DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data.

ABSTRACT:

Background

The widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE suffers from transcriptional and technical noise. Regardless of the sample quality, there is a significant number of CAGE peaks that are not associated with transcription initiation events. This type of signal is typically attributed to technical noise and more frequently to random five-prime capping or transcription bioproducts. Thus, the need for computational methods emerges, that can accurately increase the signal-to-noise ratio in CAGE data, resulting in error-free transcription start site (TSS) annotation and quantification of regulatory region usage. In this st

SUBMITTER: Grigoriadis D 

PROVIDER: S-EPMC9743497 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature