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Updated: Jul 13, 2026
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
AssociationDB: web-based exploration of genomic association
Dominik Seelow1, Katrin Hoffmann, Tom H Lindner
1Institute for Medical Genetics, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, D-13353 Berlin, Germany. dominik.seelow@charite.de
Bioinformatics (Oxford, England)
|July 31, 2007
Summary
Interpreting large-scale genome-wide association studies is challenging. We created AssociationDB, a web-based tool for interactive exploration of genetic association results, integrating gene and expression data for easier analysis.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Genome-wide association studies (GWAS) generate vast datasets with numerous markers, posing significant challenges for interpretation.
- Effective visualization and integration of genomic data are crucial for understanding complex genetic associations.
Purpose of the Study:
- To develop a user-friendly, graphical, web-based solution for interactive exploration of GWAS results.
- To integrate gene information and tissue-specific expression data with association findings.
Main Methods:
- Developed a graphical, web-based interface for visualizing association results.
- Implemented interactive features for filtering and highlighting genes based on user criteria.
- Integrated gene and tissue-specific expression data for comprehensive analysis.
Main Results:
- Association results are presented visually using physical position-based bars, with genes indicated by horizontal bars.
- The tool allows interactive filtering of association data and highlights genes based on specified terms or expression patterns.
- Provides drill-down capabilities via pop-up windows and hyperlinks for quick data access.
Conclusions:
- AssociationDB offers an effective solution for the interactive exploration and interpretation of GWAS results.
- The platform facilitates the integration of association data with genomic and expression information.
- It serves as a valuable tool for researchers analyzing case-control studies, usable as a standalone or integrated front-end.
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