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HGVA: the Human Genome Variation Archive
High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting th...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570161/ https://www.ncbi.nlm.nih.gov/pubmed/28535294 http://dx.doi.org/10.1093/nar/gkx445 |
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author | Lopez, Javier Coll, Jacobo Haimel, Matthias Kandasamy, Swaathi Tarraga, Joaquin Furio-Tari, Pedro Bari, Wasim Bleda, Marta Rueda, Antonio Gräf, Stefan Rendon, Augusto Dopazo, Joaquin Medina, Ignacio |
author_facet | Lopez, Javier Coll, Jacobo Haimel, Matthias Kandasamy, Swaathi Tarraga, Joaquin Furio-Tari, Pedro Bari, Wasim Bleda, Marta Rueda, Antonio Gräf, Stefan Rendon, Augusto Dopazo, Joaquin Medina, Ignacio |
author_sort | Lopez, Javier |
collection | PubMed |
description | High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic data for key reference projects in a clean, fast and integrated fashion. HGVA provides an efficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libraries in Python, Java and JavaScript for fast programmatic access to its knowledge base. HGVA calculates population frequencies for these projects and enriches their data with variant annotation provided by CellBase, a rich and fast annotation solution. HGVA serves as a proof-of-concept of the genome analysis developments being carried out by the University of Cambridge together with UK's 100 000 genomes project and the National Institute for Health Research BioResource Rare-Diseases, in particular, deploying open-source for Computational Biology (OpenCB) software platform for storing and analyzing massive genomic datasets. |
format | Online Article Text |
id | pubmed-5570161 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-55701612017-08-29 HGVA: the Human Genome Variation Archive Lopez, Javier Coll, Jacobo Haimel, Matthias Kandasamy, Swaathi Tarraga, Joaquin Furio-Tari, Pedro Bari, Wasim Bleda, Marta Rueda, Antonio Gräf, Stefan Rendon, Augusto Dopazo, Joaquin Medina, Ignacio Nucleic Acids Res Web Server Issue High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic data for key reference projects in a clean, fast and integrated fashion. HGVA provides an efficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libraries in Python, Java and JavaScript for fast programmatic access to its knowledge base. HGVA calculates population frequencies for these projects and enriches their data with variant annotation provided by CellBase, a rich and fast annotation solution. HGVA serves as a proof-of-concept of the genome analysis developments being carried out by the University of Cambridge together with UK's 100 000 genomes project and the National Institute for Health Research BioResource Rare-Diseases, in particular, deploying open-source for Computational Biology (OpenCB) software platform for storing and analyzing massive genomic datasets. Oxford University Press 2017-07-03 2017-05-23 /pmc/articles/PMC5570161/ /pubmed/28535294 http://dx.doi.org/10.1093/nar/gkx445 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Web Server Issue Lopez, Javier Coll, Jacobo Haimel, Matthias Kandasamy, Swaathi Tarraga, Joaquin Furio-Tari, Pedro Bari, Wasim Bleda, Marta Rueda, Antonio Gräf, Stefan Rendon, Augusto Dopazo, Joaquin Medina, Ignacio HGVA: the Human Genome Variation Archive |
title | HGVA: the Human Genome Variation Archive |
title_full | HGVA: the Human Genome Variation Archive |
title_fullStr | HGVA: the Human Genome Variation Archive |
title_full_unstemmed | HGVA: the Human Genome Variation Archive |
title_short | HGVA: the Human Genome Variation Archive |
title_sort | hgva: the human genome variation archive |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570161/ https://www.ncbi.nlm.nih.gov/pubmed/28535294 http://dx.doi.org/10.1093/nar/gkx445 |
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