Genome wide prediction of HNF4alpha functional binding sites by the use of local and global sequence context.
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)4alpha binding sites and significantly improved an overall recognition of faithful HNF4alpha targets. When applied to published findings, an unprecedented high number of false positives were identified. The technique can be applied to any transcription factor.
Kel
Alexander E
AE
BIOBASE GmbH, Halchtersche Str, 38304 Wolfenbüttel, Germany. ake@biobase.de
Niehof
Monika
M
Matys
Volker
V
Zemlin
Rüdiger
R
Borlak
Jürgen
J
D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation; D024202:Electrophoretic Mobility Shift Assay; D015894:Genome, Human; D051557:Hepatocyte Nuclear Factor 4 / Q000378:metabolism*; D006801:Humans; D011401:Promoter Regions, Genetic; D012091:Repetitive Sequences, Nucleic Acid; D017422:Sequence Analysis, DNA / Q000379:methods*
D051557:Hepatocyte Nuclear Factor 4