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Genome wide prediction of HNF4 alpha functional binding sites by the use of local and global sequence context

: Kel, A.E.; Niehof, M.; Matys, V.; Zemlin, R.; Borlak, J.

Postprint urn:nbn:de:0011-n-722103 (1.6 MByte PDF)
MD5 Fingerprint: 2c1b12165d633a16716de2e3b8cfa9cb
Created on: 20.5.2009

Genome biology 9 (2008), No.2, pp.R36
ISSN: 1465-6906
ISSN: 1465-6914
Journal Article, Electronic Publication
Fraunhofer ITEM ()
receptor response elements; composite module analyst; CIS-regulatory modules; chromatin immunoprecipitation; nuclear receptor; promoter module; transcription factor; gene expression; estrogen receptor

We report an application of machine learning algorithms that enables prediction of the functional context of transcription factor binding sites in the human genome. We demonstrate that our method allowed de novo identification of hepatic nuclear factor (HNF)4 alpha binding sites and significantly improved an overall recognition of faithful HNF4 alpha targets. When applied to published findings, an unprecedented high number of false positives were identified. The technique can be applied to any transcription factor.