Dissection of core promoter syntax through single nucleotide resolution modeling of transcription initiation.

ABSTRACT: How the DNA sequence of cis-regulatory elements encode transcription initiation patterns remains poorly understood. Here we introduce CLIPNET, a deep learning model trained on population-scale PRO-cap data that predicts the position and quantity of transcription initiation with single nucleotide resolution from DNA sequence more accurately than existing approaches. Interpretation of CLIPNET revealed a complex regulatory syntax consisting of DNA-protein interactions in five major positions between -200 and +50 bp relative to the transcription start site, as well as more subtle positional preferences among transcriptional activators. Transcriptional activator and core promoter motifs work non-additively to encode distinct aspects of initiation, with the former driving initiation quant

SUBMITTER: He AY 

PROVIDER: S-EPMC10979970 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature