DeeReCT-PolyA: a robust and generic deep learning method for PAS identification.

ABSTRACT:

Motivation

Polyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PASs) identification is not only desired for the purpose of better transcripts' end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif- and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals.

Results

In this work, we propose a robust, PAS motif agnostic, and highly interpretable and transferrable deep learning model for accurate PAS recognition, which requires no

SUBMITTER: Xia Z 

PROVIDER: S-EPMC6612895 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature