ArticleBioinformatics (Oxford, England)2024
MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow.Genome research · 2026Article
- AWTI-Net Enables Accurate and Interpretable Functional Assessment of Disease-Associated LncRNA Mutations.Interdisciplinary sciences, computational life sciences · 2026Article
- scVMAP: a comprehensive platform for integrating single-cell chromatin accessibility regions with causal variants.Nucleic acids research · 2026Article
- Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow.bioRxiv : the preprint server for biology · 2025Article
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Abstract
summaryMassively parallel reporter assay (MPRA) is an important technology for evaluating 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 genomic features, totaling 242 818 variants tested more than 30 cell lines and 30 human diseases or traits. MPRAVarDB enables users to query MPRA variants by genomic region, disease and cell line, or any combination of these parameters. Notably, MPRAVarDB offers a suite of pretrained machine-learning models tailored to the specific disease and cell line, facilitating the prediction of regulatory variants. The user-friendly interface allows users to receive query and prediction results with just a few clicks. AVAILABILITY AND IMPLEMENTATION: https://mpravardb.rc.ufl.edu.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.