Abstract
In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains.
Highlights
The biological sciences have a uniquely intertwined yet strangely dysfunctional relationship with the bioinformatics and visualization sciences
Bio/Life Sciences researchers and practitioners regularly rely on visualization techniques for solving a large range of problems, including use of charts, graphs and interactive displays
The extreme inertia of such visual representation and problem solving methods in the biological sciences is not solely limited to approaches developed before the advent of modern high-speed computers
Summary
The biological sciences have a uniquely intertwined yet strangely dysfunctional relationship with the bioinformatics and visualization sciences. It correctly identified the specific effect of the Bonus-question SNP on the gene containing it, but did not arrive at a correct conclusion regarding this gene’s overall contribution to affected status This entry was overwhelmingly selected by Team Bio as the entry that they would be most comfortable using immediately in their research work, and was selected for the Biology Experts Pick award for the contest. This filtering reduced that data to 29 SNPs of predicted relevance, and further, correctly identified the spiked-in subset of differential expression levels modulating affected and non-affected status This group approached the answer to the Bonus question using different tools than they produced for their primary elucidation of the effectors of disease status.
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