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BMC Genomics. 2013 Nov 15;14:796. doi: 10.1186/1471-2164-14-796.

Predicting tissue specific transcription factor binding sites.

Author information

1
Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA. zivbj@cs.cmu.edu.

Abstract

BACKGROUND:

Studies of gene regulation often utilize genome-wide predictions of transcription factor (TF) binding sites. Most existing prediction methods are based on sequence information alone, ignoring biological contexts such as developmental stages and tissue types. Experimental methods to study in vivo binding, including ChIP-chip and ChIP-seq, can only study one transcription factor in a single cell type and under a specific condition in each experiment, and therefore cannot scale to determine the full set of regulatory interactions in mammalian transcriptional regulatory networks.

RESULTS:

We developed a new computational approach, PIPES, for predicting tissue-specific TF binding. PIPES integrates in vitro protein binding microarrays (PBMs), sequence conservation and tissue-specific epigenetic (DNase I hypersensitivity) information. We demonstrate that PIPES improves over existing methods on distinguishing between in vivo bound and unbound sequences using ChIP-seq data for 11 mouse TFs. In addition, our predictions are in good agreement with current knowledge of tissue-specific TF regulation.

CONCLUSIONS:

We provide a systematic map of computationally predicted tissue-specific binding targets for 284 mouse TFs across 55 tissue/cell types. Such comprehensive resource is useful for researchers studying gene regulation.

PMID:
24238150
PMCID:
PMC3898213
DOI:
10.1186/1471-2164-14-796
[Indexed for MEDLINE]
Free PMC Article

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