1. VCGDB: a dynamic genome database of the Chinese population
- Author
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Yunchao Ling, Jun Yu, Yongbing Zhao, Jingfa Xiao, Jiayan Wu, Mingming Su, Jun Zhong, and Zhong Jin
- Subjects
China ,Genomics ,Genome browser ,Computational biology ,Biology ,Web Browser ,ENCODE ,Polymorphism, Single Nucleotide ,Database ,Big data ,Asian People ,Genetics ,Humans ,1000 Genomes Project ,Comparative genomics ,Genome, Human ,Chromosome Mapping ,Computational Biology ,Genome project ,Search Engine ,Genetics, Population ,Chinese population ,Databases, Nucleic Acid ,Dynamic genome ,Biotechnology ,Personal genomics ,Reference genome ,Genome-Wide Association Study - Abstract
Background The data released by the 1000 Genomes Project contain an increasing number of genome sequences from different nations and populations with a large number of genetic variations. As a result, the focus of human genome studies is changing from single and static to complex and dynamic. The currently available human reference genome (GRCh37) is based on sequencing data from 13 anonymous Caucasian volunteers, which might limit the scope of genomics, transcriptomics, epigenetics, and genome wide association studies. Description We used the massive amount of sequencing data published by the 1000 Genomes Project Consortium to construct the Virtual Chinese Genome Database (VCGDB), a dynamic genome database of the Chinese population based on the whole genome sequencing data of 194 individuals. VCGDB provides dynamic genomic information, which contains 35 million single nucleotide variations (SNVs), 0.5 million insertions/deletions (indels), and 29 million rare variations, together with genomic annotation information. VCGDB also provides a highly interactive user-friendly virtual Chinese genome browser (VCGBrowser) with functions like seamless zooming and real-time searching. In addition, we have established three population-specific consensus Chinese reference genomes that are compatible with mainstream alignment software. Conclusions VCGDB offers a feasible strategy for processing big data to keep pace with the biological data explosion by providing a robust resource for genomics studies; in particular, studies aimed at finding regions of the genome associated with diseases.
- Published
- 2014