Predicting tissue specific transcription factor binding sites

BMC Genomics

Methodology Article

Shan Zhong¹  Ziv Bar-Joseph¹  Xin He¹ 

[1] Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, 15213, Pittsburgh, PA, USA;

关键词: Transcription Factor Binding;    Transcription Factor Binding Site;    Lasso;    Binding Probability;    phastCons Score;   

DOI  :  10.1186/1471-2164-14-796

 received in 2013-10-30, accepted in 2013-11-06,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundStudies 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.ResultsWe 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.ConclusionsWe 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.

【 授权许可】

Unknown   
© Zhong et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.