Visualization is an integral aspect of genomics data analysis where the output of procedures performed in computing environments like Bioconductor is often visualized. Algorithmic-statistical analysis and interactive visualization are usually disjoint but are most effective when used iteratively. We introduce tools that provide this tight-knit integration: Epiviz (http://epiviz.cbcb.umd.edu), a web-based genome browser, and the Epivizr Bioconductor package allowing interactive, extensible and reproducible visualization within a state-of-the-art data analysis platform.

Large studies profiling microbial communities and their association with healthy or disease phenotypes are now commonplace. Processed data from many of these studies are publicly available but significant effort is required for users to effectively organize, explore and integrate it, limiting the utility of these rich data resources. Effective integrative and interactive visual and statistical tools to analyze many metagenomic samples can greatly increase the value of these data for researchers. We present Metaviz, a tool for interactive exploratory data analysis of annotated microbiome taxonomic community profiles derived from marker gene or whole metagenome shotgun sequencing. Metaviz is uniquely designed to address the challenge of browsing the hierarchical structure of metagenomic data features while rendering visualizations of data values that are dynamically updated in response to user navigation. We use Metaviz to provide the UMD Metagenome Browser web service, allowing users to browse and explore data for more than 7000 microbiomes from published studies. Users can also deploy Metaviz as a web service, or use it to analyze data through the metavizr package to interoperate with state-of-the-art analysis tools available through Bioconductor. Metaviz is free and open source with the code, documentation and tutorials publicly accessible.

Along with the survey techniques of 16S rRNA amplicon and whole-metagenome shotgun sequencing, an array of tools exists for clustering, taxonomic annotation, normalization, and statistical analysis of microbiome sequencing results. Integrative and interactive visualization that enables researchers to perform exploratory analysis in this feature rich hierarchical data is an area of need. In this work, we present Metaviz, a web browser-based tool for interactive exploratory metagenomic data analysis. Metaviz can visualize abundance data served from an R session or a Python web service that queries a graph database. As metagenomic sequencing features have a hierarchy, we designed a novel navigation mechanism to explore this feature space. We visualize abundance counts with heatmaps and stacked bar plots that are dynamically updated as a user selects taxonomic features to inspect. Metaviz also supports common data exploration techniques, including PCA scatter plots to interpret variability in the dataset and alpha diversity boxplots for examining ecological community composition. The Metaviz application and documentation is hosted at http://www.metaviz.org.

BackgroundComputational 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. In our previous work, we introduced Epiviz, which bridges the gap between the two types of tools, simplifying these workflows.ResultsIn this paper we expand on the design decisions behind Epiviz, and introduce a series of new advanced features that further support the type of interactive exploratory workflow we have targeted. 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 genome browser. In our discussion section, we present security implications of the current design, as well as a series of limitations and future research steps.ConclusionsSince many of the design choices of Epiviz are novel in genomics data analysis, this paper serves both as a document of our own approaches with lessons learned, as well as a start point for future efforts in the same direction for the genomics community.

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