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This article is part of the supplement: 22nd International Conference on Genome Informatics: Systems Biology

Open Access Proceedings

SigCS base: an integrated genetic information resource for human cerebral stroke

Young-Kyu Park12, Ok Sun Bang3, Min-Ho Cha3, Jaeheup Kim4, John W Cole5, Doheon Lee2* and Young Joo Kim6*

Author affiliations

1 Medical Genome Research Center, KRIBB, Daejeon 305-806, Korea

2 Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Korea

3 Department of Medical Research, KIOM, Daejeon 305-811, Korea

4 Cogent Biotechnology Inc., Rockville, MD 20850, USA

5 Maryland Stroke Center, Department of Neurology, Baltimore Veterans Affairs Medical Center and the University of Maryland School of Medicine, Baltimore MD 21201-1559, USA

6 Genome Resource Center, KRIBB, Daejeon 305-806, Korea

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Citation and License

BMC Systems Biology 2011, 5(Suppl 2):S10  doi:10.1186/1752-0509-5-S2-S10

Published: 14 December 2011

Abstract

Background

To understand how stroke risk factors mechanistically contribute to stroke, the genetic components regulating each risk factor need to be integrated and evaluated with respect to biological function and through pathway-based algorithms. This resource will provide information to researchers studying the molecular and genetic causes of stroke in terms of genomic variants, genes, and pathways.

Methods

Reported genetic variants, gene structure, phenotypes, and literature information regarding stroke were collected and extracted from publicly available databases describing variants, genome, proteome, functional annotation, and disease subtypes. Stroke related candidate pathways and etiologic genes that participate significantly in risk were analyzed in terms of canonical pathways in public biological pathway databases. These efforts resulted in a relational database of genetic signals of cerebral stroke, SigCS base, which implements an effective web retrieval system.

Results

The current version of SigCS base documents 1943 non-redundant genes with 11472 genetic variants and 165 non-redundant pathways. The web retrieval system of SigCS base consists of two principal search flows, including: 1) a gene-based variant search using gene table browsing or a keyword search, and, 2) a pathway-based variant search using pathway table browsing. SigCS base is freely accessible at http://sysbio.kribb.re.kr/sigcs webcite.

Conclusions

SigCS base is an effective tool that can assist researchers in the identification of the genetic factors associated with stroke by utilizing existing literature information, selecting candidate genes and variants for experimental studies, and examining the pathways that contribute to the pathophysiological mechanisms of stroke.