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Nucleic Acids Res. 2018 Jan 4;46(D1):D944-D949. doi: 10.1093/nar/gkx986.

Genome Variation Map: a data repository of genome variations in BIG Data Center.

Song S1, Tian D1, Li C1, Tang B1,2,3, Dong L1, Xiao J1,2,3,4, Bao Y1,2, Zhao W1, He H5, Zhang Z1,2,3,4.

Author information

1
BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
2
CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
3
University of Chinese Academy of Sciences, Beijing 100049, China.
4
Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai 200438, China.
5
School of Life Sciences, Peking University, Beijing 100871, China.

Abstract

The Genome Variation Map (GVM; http://bigd.big.ac.cn/gvm/) is a public data repository of genome variations. As a core resource in the BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, GVM dedicates to collect, integrate and visualize genome variations for a wide range of species, accepts submissions of different types of genome variations from all over the world and provides free open access to all publicly available data in support of worldwide research activities. Unlike existing related databases, GVM features integration of a large number of genome variations for a broad diversity of species including human, cultivated plants and domesticated animals. Specifically, the current implementation of GVM not only houses a total of ∼4.9 billion variants for 19 species including chicken, dog, goat, human, poplar, rice and tomato, but also incorporates 8669 individual genotypes and 13 262 manually curated high-quality genotype-to-phenotype associations for non-human species. In addition, GVM provides friendly intuitive web interfaces for data submission, browse, search and visualization. Collectively, GVM serves as an important resource for archiving genomic variation data, helpful for better understanding population genetic diversity and deciphering complex mechanisms associated with different phenotypes.

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