Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Heterogeneity of Transcription Factor binding specificity models within and across cell lines

Mahfuza Sharmin, Héctor Corrada Bravo, Sridhar Hannenhalli
doi: https://doi.org/10.1101/028787
Mahfuza Sharmin
1Department of Computer Science
2Center for Bioinformatics and Computational Biology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Héctor Corrada Bravo
1Department of Computer Science
2Center for Bioinformatics and Computational Biology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sridhar Hannenhalli
2Center for Bioinformatics and Computational Biology
3Department of Cell and Molecular Biology, University of Maryland, College park, MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: sridhar@umiacs.umd.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Complex gene expression patterns are mediated by binding of transcription factors (TF) to specific genomic loci. The in vivo occupancy of a TF is, in large part, determined by the TF’s DNA binding interaction partners, motivating genomic context based models of TF occupancy. However, the approaches thus far have assumed a uniform binding model to explain genome wide bound sites for a TF in a cell-type and as such heterogeneity of TF occupancy models, and the extent to which binding rules underlying a TF’s occupancy are shared across cell types, has not been investigated. Here, we develop an ensemble based approach (TRISECT) to identify heterogeneous binding rules of cell-type specific TF occupancy and analyze the inter-cell-type sharing of such rules. Comprehensive analysis of 23 TFs, each with ChIP-Seq data in 4-12 cell-types, shows that by explicitly capturing the heterogeneity of binding rules, TRISECT accurately identifies in vivo TF occupancy (93%) substantially improving upon previous methods. Importantly, many of the binding rules derived from individual cell-types are shared across cell-types and reveal distinct yet functionally coherent putative target genes in different cell-types. Closer inspection of the predicted cell-type-specific interaction partners provides insights into context-specific functional landscape of a TF. Together, our novel ensemble-based approach reveals, for the first time, a widespread heterogeneity of binding rules, comprising interaction partners within a cell-type, many of which nevertheless transcend cell-types. Notably, the putative targets of shared binding rules in different cell-types, while distinct, exhibit significant functional coherence.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted October 09, 2015.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Heterogeneity of Transcription Factor binding specificity models within and across cell lines
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Heterogeneity of Transcription Factor binding specificity models within and across cell lines
Mahfuza Sharmin, Héctor Corrada Bravo, Sridhar Hannenhalli
bioRxiv 028787; doi: https://doi.org/10.1101/028787
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Heterogeneity of Transcription Factor binding specificity models within and across cell lines
Mahfuza Sharmin, Héctor Corrada Bravo, Sridhar Hannenhalli
bioRxiv 028787; doi: https://doi.org/10.1101/028787

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4658)
  • Biochemistry (10313)
  • Bioengineering (7636)
  • Bioinformatics (26241)
  • Biophysics (13481)
  • Cancer Biology (10650)
  • Cell Biology (15361)
  • Clinical Trials (138)
  • Developmental Biology (8464)
  • Ecology (12776)
  • Epidemiology (2067)
  • Evolutionary Biology (16794)
  • Genetics (11373)
  • Genomics (15431)
  • Immunology (10580)
  • Microbiology (25087)
  • Molecular Biology (10172)
  • Neuroscience (54233)
  • Paleontology (398)
  • Pathology (1660)
  • Pharmacology and Toxicology (2884)
  • Physiology (4326)
  • Plant Biology (9213)
  • Scientific Communication and Education (1582)
  • Synthetic Biology (2545)
  • Systems Biology (6761)
  • Zoology (1459)