ArticleNature communications2021
Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed, 89 citations in OpenAlex.
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- From conservation scores to language models: a feature engineering perspective on machine learning for genetic variant interpretation.Briefings in bioinformatics · 2026Review
- Characterizing highly conserved fragments in 3'UTRs via statistical and transfer learning approaches.NAR genomics and bioinformatics · 2026Article
- Base-pair resolution conservation data improves cell type specific sequence-to-expression prediction.Bioinformatics (Oxford, England) · 2026Article
- Towards efficient perturbation for the noncoding genome.Nature communications · 2026Article
- SNPnexus: an enhanced web platform for large-scale and multi-sample variant analysis (2025 update).Nucleic acids research · 2026Article
- Analysis of whole genome sequencing and plasma metabolomics unveil genetic determinants and clinical implications for human health.Nature communications · 2026Article
- A phenome-wide association study of CNVs genotyped from genome sequencing read depth in the UK Biobank.American journal of human genetics · 2026Article
- Deep-Neural-Network-Aided Genetic Association Testing in Samples with Related Individuals.Current issues in molecular biology · 2026Article
- Whole-genome sequencing analysis of anthropometric traits in 672,976 individuals reveals convergence between rare and common genetic associations.Nature communications · 2026Article
- Decoding thebioRxiv : the preprint server for biology · 2026Article
- FAVOR 2.0: A reengineered functional annotation of variants online resource for interpreting genomic variation.Nucleic acids research · 2026Article
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- GREGoR: accelerating genomics for rare diseases.Nature · 2025Article
- BTS: a scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across >1000s of omics datasets.Bioinformatics (Oxford, England) · 2025Article
- Article
- Nephrogenomics, precision medicine and the role of genetic testing in adult kidney disease management.Nature reviews. Nephrology · 2025Review
- Clinical and data-driven optimization of Genomiser for rare disease patients: experience from the Hong Kong Genome Project.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate intolerance to variation, functional genomic annotations and primary genomic sequence to build JARVIS: a comprehensive deep learning model to prioritize non-coding regions, outperforming other human lineage-specific scores. Despite being agnostic to evolutionary conservation, JARVIS performs comparably or outperforms conservation-based scores in classifying pathogenic single-nucleotide and structural variants. In constructing JARVIS, we introduce the genome-wide residual variation intolerance score (gwRVIS), applying a sliding-window approach to whole genome sequencing data from 62,784 individuals. gwRVIS distinguishes Mendelian disease genes from more tolerant CCDS regions and highlights ultra-conserved non-coding elements as the most intolerant regions in the human genome. Both JARVIS and gwRVIS capture previously inaccessible human-lineage constraint information and will enhance our understanding of the non-coding genome.
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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.