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dc.contributor.authorWenger, A. M.
dc.contributor.authorClarke, S. L.
dc.contributor.authorGuturu, H.
dc.contributor.authorChen, J.
dc.contributor.authorSchaar, B. T.
dc.contributor.authorMcLean, C. Y.
dc.contributor.authorBejerano, G.
dc.date.accessioned2016-02-28T05:50:28Z
dc.date.available2016-02-28T05:50:28Z
dc.date.issued2013-02-04
dc.identifier.citationWenger AM, Clarke SL, Guturu H, Chen J, Schaar BT, et al. (2013) PRISM offers a comprehensive genomic approach to transcription factor function prediction. Genome Research 23: 889–904. Available: http://dx.doi.org/10.1101/gr.139071.112.
dc.identifier.issn1088-9051
dc.identifier.pmid23382538
dc.identifier.doi10.1101/gr.139071.112
dc.identifier.urihttp://hdl.handle.net/10754/599403
dc.description.abstractThe 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.
dc.description.sponsorshipWe 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.
dc.publisherCold Spring Harbor Laboratory
dc.subject.meshComputational Biology
dc.subject.meshSoftware
dc.titlePRISM offers a comprehensive genomic approach to transcription factor function prediction
dc.typeArticle
dc.identifier.journalGenome Research
dc.identifier.pmcidPMC3638144
dc.contributor.institutionDepartment of Computer Science, Stanford University, Stanford, California 94305, USA.
dc.date.published-online2013-02-04
dc.date.published-print2013-05-01


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