Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

ABSTRACT:

Background

In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide.

Results

Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning appr

SUBMITTER: Li Y 

PROVIDER: S-EPMC5984344 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature