In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.

ABSTRACT: Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. Massively parallel reporter assays (MPRAs), which are an in vitro high-throughput method, can simultaneously test thousands of variants by evaluating the existence of allele specific regulatory activity. Nevertheless, the identified labelled variants by MPRAs, which shows differential allelic regulatory effects on the gene expression are usually limited to the scale of hundreds, limiting their potential to be used as the training set for achieving a robust genome-wide prediction. To address the limitation, we propose a deep generative model, MpraVAE, to in silico generate and augment the training sample size of labelled variants. By benchmarking on several MPRA datasets,

SUBMITTER: Jin W 

PROVIDER: S-EPMC11230389 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature