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.
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.
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
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.