A graphical model approach visualizes regulatory relationships between genome-wide transcription factor binding profiles.

ABSTRACT: Integrated analysis of multiple genome-wide transcription factor (TF)-binding profiles will be vital to advance our understanding of the global impact of TF binding. However, existing methods for measuring similarity in large numbers of chromatin immunoprecipitation assays with sequencing (ChIP-seq), such as correlation, mutual information or enrichment analysis, are limited in their ability to display functionally relevant TF relationships. In this study, we propose the use of graphical models to determine conditional independence between TFs and showed that network visualization provides a promising alternative to distinguish 'direct' versus 'indirect' TF interactions. We applied four algorithms to measure 'direct' dependence to a compendium of 367 mouse haematopoietic TF ChIP-seq sample

SUBMITTER: Ng FSL 

PROVIDER: S-EPMC5496675 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature