Accurate <i>de novo</i> transcription unit annotation from run-on and sequencing data.

ABSTRACT: Functional element annotations are critical tools used to provide insight into the molecular processes governing cell development, differentiation, and disease. Run-on and sequencing assays measure the production of nascent RNAs and can provide an effective data source for discovering functional elements. However, the accurate inference of functional elements from run-on sequencing data remains an open problem because the signal is noisy and challenging to model. Here we investigated computational approaches that convert run-on and sequencing data into annotations representing transcription units, including genes and non-coding RNAs. We developed a convolutional neural network, called convolutional discovery of gene anatomy using PRO-seq (CGAP), trained to identify different anatomical fea

SUBMITTER: Munn PR 

PROVIDER: S-EPMC11870431 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature