Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

BMC Bioinformatics

Software

Magali Jaillard¹  Egon L. Willighagen²  Lars M. T. Eijssen²  Christ Leemans²  Anwesha Bohler³  Martina Kutmon⁴  Chris T. Evelo⁴  Martijn P. van Iersel⁵ 

[1] Bioinformatics Research Department, BioMérieux S.A, 69280, Marcy l’Etoile, France;Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, Maastricht, MD, The Netherlands;Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, Maastricht, MD, The Netherlands;Netherlands Consortium for Systems Biology (NCSB), Amsterdam, The Netherlands;Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, Maastricht, MD, The Netherlands;Netherlands Consortium for Systems Biology (NCSB), Amsterdam, The Netherlands;Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, Maastricht, MD, The Netherlands;General Bioinformatics, RG4 7RT, Reading, Berkshire, UK;

关键词: Automation;    Biological pathways;    Data visualisation;    Multi-omics;    Pathway analysis;    Pathway building;    R package;    Workflow integration;   

DOI  :  10.1186/s12859-015-0708-8

 received in 2014-10-01, accepted in 2015-08-17,  发布年份 2015

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Bohler et al. 2015