University of Texas Medical Branch at Galveston researchers have developed a powerful visual analytical approach to explore genetic data, enabling scientists to identify novel patterns of information that could be crucial to human health.
The method, which combines three different "bipartite visual representations" of genetic information, is described in an article to appear in the Journal of the American Medical Informatics Association. The work won a distinguished paper award when it was presented at the AMIA Summit on Translational Bioinformatics in March 2012.
In the paper, the authors use their technique to analyze data on genetic alterations in humans known as single-nucleotide polymorphisms, or SNPs. Among other things, the frequencies of particular SNPs are associated with an individual's ancestral origins; for the study, the researchers chose to examine SNP data from 60 individuals from Nigeria and 60 individuals from Utah.
"We selected SNPs that we already knew differentiated between the two groups, and then showed that our method can reveal more about the data than traditional methods," said UTMB associate professor Suresh Bhavnani, lead author on the JAMIA paper and a member of UTMB's Institute for Translational Sciences. "This is a fresh way of looking at genetic data, a methodological contribution that we believe can help biologists and clinicians make better sense of a variety of biomarkers."
Like many kinds of biomedical data, Bhavnani said, datasets describing individuals and their SNPs are particularly suited to visual representations that are bipartite: that is, they simultaneously present two different classes of data. In the case of the Utah-Nigeria SNP data, Bhavnani and his colleagues started with what is known as a bipartite network visualization an intricate computer-generated arrangement of colored dots and black, gray and white lines.
"In the bipartite network you see both the i
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| Contact: Jim Kelly jpkelly@utmb.edu 409-772-8791 University of Texas Medical Branch at Galveston Source:Eurekalert |
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