Peter Kinnaird

Computer supported collective action

Social media has become globally ubiquitous, transforming how people are networked and mobilized. This forum explores research and applications of these new networked publics at individual, organizational, and societal levels. ---Shelly Farnham, Editor

Finding genome-transcriptome-phenome association with structured association mapping and visualization in GenAMap.

Despite the success of genome-wide association studies in detecting novel disease variants, we are still far from a complete understanding of the mechanisms through which variants cause disease. Most of previous studies have considered only genome-phenome associations. However, the integration of transcriptome data may help further elucidate the mechanisms through which genetic mutations lead to disease and uncover potential pathways to target for treatment. We present a novel structured association mapping strategy for finding genome-transcriptome-phenome associations when SNP, gene-expression, and phenotype data are available for the same cohort. We do so via a two-step procedure where genome-transcriptome associations are identified by GFlasso, a sparse regression technique presented previously. Transcriptome-phenome associations are then found by a novel proposed method called gGFlasso, which leverages structure inherent in the genes and phenotypic traits. Due to the complex nature of three-way association results, visualization tools can aid in the discovery of causal SNPs and regulatory mechanisms affecting diseases. Using wellgrounded visualization techniques, we have designed new visualizations that filter through large three-way association results to detect interesting SNPs and associated genes and traits. The two-step GFlasso-gGFlasso algorithmic approach and new visualizations are integrated into GenAMap, a visual analytics system for structured association mapping. Results on simulated datasets show that our approach has the potential to increase the sensitivity and specificity of association studies, compared to existing procedures that do not exploit the full structural information of the data. We report results from an analysis on a publically available mouse dataset, showing that identified SNP-gene-trait associations are compatible with known biology.

GenAMap: Visualization strategies for structured association mapping

Association mapping studies promise to link DNA mutations to gene expression data, possibly leading to innovative treatments for diseases. One challenge in large-scale association mapping studies is exploring the results of the computational analysis to find relevant and interesting associations. Although many association mapping studies find associations from a genome-wide collection of genomic data to hundreds or thousands of traits, current visualization software only allow these associations to be explored one trait at a time. The inability to explore the association of a genomic location to multiple traits hides the inherent interaction between traits in the analysis. Additionally, researchers must rely on collections of in-house scripts and multiple tools to perform an analysis, adding time and effort to find interesting associations. In this paper, we present a novel visual analytics system called GenAMap. GenAMap replaces the time-consuming analysis of large-scale association mapping studies with exploratory visualization tools that give geneticists an overview of the data and lead them to relevant information. We present the results of a preliminary evaluation that validated our basic approach.

GWAS in a box: statistical and visual analytics of structured associations via GenAMap.

With the continuous improvement in genotyping and molecular phenotyping technology and the decreasing typing cost, it is expected that in a few years, more and more clinical studies of complex diseases will recruit thousands of individuals for pan-omic genetic association analyses. Hence, there is a great need for algorithms and software tools that could scale up to the whole omic level, integrate different omic data, leverage rich structure information, and be easily accessible to non-technical users. We present GenAMap, an interactive analytics software platform that 1) automates the execution of principled machine learning methods that detect genome- and phenome-wide associations among genotypes, gene expression data, and clinical or other macroscopic traits, and 2) provides new visualization tools specifically designed to aid in the exploration of association mapping results. Algorithmically, GenAMap is based on a new paradigm for GWAS and PheWAS analysis, termed structured association mapping, which leverages various structures in the omic data. We demonstrate the function of GenAMap via a case study of the Brem and Kruglyak yeast dataset, and then apply it on a comprehensive eQTL analysis of the NIH heterogeneous stock mice dataset and report some interesting findings. GenAMap is available from http://sailing.cs.cmu.edu/genamap.

Enabling dynamic network analysis through visualization in TVNViewer

BackgroundMany biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis.ResultsIn this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets.ConclusionsTVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.

Announcing the XRDS blog

X R D S • f a l l 2 0 1 2 • V o l . 1 9 • N o . 1 worked with the Electronic Frontier Foundation and the National Lawyers Guild among other organizations. Wolfgang Richter is a Ph.D. student at Carnegie Mellon in computer science focusing on distributed computing Systems. Wolf is developing technologies that will enable new applications in cloud computing. Dimitris Mitropoulos is a Ph.D. candidate tory posts from each of the bloggers in this issue. We hope you find them as diverse and informative as we do. By the time this issue is in your hands there should be even more posts from each of the bloggers up on the website and maybe even some new faces there as well. We’re continuing to expand the blog as well, so please write in and let us know what you think. Nominate a blogger or give feedback at eic@ xrds.acm.org. In the coming months we’ll be reaching out to ACM student chapters at universities worldwide. If you’re involved with one of these and would like to partner with XRDS, please reach out. Finally, we’re searching for help with promoting XRDS content on social networks and external blogs and news sites. If you have any interest in helping out, definitely get in touch! Strong candidates will have a background in computer science or technology and business or marketing. We’d love to hear from you!

An interview with Dr. Beth Simone Noveck

The former U.S. Deputy Chief Technology Officer and the author of Wiki Government: How Technology Can Make Government Better, Democracy Stronger and Citizens More Powerful discusses open government and what it really means.