Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants.

ABSTRACT:

Motivation

Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mechanisms underlying the significant loci are largely unknown. In this study, we aim to predict non-coding variants that may functionally affect translation initiation through long-range chromatin interaction.

Results

By incorporating the Hi-C data, we propose a novel and powerful deep learning model of artificial intelligence to classify interacting and non-interacting fragment pairs and predict the functional effects of sequence alteration of single nucleotide on chromatin interaction and thus on gene expression. The changes in chromatin interaction probability between the reference sequence and the altered sequence reflect the

SUBMITTER: Meng XH 

PROVIDER: S-EPMC8208740 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature