Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations.

ABSTRACT: Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding pro

SUBMITTER: Lu R 

PROVIDER: S-EPMC6464064 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature