Predicting regulatory variants with composite statistic.

ABSTRACT:

Motivation

Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction.

Results

Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a giv

SUBMITTER: Li MJ 

PROVIDER: S-EPMC6280872 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature