ArticleBMC bioinformatics2018
GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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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
9 citing papers in PubMed, 29 citations in OpenAlex.
- Decoding complex inherited phenotypes in rare disorders: the DECIPHERD initiative for rare undiagnosed diseases in Chile.European journal of human genetics : EJHG · 2024Article
- EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation.BMC bioinformatics · 2022Article
- Phenotype-aware prioritisation of rare Mendelian disease variants.Trends in genetics : TIG · 2022Review
- Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease.Human mutation · 2022Review
- Whole-exome sequencing reveals a novel homozygous mutation in the COQ8B gene associated with nephrotic syndrome.Scientific reports · 2021Article
- Whole-Exome Sequencing Reveals a Rare Variant ofFrontiers in genetics · 2021Article
- Diagnosis of Rare Diseases: a scoping review of clinical decision support systems.Orphanet journal of rare diseases · 2020Article
- Article
- The utility of phenomics in diagnosis of inherited metabolic disorders.Clinical medicine (London, England) · 2019Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenIO is a novel web-server, designed to assist clinical genomics researchers and medical doctors in the diagnostic process of rare genetic diseases. The tool identifies the most probable variants causing a rare disease, using the genomic and clinical information provided by a medical practitioner. Variants identified in a whole-genome, whole-exome or target sequencing studies are annotated, classified and filtered by clinical significance. Candidate genes associated with the patient's symptoms, suspected disease and complementary findings are identified to obtain a small manageable number of the most probable recessive and dominant candidate gene variants associated with the rare disease case. Additionally, following the American College of Medical Genetics and Genomics and the Association of Molecular Pathology (ACMG-AMP) guidelines and recommendations, all potentially pathogenic variants that might be contributing to disease and secondary findings are identified.
resultsA retrospective study was performed on 40 patients with a diagnostic rate of 40%. All the known genes that were previously considered as disease causing were correctly identified in the final inherit model output lists. In previously undiagnosed cases, we had no additional yield.
conclusionThis unique, intuitive and user-friendly tool to assists medical doctors in the clinical genomics diagnostic process is openly available at https://bioinformatics.ibioba-mpsp-conicet.gov.ar/GenIO/ .
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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.