Abstract
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.
Highlights
Studies of gene regulation often utilize genome-wide predictions of transcription factor (TF) binding sites
The difference in areas under the ROC curve (AUC) is highly significant: p = 4.88 · 10−4 using the best combination of position weight matrices (PWMs) and cutoff for CENTIPEDE. These results indicate that our PIPES model, which relies on k-mer based representation and avoids strict cutoffs, can improve in vivo predictions of TFBS
Combining protein binding microarrays (PBMs) and DNase data, we presented the first major effort to provide a systematic map of computationally predicted tissue-specific targets for hundreds of TFs across a large number of tissues in mouse
Summary
Studies of gene regulation often utilize genome-wide predictions of transcription factor (TF) binding sites. Chromatin immunoprecipitation(ChIP) followed by microarray (ChIP-chip) [3] or sequencing (ChIP-seq) [4] has been extensively used to study the in vivo binding locations of individual transcription factors and cofactors in a wide range of species and tissues [1,2,5,6,7,8,9]. Despite their popularity, such methods can only study a single TF in a single cell type, under a specific condition, in each experiment. Using computational methods to integrate other genomic resources in order to predict tissue-specific transcription factor binding is an important research challenge
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