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AssociationViewer: a scalable and integrated software tool for visualization of large-scale variation data in genomic
Olivier Martin1, Armand Valsesia, Amalio Telenti
1Swiss Institute of Bioinformatics, Switzerland.
Bioinformatics (Oxford, England)
|January 27, 2009
Summary
AssociationViewer is a new software tool for visualizing large genetic datasets, such as genome-wide association studies. It helps researchers interpret single nucleotide polymorphism (SNP) association studies within a broader genomic context.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Genome-wide association studies (GWAS) generate large datasets.
- Interpreting single nucleotide polymorphism (SNP) associations requires integrated visualization tools.
- Existing tools may lack comprehensive features for large-scale genomic data analysis.
Purpose of the Study:
- To introduce AssociationViewer, a software tool for visualizing and interpreting large-scale genetic and genomic data.
- To provide an integrated framework for analyzing results from genome-wide association studies.
- To enhance the interpretation of SNP association studies in their genomic context.
Main Methods:
- Developed AssociationViewer as a Java-based software application.
- Integrated data retrieval from Ensembl (gene annotations) and HapMap (linkage disequilibrium).
- Enabled import of custom data in BED or WIG formats.
- Implemented data track aggregation and intersection functionalities.
- Utilized an efficient cache system for handling large datasets.
Main Results:
- AssociationViewer facilitates the visualization of large-scale genetic and genomic data.
- The software enables integrated interpretation of SNP association studies.
- It supports the display of multiple, very large genomic datasets simultaneously.
- Functionalities for data aggregation and intersection are provided.
Conclusions:
- AssociationViewer offers a comprehensive solution for visualizing and interpreting complex genomic data.
- The tool supports diverse data sources, including public databases and custom files.
- Its efficient design allows for the display and analysis of very large genomic datasets, aiding genetic research.
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