Exploratory Visual AnalysisFrom the Computational Genetics Laboratory at Dartmouth


The availability of bioinformatics methods and software for the analysis of high-dimensional biological data has significantly increased in step with the availability of new technologies such as microarrays and mass spectrometry.  Despite the growth and maturation of bioinformatics it is still common for an experimental or observational biologist to receive their statistical or computational analysis results in a tab-delimited text file or an Excel spreadsheet.  The investigator is then left with thousands of rows of gene IDs and p-values.  Interpretation and inference becomes a tedious exercise in sifting through the results file one gene at a time.


Our goal was to develop an Exploratory Visual Analysis (EVA) database and software package to facilitate visual inspection of statistical and computational results in the context of biological information such as biochemical pathway, chromosomal location, and Gene Ontology.  EVA is an alternative to the Excel spreadsheet and is intended to allow the biologist  to quickly explore their results visually and in an interactive to identify biologically meaningful patterns.


We have developed a database for storing statistical and computational results along with annotated information about each gene from public databases such as Entrez Gene from NCBI.  The results database can then be interactivelly explored using the EVA GUI.  The prototype of EVA was developed in Oracle and Visual Basic with pilot funding from an NIH/NLM BISTI grant (P20 LM007613) in 2003.  With development funding from the Norris-Cotton Cancer Center we are porting EVA to MySQL and Java for platform independence.  An alpha version of the open-source EVA software package should be available for testing in early 2007.  For updates on the availability of EVA please check back here, check the Computational Genetics Laboratory home page, or check Epistasis Blog.

We are making good progress on EVA.  Some recent screenshots can be found below.


Reif DM, Dudek SM, Shaffer CM, Wang J, Moore JH. Exploratory visual analysis of pharmacogenomic results. Pac Symp Biocomput. 2005:296-307. [PubMed]

Reif DM, Moore JH. Visual analysis of statistical results from microarray studies of human breast cancer. Oncol Rep. 2006;15 Spec no.:1043-7.  [PubMed]

Huang C, Kim Y, Caramori ML, Moore JH, Rich SS, Mychaleckyj JC, Walker PC, Mauer M. Diabetic nephropathy is associated with gene expression levels of oxidative phosphorylation and related pathways. Diabetes. 2006 Jun;55(6):1826-31.  [PubMed]
Reif DM, Israel MA, Moore JH. Exploratory visual analysis of statistical results from microarray experiments comparing high and low grade glioma.  Cancer Informatics, in press (2007).


These screenshots were taken on January 18, 2007 and illustrate our progress with EVA.