Abstract
Cluster analysis methods have been extensively researched, but the adoption of new methods is often hindered by technical barriers in their implementation and use. WebGimm is a free cluster analysis web-service, and an open source general purpose clustering web-server infrastructure designed to facilitate easy deployment of integrated cluster analysis servers based on clustering and functional annotation algorithms implemented in R. Integrated functional analyses and interactive browsing of both, clustering structure and functional annotations provides a complete analytical environment for cluster analysis and interpretation of results. The Java Web Start client-based interface is modeled after the familiar cluster/treeview packages making its use intuitive to a wide array of biomedical researchers. For biomedical researchers, WebGimm provides an avenue to access state of the art clustering procedures. For Bioinformatics methods developers, WebGimm offers a convenient avenue to deploy their newly developed clustering methods. WebGimm server, software and manuals can be freely accessed at http://ClusterAnalysis.org/.
Highlights
Identifying groups of co-expressed genes through cluster analysis has been successfully used to elucidate affected biological pathways and postulate transcriptional regulatory mechanisms
We developed WebGimm, an open source general purpose clustering web-server infrastructure designed to facilitate the easy deployment of integrated cluster analysis servers based on clustering algorithms implemented in R
Implementation WebGimm is an open source general purpose clustering web-server infrastructure designed to facilitate the easy deployment of integrated cluster analysis servers based on clustering algorithms implemented in R
Summary
Identifying groups of co-expressed genes through cluster analysis has been successfully used to elucidate affected biological pathways and postulate transcriptional regulatory mechanisms. An integrated web-based cluster/treeview-like platform that incorporates functional enrichment analysis will further improve the utility of even simple hierarchical clustering procedures. We aimed to combine the “wrapper” model to facilitate access to clustering algorithms implemented in R, with the web-server model of deployment that obviates any local software installation.
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