ArticleNucleic acids research2019
regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
- GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.Nature communications · 2022Pooled it
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Clinical Variant Databases and Machine Learning Prediction Supporting Genomic Medicine.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Endogenous fine-mapping and prioritization of functional regulatory elements in complex genetic loci.Cell genomics · 2025Article
- Stratifying variant deleteriousness and trait-modulating effect under human recent adaptation using the FIND model.Genome biology · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility.HGG advances · 2024Article
- In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.bioRxiv : the preprint server for biology · 2024Article
- Association of NID2 SNPs with Glioma Risk and Prognosis in the Chinese Population.Neuromolecular medicine · 2024Article
- CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data.Briefings in bioinformatics · 2024Article
- CWAS-Plus: Estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data.medRxiv : the preprint server for health sciences · 2024Article
- Obesity-Dependent Association of the rs10454142Biomedicines · 2024Article
- Topologically associating domains define the impact of de novo promoter variants on autism spectrum disorder risk.Cell genomics · 2024Article
- Sex-Hormone-Binding Globulin Gene Polymorphisms and Breast Cancer Risk in Caucasian Women of Russia.International journal of molecular sciences · 2024Article
- VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome.Nucleic acids research · 2024Article
- Deep Learning in Hematology: From Molecules to Patients.Clinical hematology international · 2024Review
- Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants.Genomics, proteomics & bioinformatics · 2023Article
- RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants.Genomics, proteomics & bioinformatics · 2023Article
- Discovery of novel predisposing coding and noncoding variants in familial Hodgkin lymphoma.Blood · 2023Article
- An autoimmune pleiotropic SNP modulates IRF5 alternative promoter usage through ZBTB3-mediated chromatin looping.Nature communications · 2023Article
Corrections and comments
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Authors and funding
17 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants on specific locus or at base-wise resolution. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.
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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.