Probabilistic inference of transcription factor binding from multiple data sources.

ABSTRACT: An important problem in molecular biology is to build a complete understanding of transcriptional regulatory processes in the cell. We have developed a flexible, probabilistic framework to predict TF binding from multiple data sources that differs from the standard hypothesis testing (scanning) methods in several ways. Our probabilistic modeling framework estimates the probability of binding and, thus, naturally reflects our degree of belief in binding. Probabilistic modeling also allows for easy and systematic integration of our binding predictions into other probabilistic modeling methods, such as expression-based gene network inference. The method answers the question of whether the whole analyzed promoter has a binding site, but can also be extended to estimate the binding probability

SUBMITTER: Lahdesmaki H 

PROVIDER: S-EPMC2268002 | biostudies-literature | 2008 Mar

REPOSITORIES: biostudies-literature