Genomic variation analyzer (caGWAS)
Query, integrate, analyze, and report significant associations between genetic variations and disease, drug response, or other clinical outcomes using Genome-Wide Association Scan (caGWAS).
caGWAS also allows researchers and bioinformaticians to access and analyze clinical and experimental data across multiple clinical studies.
- Read the caGWAS Fact Sheet for a basic overview
- Watch the caGWAS introductory presentation for a full capabilities overview
What would you like to do?
Learn more about this application
Visit the caGWAS section of the Molecular Analysis Tools Knowledge Center to submit questions, comments, feature requests, bug reports, and end-user requirements.
- Visit the caGWAS community wiki for additional information and resources
- View the caGWAS demo presentation
Access gene expression and other research data
Genomic/clinical data on glioblastoma multiforme (GBM) and ovarian tumors
(Available through TCGA)
- Learn about The Cancer Genome Atlas (TCGA) project
- Visit the TCGA Data Portal website
- Access the TCGA data
Gene expression/genomic characterizations and gene sequencing data for acute lymphoblastic and myeloid leukemias (ALL and AML), neuroblastoma, osteosarcoma, and high-risk Wilms tumor in children
(Available through TARGET)
- Learn about the TARGET project
- Visit the TARGET website
- Visit the TARGET Data Portal
- Browse the open-access TARGET data
- Apply for authorized access to the controlled-access TARGET data
- Access the TARGET data through the Authorized Access system
Human genes that have associations with cancer
(Available through the Cancer Gene Index)
Microarray gene expression data across a variety of cancers
(Available through NCI-hosted instance of caArray)
- Learn more about caArray
- Review a complete list of all available microarray datasets
- Register for free access to download and analyze this data
- Sample Data: GlaxoSmithKline has generated and donated SNP and expression data for over 300 cancer cell lines and made this information available through caArray.
Pre-surgical brain tumor MRI images matched to gene expression data
(Available through VASARI)
Prostate and breast cancer SNPs
(Available through the CGEMS)
- Learn about the Cancer Genetic Markers of Susceptibility (CGEMS) project
- Visit the CGEMS Data Portal website
- Apply for authorized access to CGEMS data
- Access the CGEMS data through the Authorized Access system
Primary brain tumor data matched to molecular and clinical data
(Available through REMBRANDT)
View other research data resources available through caBIG®.
Read more about this application
Review advanced resources and support options
caGWAS is part of the Integrative Cancer Research (ICR) Workspace, which works to produce interoperable tools and resources to assist researchers in the analysis of molecular and genomic data.
caBIG® offers a number of support options for those learning about and deploying caBIG® tools and resources.
- Get additional help from Support Service Providers.
- Learn from others using caGWAS and address any additional questions through the end user discussion forum.
caGWAS is part of a collection of life science software tools designed to facilitate the discovery of the next generation of cancer diagnostics and therapeutics. Other applications and infrastructure of interest include:
- Genomic data analysis platform (geWorkbench): Analyze genomic data using desktop tools for the management, analysis, visualization, and annotation of microarray-based gene expression and sequence data.
- Genomic data workflow tool (GenePattern): Analyze gene expression, proteomic, and SNP data; create multi-step analysis pipelines that enable in silico research.
- Microarray data management system (caArray): Support annotation and exchange of array data; integrate array data with clinical, imaging, tissue, and other functional genomics data.
- Data analysis portal (caIntegrator): Access and analyze clinical and experimental data across multiple clinical trials and studies.
- Molecular analysis portal (CMA): Find novel correlations between data and observations using analytical tools that help to visualize and integrate genomic data with corresponding clinical information.