Improved models for transcription factor binding site identification using nonindependent interactions

Yue Zhao, Shuxiang Ruan, Manishi Pandey, Gary D. Stormo

Research output: Contribution to journalArticle

80 Scopus citations

Abstract

Identifying transcription factor (TF) binding sites is essential for understanding regulatory networks. The specificity of most TFs is currently modeled using position weight matrices (PWMs) that assume the positions within a binding site contribute independently to binding affinity for any site. Extensive, high-throughput quantitative binding assays let us examine, for the first time, the independence assumption for many TFs. We find that the specificity of most TFs is well fit with the simple PWM model, but in some cases more complex models are required. We introduce a binding energy model (BEM) that can include energy parameters for nonindependent contributions to binding affinity. We show that in most cases where a PWM is not sufficient, a BEM that includes energy parameters for adjacent dinucleotide contributions models the specificity very well. Having more accurate models of specificity greatly improves the interpretation of in vivo TF localization data, such as from chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments.

Original languageEnglish
Pages (from-to)781-790
Number of pages10
JournalGenetics
Volume191
Issue number3
DOIs
StatePublished - Jul 1 2012

Fingerprint Dive into the research topics of 'Improved models for transcription factor binding site identification using nonindependent interactions'. Together they form a unique fingerprint.

  • Cite this