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GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases

BACKGROUND: GenIO 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 pr...

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Autores principales: Koile, Daniel, Cordoba, Marta, de Sousa Serro, Maximiliano, Kauffman, Marcelo Andres, Yankilevich, Patricio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5787240/
https://www.ncbi.nlm.nih.gov/pubmed/29374474
http://dx.doi.org/10.1186/s12859-018-2027-3
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author Koile, Daniel
Cordoba, Marta
de Sousa Serro, Maximiliano
Kauffman, Marcelo Andres
Yankilevich, Patricio
author_facet Koile, Daniel
Cordoba, Marta
de Sousa Serro, Maximiliano
Kauffman, Marcelo Andres
Yankilevich, Patricio
author_sort Koile, Daniel
collection PubMed
description BACKGROUND: GenIO 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. RESULTS: A 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. CONCLUSION: This 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2027-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-57872402018-02-08 GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases Koile, Daniel Cordoba, Marta de Sousa Serro, Maximiliano Kauffman, Marcelo Andres Yankilevich, Patricio BMC Bioinformatics Software BACKGROUND: GenIO 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. RESULTS: A 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. CONCLUSION: This 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2027-3) contains supplementary material, which is available to authorized users. BioMed Central 2018-01-27 /pmc/articles/PMC5787240/ /pubmed/29374474 http://dx.doi.org/10.1186/s12859-018-2027-3 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Koile, Daniel
Cordoba, Marta
de Sousa Serro, Maximiliano
Kauffman, Marcelo Andres
Yankilevich, Patricio
GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases
title GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases
title_full GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases
title_fullStr GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases
title_full_unstemmed GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases
title_short GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases
title_sort genio: a phenotype-genotype analysis web server for clinical genomics of rare diseases
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5787240/
https://www.ncbi.nlm.nih.gov/pubmed/29374474
http://dx.doi.org/10.1186/s12859-018-2027-3
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