Go!Poly: A gene-oriented polymorphism database

doi:10.1002/humu.1209

2001-01-01

DOI：10.1002/humu.1209

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摘要

En 中文

Human genome polymorphisms play a key role in defining the molecular basis of phenotypic differences between individuals in aspects such as disease susceptibility and drug responses. The database requirements for supporting the study of human genetic variation have been well recognized. In order to meet these needs, several generalized databases have been built. However, it is still hard for users to find gene-related variation data from these huge and sophisticated databases. In its role as a gene-oriented directory of polymorphism data, Go!Poly (Gene Oriented Polymorphism Database; http://61.139.84.5/gopoly/) utilizes two new highly curated and non-redundant resources, LocusLink (http://www.ncbi.nlm.nih.gov/LocusLink/) and RefSeq (http://www.ncbi.nlm.nih.gov/LocusLink/refseq.html), as the standard for identifying and positioning nucleotide variations. As a generalized polymorphism database, Go!Poly extracts human gene, linked sequence variations of all common types (SNP, insertion-deletion, simple tandem repeat, and complex nucleotides variations) from various public resources including scientific journals and internet resources, such as HGBASE (http://hgbase.cgr.ki.se) and dbSNP (http://www.ncbi.nhn.nih.gov/SNPn/). The polymorphism data are then categorized into different gene loci, and the reference sequences given by LocusLink are used as positioning references. Through close integration with LocusLink, Go!Poly also provides facilitated connections among sequence data, gene name, and related biological information. This feature also makes Go!Poly easy to search and navigate. Future automated annotations and internal consistency checking may also benefit from this. Extensive efforts are being taken to make the polymorphism information generated by the Chinese scientific community available from this resource. Hum Mutat 18:382-387, 2001. (C) 2001 Wiley-Liss, Inc.

Keyword:

SNP

polymorphism database

genomic analysis

bioinformatics

variation