ArticleBMC bioinformatics2015
Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The metaRbolomics Toolbox in Bioconductor and beyond.Metabolites · 2019Review
- Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium.BMC bioinformatics · 2019Article
- Perturbation of IIS/TOR signaling alters the landscape of sex-differential gene expression in Drosophila.BMC genomics · 2018Article
- Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).Bioinformatics and biology insights · 2018Review
- Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources.PLoS computational biology · 2016Article
- WikiPathways: capturing the full diversity of pathway knowledge.Nucleic acids research · 2016Article
- Tandem Domains with Tuned Interactions Are a Powerful Biological Design Principle.PLoS biology · 2015Article
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Authors and funding
8 authors.
Funding
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Abstract
backgroundBiological pathways are descriptive diagrams of biological processes widely used for functional analysis of differentially expressed genes or proteins. Primary data analysis, such as quality control, normalisation, and statistical analysis, is often performed in scripting languages like R, Perl, and Python. Subsequent pathway analysis is usually performed using dedicated external applications. Workflows involving manual use of multiple environments are time consuming and error prone. Therefore, tools are needed that enable pathway analysis directly within the same scripting languages used for primary data analyses. Existing tools have limited capability in terms of available pathway content, pathway editing and visualisation options, and export file formats. Consequently, making the full-fledged pathway analysis tool PathVisio available from various scripting languages will benefit researchers.
resultsWe developed PathVisioRPC, an XMLRPC interface for the pathway analysis software PathVisio. PathVisioRPC enables creating and editing biological pathways, visualising data on pathways, performing pathway statistics, and exporting results in several image formats in multiple programming environments. We demonstrate PathVisioRPC functionalities using examples in Python. Subsequently, we analyse a publicly available NCBI GEO gene expression dataset studying tumour bearing mice treated with cyclophosphamide in R. The R scripts demonstrate how calls to existing R packages for data processing and calls to PathVisioRPC can directly work together. To further support R users, we have created RPathVisio simplifying the use of PathVisioRPC in this environment. We have also created a pathway module for the microarray data analysis portal ArrayAnalysis.org that calls the PathVisioRPC interface to perform pathway analysis. This module allows users to use PathVisio functionality online without having to download and install the software and exemplifies how the PathVisioRPC interface can be used by data analysis pipelines for functional analysis of processed genomics data.
conclusionsPathVisioRPC enables data visualisation and pathway analysis directly from within various analytical environments used for preliminary analyses. It supports the use of existing pathways from WikiPathways or pathways created using the RPC itself. It also enables automation of tasks performed using PathVisio, making it useful to PathVisio users performing repeated visualisation and analysis tasks. PathVisioRPC is freely available for academic and commercial use at http://projects.bigcat.unimaas.nl/pathvisiorpc.
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