulatory modules from in vivo binding data using a topic model. We found that the binding of 115 TFs in K562 cells can be organized into 49 interpretable modules. Furthermore, we found that tens of thousands of regulatory regions use multiple modules, a structure that cannot be observed with previous hard clustering based methods. The modules discovered recapitulate many published protein-protein physical interactions, have consistent functional annotations of chromatin states, and uncover context specific co-binding such as gene proximal binding of NFY + FOS + SP and distal binding of NFY + FOS + USF. For certain TFs, the co-binding partners of direct binding (motif present) differs from those of indirect binding (motif absent); the distinct set of co-binding partners can predict whether the TF binds directly or indirectly with up to 95% accuracy. Joint analysis across two cell types reveals both cell-type-specific and shared regulatory modules.
Conclusions
Our results provide comprehensive cell-type-specific combinatorial binding maps and suggest a modular organization of combinatorial binding.
Keywords
Computational genomics Transcription factor Combinatorial binding Direct and indirect binding Topic model
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1186/s12864-016-3434-3
