gsasnp2

We have hosted the application gsasnp2 in order to run this application in our online workstations with Wine or directly.

Quick description about gsasnp2:

* GSA-SNP2 is a successor of GSA-SNP (Nam et al. 2010, NAR web server issue). GSA-SNP2 accepts human GWAS summary data (rs numbers, p-values) or gene-wise p-values and outputs pathway genesets �enriched� with genes associated with the given phenotype. It also provides both local and global protein interaction networks in the associated pathways.

* Article: SYoon, HCTNguyen, YJYoo, JKim, BBaik, SKim, JKim, SKim, DNam, "Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2", Nucleic Acids Research, Vol. 46(10), e60(2018).

* PubMed ID: 29562348

* DOI: 10.1093/nar/gky175

-> PLEASE MOVE OR MAKE A COPY OF 'DATA' FOLDER INTO YOUR INTENSIVE TEST FOLDER (I.E. LINUX, MAC OR WINDOWS SPECIFIED FOLDER) TO ALLOW THE PROGRAM TO FIND THE PREDESIGNED DATA.

* UPDATE NOTE:
-> Sep-1-2020: add an update for Ubuntu-20.04. You will need Boost library installed (sudo apt-get install libboost-all-dev)
-> Mar-7-2018: revise header terms in the output file.

Features:

• 1/ 'DECENT TYPE I ERROR CONTROL' achieved by the following two processes: A) Gene scores are �adjusted� to the number of SNPs assigned to each gene using monotone cubic spline trend curve. B) Adjacent genes with high inter-gene correlations within each pathway were removed
• 2/ 'HIGH POWER AND FAST COMPUTATION' based on the random set model
• 3/ 'NO CRITICAL FREE PARAMETER'
• 4/ 'PROTEIN INTERACTION NETWORKS' among the member genes were visualized for the significant pathways. This function enables the user to prioritize the core sub-networks within and across the significant pathways. The STRING and HIPPIE networks are currently provided
• 5/ 'EASY TO USE': It only requires GWAS summary data (or gene p-values) and takes only a minute or two to get results. Other powerful self-contained pathway tools require the SNP correlation input as well and take a much longer time. User can also upload their own pathway gene-sets and protein interaction networks.

User interface: Win32 (MS Windows), Command-line.
Programming Language: C++, PHP, JavaScript.
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