Abstract

Kvik is an open-source system that we developed for explorative analysis of functional genomics data from large epidemiological studies. Creating such studies requires a significant amount of time and resources. It is therefore usual to reuse the data from one study for several research projects. Often each project requires implementing new analysis code, integration with specific knowledge bases, and specific visualizations. Existing data exploration tools do not provide all the required functionality for such multi-study data exploration. We have therefore developed the Kvik framework which makes it easy to implement specialized data exploration tools for specific projects. Applications in Kvik follow the three-tier architecture commonly used in web applications, with REST interfaces between the tiers. This makes it easy to adapt the applications to new statistical analyses, metadata, and visualizations. Kvik uses R to perform on-demand data analyses when researchers explore the data. In this note, we describe how we used Kvik to develop the Kvik Pathways application to explore gene expression data from healthy women with high and low plasma ratios of essential fatty acids using biological pathway visualizations. Researchers interact with Kvik Pathways through a web application that uses the JavaScript libraries Cytoscape.js and D3. We use Docker containers to make deployment of Kvik Pathways simple.

Highlights

  • Visual explorative analysis is essential to an understanding of biological functions in large-scale ‘omics’ datasets

  • We provide an online version of Kvik Pathways at kvik.cs.uit.no and a Docker image at registry.hub.docker.com/u/ fjukstad/kvik to run Kvik Pathways in a local Docker instance or on a cloud service such as Amazon Web Services or Google Compute Engine

  • Familiar Kvik Pathways uses the familiar pathway representations from Kyoto encyclopedia of genes and genomes (KEGG) and graphical user interfaces found in modern web applications

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Summary

30 Mar 2015 report report

Any reports and responses or comments on the article can be found at the end of the article. Keywords Functional genomics, Epidemiological studies, Data exploration, Ondemand data analysis, Open-source software, Kvik. This article is included in the Container Virtualization in Bioinformatics collection

Introduction
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