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Shared genomics: high performance computing for distributed insights in genomic medical research
David C Hoyle1, Mark Delderfield, Lee Kitching
1North West Institute for BioHealth Informatics, University of Manchester, Manchester, M13 9PL, UK.
Studies in Health Technology and Informatics
|July 14, 2009
Summary
This study introduces High Performance Computing (HPC) genetics analysis codes for clinical researchers. These tools automate statistical analysis and bioinformatics annotation for genome-wide association studies, improving disease genetics research.
Area of Science:
- Genomic Medicine
- Computational Biology
- Statistical Genetics
Background:
- Genome-wide association studies (GWAS) are crucial for identifying genetic links to diseases.
- The increasing scale of GWAS necessitates advanced computational solutions for statistical analysis.
- Bioinformatics annotation is essential for interpreting the biomedical implications of genetic findings.
Purpose of the Study:
- To develop High Performance Computing (HPC) statistical genetics analysis codes tailored for clinical researchers.
- To integrate automated bioinformatics annotation with statistical genetics analysis.
- To create a user-friendly interface (Workbench) for accessing HPC and annotation tools.
Main Methods:
- Development of specialized HPC statistical genetics analysis codes.
- Implementation of automated bioinformatics annotation by orchestrating web services via scientific workflows.
- Creation of a client Workbench to abstract HPC infrastructure and bioinformatics processes.
Main Results:
- Successfully developed and tested HPC codes for statistical genetics analysis.
- Automated annotation of statistical results with relevant biological information.
- Provided an accessible Workbench interface for clinical researchers.
Conclusions:
- The developed HPC codes and automated annotation facilitate clinical researchers' use of large-scale genetic data.
- The Workbench enhances usability and collaboration in disease genetics research.
- This approach supports the advancement of statistical genetics and genomic medicine.
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