TS-m6A-DL: Tissue-specific identification of N6-methyladenosine sites using a universal deep learning model.

ABSTRACT: The most communal post-transcriptional modification, N6-methyladenosine (m6A), is associated with a number of crucial biological processes. The precise detection of m6A sites around the genome is critical for revealing its regulatory function and providing new insights into drug design. Although both experimental and computational models for detecting m6A sites have been introduced, but these conventional methods are laborious and expensive. Furthermore, only a handful of these models are capable of detecting m6A sites in various tissues. Therefore, a more generic and optimized computational method for detecting m6A sites in different tissues is required. In this paper, we proposed a universal model using a deep neural network (DNN) and named it TS-m6A-DL, which can classify m6A sites in s

SUBMITTER: Abbas Z 

PROVIDER: S-EPMC8383060 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature