Apr 1, 2017

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

BMC Systems Biology
Qiao LiuRui Jiang

Abstract

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. 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 sequences. We demonstrated that our method outperforms existing methods in distinguishing known accessible chromatin regions from random genomic sequences. Furthermore, the performance of our method can further be improved with the incorporation of sequence conservation features. Based on this model, we assessed importance of the k-mer features by a series of permutation experiments, and we characterized the risk of a single nucleotide polymorphism (SNP) on the function of the ...Continue Reading

Mentioned in this Paper

Genome-Wide Association Study
Study
Genome
Pathogenic Organism
Genome Assembly Sequence
Genomics
Protein Domain
Chromatin Location
Mammary Neoplasms, Human
DNA Helix Destabilizing Proteins

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