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

Bohler A, Eijssen LM, van Iersel MP, Leemans C, Willighagen EL, Kutmon M, Jaillard M, Evelo CT - BMC Bioinformatics (2015)

Bottom Line: Consequently, making the full-fledged pathway analysis tool PathVisio available from various scripting languages will benefit researchers.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.It also enables automation of tasks performed using PathVisio, making it useful to PathVisio users performing repeated visualisation and analysis tasks.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, MD, Maastricht, The Netherlands. anwesha.dutta@maastrichtuniversity.nl.

ABSTRACT

Background: Biological 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.

Results: We 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.

Conclusions: PathVisioRPC 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.

No MeSH data available.


Related in: MedlinePlus

Gene Ontology Enrichment analysis for Bone Marrow Cells. a Gene Expression Data visualised on Gene Ontology Terms, (b) Back page showing the Gene Ontology Annotation for GO term GO:0050729, positive regulation of inflammatory response, (c) Back page showing the Gene expression data for the five genes (Adora3, S100a9,Ccl3, Tnfsf4, and Tlr3) found in the dataset which map to the GO class positive regulation of inflammatory response
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4546821&req=5

Fig3: Gene Ontology Enrichment analysis for Bone Marrow Cells. a Gene Expression Data visualised on Gene Ontology Terms, (b) Back page showing the Gene Ontology Annotation for GO term GO:0050729, positive regulation of inflammatory response, (c) Back page showing the Gene expression data for the five genes (Adora3, S100a9,Ccl3, Tnfsf4, and Tlr3) found in the dataset which map to the GO class positive regulation of inflammatory response

Mentions: In bone marrow cells, processes related to defence response, developmental processes, and response to external/chemical stimuli are highly affected, as was also observed in the original publication [50]. For instance, the GO term “positive regulation of inflammatory response” is highly stimulated and the transcripts in the dataset related to that term are mostly upregulated (Fig. 3). For splenocytes, transcripts increased in abundance are mostly involved in cell cycle arrest and regulation, whereas the original publication reported these to be down regulated. Even though the original publication reports that transcripts involved in immune response are mostly upregulated, it is clear from the present analysis that these transcripts show a 50/50 ratio between up- and down-regulation. Whether that is caused by real differences in remaining data after quality control and statistical evaluation or just differences in how data is communicated is not clear since the processed data from the original publication is not available. The GO terms RNA processing and proteolysis contain mostly transcripts down regulated under the experimental conditions [50]. In PBMCs, similar to the original publication, only few GO terms were enriched for up-regulated transcripts. For down-regulated transcripts, regulation of gene expression is the most notable term that is enriched. Other enriched GO terms for PBMCs contain approximately as many up- and down-regulated transcripts [50].Fig. 3


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

Bohler A, Eijssen LM, van Iersel MP, Leemans C, Willighagen EL, Kutmon M, Jaillard M, Evelo CT - BMC Bioinformatics (2015)

Gene Ontology Enrichment analysis for Bone Marrow Cells. a Gene Expression Data visualised on Gene Ontology Terms, (b) Back page showing the Gene Ontology Annotation for GO term GO:0050729, positive regulation of inflammatory response, (c) Back page showing the Gene expression data for the five genes (Adora3, S100a9,Ccl3, Tnfsf4, and Tlr3) found in the dataset which map to the GO class positive regulation of inflammatory response
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4546821&req=5

Fig3: Gene Ontology Enrichment analysis for Bone Marrow Cells. a Gene Expression Data visualised on Gene Ontology Terms, (b) Back page showing the Gene Ontology Annotation for GO term GO:0050729, positive regulation of inflammatory response, (c) Back page showing the Gene expression data for the five genes (Adora3, S100a9,Ccl3, Tnfsf4, and Tlr3) found in the dataset which map to the GO class positive regulation of inflammatory response
Mentions: In bone marrow cells, processes related to defence response, developmental processes, and response to external/chemical stimuli are highly affected, as was also observed in the original publication [50]. For instance, the GO term “positive regulation of inflammatory response” is highly stimulated and the transcripts in the dataset related to that term are mostly upregulated (Fig. 3). For splenocytes, transcripts increased in abundance are mostly involved in cell cycle arrest and regulation, whereas the original publication reported these to be down regulated. Even though the original publication reports that transcripts involved in immune response are mostly upregulated, it is clear from the present analysis that these transcripts show a 50/50 ratio between up- and down-regulation. Whether that is caused by real differences in remaining data after quality control and statistical evaluation or just differences in how data is communicated is not clear since the processed data from the original publication is not available. The GO terms RNA processing and proteolysis contain mostly transcripts down regulated under the experimental conditions [50]. In PBMCs, similar to the original publication, only few GO terms were enriched for up-regulated transcripts. For down-regulated transcripts, regulation of gene expression is the most notable term that is enriched. Other enriched GO terms for PBMCs contain approximately as many up- and down-regulated transcripts [50].Fig. 3

Bottom Line: Consequently, making the full-fledged pathway analysis tool PathVisio available from various scripting languages will benefit researchers.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.It also enables automation of tasks performed using PathVisio, making it useful to PathVisio users performing repeated visualisation and analysis tasks.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, MD, Maastricht, The Netherlands. anwesha.dutta@maastrichtuniversity.nl.

ABSTRACT

Background: Biological 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.

Results: We 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.

Conclusions: PathVisioRPC 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.

No MeSH data available.


Related in: MedlinePlus