DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning.

ABSTRACT:

Background

N6-methyladensine (m6A) is a common and abundant RNA methylation modification found in various species. As a type of post-transcriptional methylation, m6A plays an important role in diverse RNA activities such as alternative splicing, an interplay with microRNAs and translation efficiency. Although existing tools can predict m6A at single-base resolution, it is still challenging to extract the biological information surrounding m6A sites.

Results

We implemented a deep learning framework, named DeepM6ASeq, to predict m6A-containing sequences and characterize surrounding biological features based on miCLIP-Seq data, which detects m6A sites at single-base resolution. DeepM6ASeq showed better performance as compared to other machine learning classifiers. Moreover, an

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC6311933 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature