PRISM offers a comprehensive genomic approach to transcription factor function prediction

A. M. Wenger, S. L. Clarke, H. Guturu, J. Chen, B. T. Schaar, C. Y. McLean, G. Bejerano

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


The human genome encodes 1500-2000 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 nonredundant 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 2543 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.
Original languageEnglish (US)
Pages (from-to)889-904
Number of pages16
JournalGenome Research
Issue number5
StatePublished - Feb 4 2013
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank Seung Kim for providing us mPAC cells; Tom Cramer for freeing the PRISM Stanford domain name; Ravi Parikh for improving the user interface of the PRISM resource; Michael Hiller for the mouse 44-way alignment; and Will Talbot, Nadav Ahituv, Betty Booker, and the Bejerano laboratory for helpful comments. This work was supported by a Stanford Graduate Fellowship (A.M.W.), a Bio-X Stanford Interdisciplinary Graduate Fellowship (A.M.W.), an HHMI Gilliam Fellowship (S.L.C.), a National Science Foundation Fellowship DGE-1147470 (H.G.), a Bio-X Graduate Fellowship (C.Y.M.), NIH grants R01HG005058 and R01HD059862, NSF Center for Science of Information (CSoI) grant CCF-0939370, and KAUST (all to G.B.). G.B. is a Packard Fellow and Microsoft Research Fellow.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


Dive into the research topics of 'PRISM offers a comprehensive genomic approach to transcription factor function prediction'. Together they form a unique fingerprint.

Cite this