Epiviz: a view inside the design of an integrated visual analysis software for genomics

Abstract

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.

Metaviz: interactive statistical and visual analysis of metagenomic data

“…Given the structure of metagenomic data, the user navigation tools and the database storage are tailored to taxonomic hierarchies. We moved from a relational database model used in Epiviz ( 15 ), our previous interactive data analysis tool for functional genomic data such as gene expression and methylation data, to a graph database to manage the feature hierarchy and abundance counts. The fundamental operation enabled by this data back end is to efficiently aggregate abundance counts to a specific subset of nodes in the taxonomic hierarchy during interactive exploration.…”

Metaviz: interactive statistical and visual analysis of metagenomic data

“…Given the structure of metagenomic data, the user navigation tools and the database storage are tailored to taxonomic hierarchies. We moved from a relational database model used in Epiviz (Chelaru & Bravo, 2015), an interactive application for visualization and analysis of functional genomic data such as gene expression and methylation data, to a graph database to hold the feature hierarchy and abundance counts.…”

Highlights from the 5th Symposium on Biological Data Visualization: Part 1

“…In the omics field, Younesy et al [ 1 ] describe VisRseq : a user-friendly interface for biologists to use libraries in R that provides a method for linking R -apps with interactive components. Chelaru et al [ 2 ] expand on the design behind Epiviz , another tool for bringing genome visualization and computational environments together. Hennig et al [ 3 ] describe Pan-Tetris and Aurisano et al [ 4 ] describe BactoGeNIE : both systems are designed for comparing different genomes.…”