Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments
Name
Kheradpour-2013-Systematic discovery.pdf
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1.34 MB
Format
Adobe PDF
Checksum (MD5)
18ad3800c0dffe96590f19cfe5d0d984
Author(s)
Kheradpour, Pouya
Kellis, Manolis
Date Issued
December 2013
Journal
Nucleic Acids Research
Publisher
Oxford University Press
Citation
Kheradpour, P., and M. Kellis. “Systematic Discovery and Characterization of Regulatory Motifs in ENCODE TF Binding Experiments.” Nucleic Acids Research 42, no. 5 (March 1, 2014): 2976–2987.
Version
Final published version
Abstract
Recent advances in technology have led to a dramatic increase in the number of available transcription factor ChIP-seq and ChIP-chip data sets. Understanding the motif content of these data sets is an important step in understanding the underlying mechanisms of regulation. Here we provide a systematic motif analysis for 427 human ChIP-seq data sets using motifs curated from the literature and also discovered de novo using five established motif discovery tools. We use a systematic pipeline for calculating motif enrichment in each data set, providing a principled way for choosing between motif variants found in the literature and for flagging potentially problematic data sets. Our analysis confirms the known specificity of 41 of the 56 analyzed factor groups and reveals motifs of potential cofactors. We also use cell type-specific binding to find factors active in specific conditions. The resource we provide is accessible both for browsing a small number of factors and for performing large-scale systematic analyses. We provide motif matrices, instances and enrichments in each of the ENCODE data sets. The motifs discovered here have been used in parallel studies to validate the specificity of antibodies, understand cooperativity between data sets and measure the variation of motif binding across individuals and species.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Publisher with Creative Commons License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/nar/gkt1249
