ArticleNature methods2013
eXtasy: variant prioritization by genomic data fusion.
Article in Nature methods, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 1 of them a synthesis that pooled it.
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Who cites it
101 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational resources associating diseases with genotypes, phenotypes and exposures.Briefings in bioinformatics · 2019Pooled it
- Single-cell data combined with phenotypes improves variant interpretation.BMC genomics · 2025Article
- Yves Moreau has received the 2023 Einstein Foundation Individual Award for Promoting Quality in Research.Bioinformatics advances · 2024Article
- diseaseGPS: auxiliary diagnostic system for genetic disorders based on genotype and phenotype.Bioinformatics (Oxford, England) · 2023Article
- Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.PLoS computational biology · 2023Article
- Nonlinear data fusion over Entity-Relation graphs for Drug-Target Interaction prediction.Bioinformatics (Oxford, England) · 2023Article
- Phenotype-aware prioritisation of rare Mendelian disease variants.Trends in genetics : TIG · 2022Review
- Predicting genes from phenotypes using human phenotype ontology (HPO) terms.Human genetics · 2022Article
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease.Human mutation · 2022Review
- PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care.BMC medical informatics and decision making · 2022Article
- Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.Briefings in bioinformatics · 2022Article
- From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data.Nucleic acids research · 2022Article
- GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation.BMC medical genomics · 2022Article
- A whole genome sequencing approach to anterior cruciate ligament rupture-a twin study in two unrelated families.PloS one · 2022Article
- Editorial: Towards genome interpretation: Computational methods to model the genotype-phenotype relationship.Frontiers in bioinformatics · 2022Article
- Computational approaches for predicting variant impact: An overview from resources, principles to applications.Frontiers in genetics · 2022Review
- Angiogenesis goes computational - The future way forward to discover new angiogenic targets?Computational and structural biotechnology journal · 2022Review
- MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity ofJACC. Basic to translational science · 2021Article
- MutationTaster2021.Nucleic acids research · 2021Article
41 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Massively parallel sequencing greatly facilitates the discovery of novel disease genes causing Mendelian and oligogenic disorders. However, many mutations are present in any individual genome, and identifying which ones are disease causing remains a largely open problem. We introduce eXtasy, an approach to prioritize nonsynonymous single-nucleotide variants (nSNVs) that substantially improves prediction of disease-causing variants in exome sequencing data by integrating variant impact prediction, haploinsufficiency prediction and phenotype-specific gene prioritization.
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