ArticleNature communications2019
Ranking of non-coding pathogenic variants and putative essential regions of the human genome.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.
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
53 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
- Comprehensive multi-omics integration identifies differentially active enhancers during human brain development with clinical relevance.Genome medicine · 2021Pooled it
- ELE: Estimating tissue-specific long noncoding RNA gene essentiality using graph neural networks.Quantitative biology (Beijing, China) · 2026Article
- Graph-based deep learning approach for high-throughput protein-DNA interaction scoring.Acta pharmacologica Sinica · 2026Article
- Haplotype editing with CRISPR-Cas9 as a therapeutic approach for dominant-negative missense mutations in NEFL.Molecular therapy : the journal of the American Society of Gene Therapy · 2026Article
- Accelerating rare disease diagnostics by linking DNA and RNA through an explainable and interactive RNA-guided workflow.NAR genomics and bioinformatics · 2026Article
- A multidisciplinary RNA-guided approach to complement genomic analysis of unsolved patients with an inborn error of immunity.Frontiers in immunology · 2026Article
- Stratifying variant deleteriousness and trait-modulating effect under human recent adaptation using the FIND model.Genome biology · 2025Article
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Genetic Variants Associated with Suspected Neonatal Hypoxic Ischaemic Encephalopathy: A Study in a South African Context.International journal of molecular sciences · 2025Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 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
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Hidden secrets of the cancer genome: unlocking the impact of non-coding mutations in gene regulatory elements.Cellular and molecular life sciences : CMLS · 2024Review
- 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
- Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases.Genome medicine · 2023Article
- Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data.iScience · 2023Article
- Structural underpinnings of mutation rate variations in the human genome.Nucleic acids research · 2023Article
- Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants.Genomics, proteomics & bioinformatics · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. Here we train a machine learning model using functional, mutational and structural features, including new genome essentiality metrics, 3D genome organization and enhancer reporter data to identify deleterious variants in non-coding regions. We assess the model for functional correlates by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements in over 3 Mb of genome sequence. Finally, we explore two user cases that involve indels and the disruption of enhancers associated with a developmental disease. We rank variants in the non-coding genome according to their predicted deleteriousness. The model prioritizes non-coding regions associated with regulation of important genes and with cell viability, an in vitro surrogate of essentiality.
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Registered trials
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.