Mapping transcription factor binding sites by learning UV damage fingerprints.

ABSTRACT: Deciphering transcriptional networks requires methods to accurately map binding sites of sequence-specific transcription factors (ssTFs) across the genome. Here, we show that ssTF binding induces distinct patterns of UV-induced cyclobutane pyrimidine dimers (CPDs), and that these CPD 'fingerprints' can be exploited by machine learning methods to identify ssTF binding sites (TFBS). As a proof of principle, we analyzed CPD-seq data from yeast cells using the Random Forest algorithm to identify 75 TFBS bound by the Hap2/Hap3/Hap5 ssTF complex, including ∼25 new sites missed by previous chromatin immunoprecipitation (ChIP)-based experiments. Parallel analysis of the Gcr1 ssTF using a neural network trained on CPD-seq data including only 6 known binding sites identified 63 Gcr1 TFBS across the

SUBMITTER: Wilson HE 

PROVIDER: S-EPMC12526043 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature