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Exploratory visual analysis of pharmacogenomic results
David M Reif1, Scott M Dudek, Christian M Shaffer
1Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, NH 03756, USA. reif@chgr.mc.vanderbilt.edu
Summary
Exploratory Visual Analysis (EVA) software integrates statistical and annotation data for pharmacogenomic datasets. This tool facilitates novel hypothesis generation and replicates previous findings, enhancing drug discovery research.
Area of Science:
- Pharmacogenomics
- Bioinformatics
- Computational Biology
Background:
- Analyzing large pharmacogenomic datasets presents significant challenges.
- Integrating statistical and biological annotation is crucial for experimental result exploration.
- Existing tools often lack a flexible combination of statistical and annotation capabilities.
Purpose of the Study:
- To introduce the Exploratory Visual Analysis (EVA) software and database.
- To demonstrate EVA's utility in analyzing pharmacogenomic data.
- To showcase EVA's ability to generate novel hypotheses and replicate existing findings.
Main Methods:
- Development of the Exploratory Visual Analysis (EVA) software and database.
- Creation of a custom graphical user interface (GUI).
- Application of EVA to a publicly available pharmacogenomic dataset.
Main Results:
- EVA successfully replicated findings from a previous pharmacogenomic study.
- EVA elucidated novel, biologically plausible hypotheses.
- The software provides a flexible visual display for diverse statistical results and biological questions.
Conclusions:
- EVA offers a comprehensive solution for analyzing pharmacogenomic datasets.
- The tool integrates statistical and annotation information effectively.
- EVA has broad utility for the pharmacogenomics research community.