A semi-supervised model to predict regulatory effects of genetic variants at single nucleotide resolution using massively parallel reporter assays.

ABSTRACT:

Motivation

Predicting regulatory effects of genetic variants is a challenging but important problem in functional genomics. Given the relatively low sensitivity of functional assays, and the pervasiveness of class imbalance in functional genomic data, popular statistical prediction models can sharply underestimate the probability of a regulatory effect. We describe here the presence-only model (PO-EN), a type of semi-supervised model, to predict regulatory effects of genetic variants at sequence-level resolution in a context of interest by integrating a large number of epigenetic features and massively parallel reporter assays (MPRAs).

Results

Using experimental data from a variety of MPRAs we show that the presence-only model produces better calibrated predicted probabiliti

SUBMITTER: Yang Z 

PROVIDER: S-EPMC8337004 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature