bioRxivBioinformaticsPreprint
MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants
Javlon Nizomov, Weijia JiniD, Yi Xia, Yunlong LiuiD, Zhigang LiiD, Li CheniD
- Citations
- 0
Abstract
SummaryMassively parallel reporter assay (MPRA) is an important technology to evaluate the impact of genetic variants on gene regulation. Here, we present MPRAVarDB, an online database and web server, for exploring regulatory effects of genetic variants. MPRAVarDB harbors 18 MPRA experiments designed to assess the regulatory effects of genetic variants associated with GWAS loci, eQTLs and various genomic features, resulting in a total of 242,818 variants tested across more than 30 cell lines and 30 human diseases or traits. MPRAVarDB empowers the query of MPRA variants by genomic region, disease and cell line or by any combination of these query terms. Notably, MPRAVarDB offers a suite of pretrained machine learning models tailored to the specific disease and cell line, facilitating the genome-wide prediction of regulatory variants. MPRAVarDB is friendly to use, and users only need a few clicks to receive query and prediction results. Availabilityhttps://mpravardb.rc.ufl.edu Supplementary informationSupplementary data are available at Bioinformatics online.
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: variant effect prediction
- approach: machine learning (classical)
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- MPRAbase: A Massively Parallel Reporter Assay Database
Jingjing Zhao, Fotis A. Baltoumas, … Nadav Ahituv·Genome ResearchPeer-reviewed·Apr 22, 2025
- Genome-wide regulatory model from MPRA data predicts functional regions, eQTLs, and GWAS hits
- In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder
Weijia Jin, Yi Xin Xia, … Li Chen·bioRxiv·Jun 25, 2024
- Meta-analysis of massive parallel reporter assay enables functional regulatory elements prediction
Anat Kreimer, Zhongxia Yan, … Nir Yosef·bioRxiv·Jan 29, 2019
- Massively parallel characterization and predictive modelling of neuronal regulatory variation
Kilian Salomon, Chengyu Deng, … Martin Kircher·bioRxiv·Jul 17, 2026
- Identifying non-coding variant effects at scale via machine learning models of cis-regulatory reporter assays
John C. Butts, Stephen Rong, … Steven K. Reilly·bioRxiv·Apr 16, 2025