ArticlePLoS genetics2022
Classification of non-coding variants with high pathogenic impact.
Article in PLoS genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Genetic testing for diagnosing neurodevelopmental disorders and epilepsy: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Unveiling ocular developmental disorders through short-read whole-genome sequencing.European journal of human genetics : EJHG · 2026Article
- The oligogenic inheritance test GCOD detects risk genes and their interactions in congenital heart defects.Genome research · 2026Article
- SomaMutDB 2.0: A comprehensive database for exploring somatic mutations and their functional impact in normal human tissues.Nucleic acids research · 2026Article
- Deep DNA and protein level feature integration for robust clinical variant interpretation using probabilistic gradient boosting.Frontiers in digital health · 2026Article
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Identification of a newMolecular genetics and metabolism reports · 2025Article
- Advancements in genetic research by the Hispanic Community Health Study/Study of Latinos: A 10-year retrospective review.HGG advances · 2025Review
- A Rare Noncoding Enhancer Variant inCirculation · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- PSAP-Genomic-Regions: A Method Leveraging Population Data to Prioritize Coding and Non-Coding Variants in Whole Genome Sequencing for Rare Disease Diagnosis.Genetic epidemiology · 2025Article
- Integrative identification of non-coding regulatory regions driving metastatic prostate cancer.Cell reports · 2024Article
- TAD boundary deletion causes PITX2-related cardiac electrical and structural defects.Nature communications · 2024Article
- Review
- 3-hour genome sequencing and targeted analysis to rapidly assess genetic risk.Genetics in medicine open · 2024Article
- Identification of ancestral gnathostome Gli3 enhancers with activity in mammals.Development, growth & differentiation · 2024Article
- Integrative computational analyses implicate regulatory genomic elements contributing to spina bifida.Genetics in medicine open · 2024Article
- Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies.Current atherosclerosis reports · 2023Review
- A curated census of pathogenic and likely pathogenic UTR variants and evaluation of deep learning models for variant effect prediction.Frontiers in molecular biosciences · 2023Article
- From bugs to bedside: functional annotation of human genetic variation for neurological disorders using invertebrate models.Human molecular genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole genome sequencing is increasingly used to diagnose medical conditions of genetic origin. While both coding and non-coding DNA variants contribute to a wide range of diseases, most patients who receive a WGS-based diagnosis today harbour a protein-coding mutation. Functional interpretation and prioritization of non-coding variants represents a persistent challenge, and disease-causing non-coding variants remain largely unidentified. Depending on the disease, WGS fails to identify a candidate variant in 20-80% of patients, severely limiting the usefulness of sequencing for personalised medicine. Here we present FINSURF, a machine-learning approach to predict the functional impact of non-coding variants in regulatory regions. FINSURF outperforms state-of-the-art methods, owing in particular to optimized control variants selection during training. In addition to ranking candidate variants, FINSURF breaks down the score for each variant into contributions from individual annotations, facilitating the evaluation of their functional relevance. We applied FINSURF to a diverse set of 30 diseases with described causative non-coding mutations, and correctly identified the disease-causative non-coding variant within the ten top hits in 22 cases. FINSURF is implemented as an online server to as well as custom browser tracks, and provides a quick and efficient solution to prioritize candidate non-coding variants in realistic clinical settings.
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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.