A sequence-based method to predict the impact of regulatory variants using random forest.

ABSTRACT:

Background

Most disease-associated variants identified by genome-wide association studies (GWAS) exist in noncoding regions. In spite of the common agreement that such variants may disrupt biological functions of their hosting regulatory elements, it remains a great challenge to characterize the risk of a genetic variant within the implicated genome sequence. Therefore, it is essential to develop an effective computational model that is not only capable of predicting the potential risk of a genetic variant but also valid in interpreting how the function of the genome is affected with the occurrence of the variant.

Results

We developed a method named kmerForest that used a random forest classifier with k-mer counts to predict accessible chromatin regions purely based on DNA s

SUBMITTER: Liu Q 

PROVIDER: S-EPMC5374684 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature