ArticleBMC genomics2014
HGCS: an online tool for prioritizing disease-causing gene variants by biological distance.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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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
27 citing papers in PubMed.
- IFIH1 loss-of-function variants contribute to very early-onset inflammatory bowel disease.Human genetics · 2021Trial
- MDVarP: modifier ~ disease-causing variant pairs predictor.BioData mining · 2024Article
- Faster and more accurate pathogenic combination predictions with VarCoPP2.0.BMC bioinformatics · 2023Article
- Novel brown adipose tissue candidate genes predicted by the human gene connectome.Scientific reports · 2022Article
- Large-scale discovery of novel neurodevelopmental disorder-related genes through a unified analysis of single-nucleotide and copy number variants.Genome medicine · 2022Article
- Candidate genes and sequence variants for susceptibility to mycobacterial infection identified by whole-exome sequencing.Frontiers in genetics · 2022Article
- An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases.Computational and structural biotechnology journal · 2022Article
- Network analysis reveals rare disease signatures across multiple levels of biological organization.Nature communications · 2021Article
- The VRNetzer platform enables interactive network analysis in Virtual Reality.Nature communications · 2021Article
- Article
- Identifying disease-causing mutations in genomes of single patients by computational approaches.Human genetics · 2020Review
- De novo variants in exomes of congenital heart disease patients identify risk genes and pathways.Genome medicine · 2020Article
- Chronic mucocutaneous candidiasis and connective tissue disorder in humans with impaired JNK1-dependent responses to IL-17A/F and TGF-β.Science immunology · 2019Article
- ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations.Nucleic acids research · 2019Article
- Predicting disease-causing variant combinations.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- CDG: An Online Server for Detecting Biologically Closest Disease-Causing Genes and its Application to Primary Immunodeficiency.Frontiers in immunology · 2018Article
- Evaluating the Genetics of Common Variable Immunodeficiency: Monogenetic Model and Beyond.Frontiers in immunology · 2018Article
- Discovery of Variants Underlying Host Susceptibility to Virus Infection Using Whole-Exome Sequencing.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Capturing the biology of disease severity in a PSC-based model of familial dysautonomia.Nature medicine · 2016Article
- Exome and genome sequencing for inborn errors of immunity.The Journal of allergy and clinical immunology · 2016Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundIdentifying the genotypes underlying human disease phenotypes is a fundamental step in human genetics and medicine. High-throughput genomic technologies provide thousands of genetic variants per individual. The causal genes of a specific phenotype are usually expected to be functionally close to each other. According to this hypothesis, candidate genes are picked from high-throughput data on the basis of their biological proximity to core genes - genes already known to be responsible for the phenotype. There is currently no effective gene-centric online interface for this purpose.
resultsWe describe here the human gene connectome server (HGCS), a powerful, easy-to-use interactive online tool enabling researchers to prioritize any list of genes according to their biological proximity to core genes associated with the phenotype of interest. We also make available an updated and extended version for all human gene-specific connectomes. The HGCS is freely available to noncommercial users from: http://hgc.rockefeller.edu.
conclusionsThe HGCS should help investigators from diverse fields to identify new disease-causing candidate genes more effectively, via a user-friendly online interface.
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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.