Abstract
BackgroundThe growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software.ResultsChipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies.ConclusionsChipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available.
Highlights
The growth of high-throughput technologies such as microarrays and generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace
In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies
Chipster facilitates reproducible and collaborative research by enabling users to save the performed analysis steps as reusable, automatic workflows, which can be shared with other users
Summary
The growth of high-throughput technologies such as microarrays and generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. The growth of high-throughput technologies such as microarrays and generation sequencing (NGS) has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills, such as knowledge of the R programming language in the case of Bioconductor. This can be a bottleneck for wet lab scientists, who typically have a life science background and no Implementation Chipster’s ability to provide a biologist-friendly access to a powerful bioinformatics platform is technically based on a desktop application user interface, a flexible distributed architecture, and the ability to integrate many types of analysis tools. To make the client installation and updates as easy and automatic as possible, Chipster uses the Java Web Start technology
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