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Published on: January 2, 2011
An eQTL biological data visualization challenge and approaches from the visualization community.
Christopher W Bartlett1, Soo Yeon Cheong, Liping Hou
1The Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA. christopher.bartlett@nationwidechildrens.org
BMC Bioinformatics
|May 22, 2012
Summary
The BioVis 2011 contest challenged participants to analyze complex biological data, specifically expression Quantitative Trait Locus (eQTL) data, to identify disease-predicting patterns of single nucleotide polymorphisms (SNPs). This initiative aimed to foster innovation in biological data visualization and analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Data Visualization
Background:
- The IEEE VisWeek conference established a symposium on Biological Data Visualization (BioVis) to bridge visualization and life sciences communities.
- Domain-specific visualization symposia aim to address unique challenges and requirements within specialized fields.
- The BioVis symposium sought to integrate biological data and domain expertise into visualization research and vice versa.
Purpose of the Study:
- To explore unique visualization characteristics and requirements within the biological data domain.
- To foster innovation by presenting a challenging biological data analysis problem with no existing solutions.
- To develop viable tools for complex biological grand challenges through a data analysis and visualization contest.
Main Methods:
- A data analysis and visualization contest was organized as a key activity of the BioVis 2011 symposium.
- Contestants were provided with a synthetic expression Quantitative Trait Locus (eQTL) dataset, including gene expression, single nucleotide polymorphism (SNP) variation, and a hypothetical disease model.
- Participants were tasked with analyzing the data to identify patterns of SNPs and interactions that predict an individual's disease state, using a mix of analytical and visual exploratory methods.
Main Results:
- Nine teams competed, employing diverse analytical and visual exploratory approaches.
- Entries were judged by independent panels of visualization and biological experts, with awards for favorite entries and an overall best entry.
- Special mentions were given for innovative aspects, and a bonus question assessed the practical applicability of methods to a gene therapy scenario.
Conclusions:
- The BioVis contest successfully engaged participants with a challenging biological domain, aiming to produce practical tools.
- The contest highlighted the potential of combining visualization and analytical methods for complex biological data analysis.
- Future BioVis contests will continue to address underserved biological domains with novel, challenging questions.
