PRISM offers a comprehensive genomic approach to transcription factor function prediction
- Aaron M Wenger,
- Shoa L Clarke,
- Harendra Guturu,
- Jenny Chen,
- Bruce T Schaar,
- Cory Y McLean and
- Gill Bejerano1
- ↵* Corresponding author; email: bejerano{at}stanford.edu
Abstract
The human genome encodes 1,500-2,000 different transcription factors (TFs). ChIP-seq is revealing the global binding profiles of a fraction of TFs in a fraction of their biological contexts. These data show that the majority of TFs bind directly next to a large number of context relevant target genes, that most binding is distal, and that binding is context specific. Because of the effort and cost involved, ChIP-seq is seldom used in search of novel TF function. Such exploration is instead done using expression perturbation and genetic screens. Here we propose a comprehensive computational framework for transcription factor function prediction. We curate 332 high quality non-redundant TF binding motifs that represent all major DNA binding domains, and improve cross-species conserved binding site prediction to obtain 3.3 million conserved, mostly distal, binding site predictions. We combine these with 2.4 million facts about all human and mouse gene functions, in a novel statistical framework, in search of enrichments of particular motifs next to groups of target genes of particular functions. Rigorous parameter tuning and a harsh null are used to minimize false positives. Our novel PRISM (Predicting Regulatory Information from Single Motifs) approach obtains 2,543 TF function predictions in a large variety of contexts, at a false discovery rate of 16%. The predictions are highly enriched for validated TF roles, and 45 of 67 (67%) tested binding site regions in five different contexts act as enhancers in functionally matched cells.
- Received February 12, 2012.
- Accepted January 25, 2013.
- © 2013, Published by Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
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