ArticleBioinformatics (Oxford, England)2019
De novo pattern discovery enables robust assessment of functional consequences of non-coding variants.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.Nature communications · 2022Pooled it
- Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.Current protocols in human genetics · 2019Pooled it
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- MIRACN: a residual convolutional neural network for predicting cell line specific functional regulatory variants.Briefings in bioinformatics · 2025Article
- In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.bioRxiv : the preprint server for biology · 2024Article
- Topologically associating domains define the impact of de novo promoter variants on autism spectrum disorder risk.Cell genomics · 2024Article
- VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome.Nucleic acids research · 2024Article
- Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants.Genomics, proteomics & bioinformatics · 2023Article
- Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.Nucleic acids research · 2023Article
- Multi-omics approach dissects cis-regulatory mechanisms underlying North Carolina macular dystrophy, a retinal enhanceropathy.American journal of human genetics · 2022Article
- Exploration of Tools for the Interpretation of Human Non-Coding Variants.International journal of molecular sciences · 2022Review
- TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.Bioinformatics (Oxford, England) · 2022Article
- Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.Bioinformatics (Oxford, England) · 2022Article
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Authors and funding
9 authors.
Funding
Abstract
motivationGiven the complexity of genome regions, prioritize the functional effects of non-coding variants remains a challenge. Although several frameworks have been proposed for the evaluation of the functionality of non-coding variants, most of them used 'black boxes' methods that simplify the task as the pathogenicity/benign classification problem, which ignores the distinct regulatory mechanisms of variants and leads to less desirable performance. In this study, we developed DVAR, an unsupervised framework that leverage various biochemical and evolutionary evidence to distinguish the gene regulatory categories of variants and assess their comprehensive functional impact simultaneously.
resultsDVAR performed de novo pattern discovery in high-dimensional data and identified five regulatory clusters of non-coding variants. Leveraging the new insights into the multiple functional patterns, it measures both the between-class and the within-class functional implication of the variants to achieve accurate prioritization. Compared to other two-class learning methods, it showed improved performance in identification of clinically significant variants, fine-mapped GWAS variants, eQTLs and expression-modulating variants. Moreover, it has superior performance on disease causal variants verified by genome-editing (like CRISPR-Cas9), which could provide a pre-selection strategy for genome-editing technologies across the whole genome. Finally, evaluated in BioVU and UK Biobank, two large-scale DNA biobanks linked to complete electronic health records, DVAR demonstrated its effectiveness in prioritizing non-coding variants associated with medical phenotypes. AVAILABILITY AND IMPLEMENTATION: The C++ and Python source codes, the pre-computed DVAR-cluster labels and DVAR-scores across the whole genome are available at https://www.vumc.org/cgg/dvar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.