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Nucleic Acids Res. 2017 Jul 3;45(W1):W189-W194. doi: 10.1093/nar/gkx445.

HGVA: the Human Genome Variation Archive.

Author information

1
Genomics England, Charterhouse Square, London EC1M 6BQ, UK.
2
Department of Haematology, University of Cambridge, Cambridge CB2 0PT, UK.
3
Department of Medicine, University ofCambridge, Cambridge, CB2 0QQ, UK.
4
NIHR BioResource-Rare Diseases,Cambridge University Hospitals, CambridgeBiomedical Campus, Cambridge CB2 0QQ, UK.
5
HPC Service, UIS, University of Cambridge, Cambridge CB3 0FB, UK.
6
Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocio, Sevilla 41013, Spain.
7
Functional Genomics Node (INB), FPS, Hospital Virgen del Rocio, Sevilla 41013, Spain.
8
Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, Sevilla 41013, Spain.

Abstract

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.

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