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dc.contributor.advisorCorrada Bravo, Hectoren_US
dc.contributor.authorChelaru, Florinen_US
dc.date.accessioned2015-06-26T05:39:48Z
dc.date.available2015-06-26T05:39:48Z
dc.date.issued2015en_US
dc.identifierhttps://doi.org/10.13016/M2ZS7X
dc.identifier.urihttp://hdl.handle.net/1903/16635
dc.description.abstractComputational and visual data analysis for genomics has traditionally involved a combination of tools and resources, of which the most ubiquitous consist of genome browsers, focused mainly on integrative visualization of large numbers of big datasets, and computational environments, focused on data modeling of a small number of moderately sized datasets. Workflows that involve the integration and exploration of multiple heterogeneous data sources, small and large, public and user specific have been poorly addressed by these tools. Commonly, the data visualized in these tools is the output of analyses performed in powerful computing environments like R/Bioconductor or Python. Two essential aspects of data analysis are usually treated as distinct, in spite of being part of the same exploratory process: algorithmic analysis and interactive visualization. In current technologies these are not integrated within one tool, but rather, one precedes the other. Recent technological advances in web-based data visualization have made it possible for interactive visualization tools to tightly integrate with powerful algorithmic tools, without being restricted to one such tool in particular. We introduce Epiviz (http://epiviz.cbcb.umd.edu), an integrative visualization tool that bridges the gap between the two types of tools, simplifying genomic data analysis workflows. Epiviz is the first genomics interactive visualization tool to provide tight-knit integration with computational and statistical modeling and data analysis. We discuss three ways in which Epiviz advances the field of genomic data analysis: 1) it brings code to interactive visualizations at various different levels; 2) takes the first steps in the direction of collaborative data analysis by incorporating user plugins from source control providers, as well as by allowing analysis states to be shared among the scientific community; 3) combines established analysis features that have never before been available simultaneously in a visualization tool for genomics. Epiviz can be used in multiple branches of genomics data analysis for various types of datasets, of which we detail two: functional genomics data, aligned to a continuous coordinate such as the genome, and metagenomics, organized according to volatile hierarchical coordinate spaces. We also present security implications of the current design, performance benchmarks, a series of limitations and future research steps.en_US
dc.language.isoenen_US
dc.titleEpiviz: Integrative Visual Analysis Software for Genomicsen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pqcontrolledBioinformaticsen_US
dc.subject.pqcontrolledGeneticsen_US
dc.subject.pquncontrolledDNA methylationen_US
dc.subject.pquncontrolledfunctional genomicsen_US
dc.subject.pquncontrolledgene expressionen_US
dc.subject.pquncontrolledintegrative visualizationen_US
dc.subject.pquncontrolledmetagenomicsen_US
dc.subject.pquncontrolledR Bioconductoren_US


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