269 Works

Openphacts/Ops_Linkeddataapi: Open Phacts Linked Data Api 2.1.0

Fundatureanu-Sever, Randy Kerber, Stian Soiland-Reyes, Ian Dunlop, Antonis Loizou, Ronald Siebes, Egon Willighagen & Paul Groth
This release improves and extends the earlier versions. New API methods are included around interactions.

Openphacts/Ops_Linkeddataapi: Open Phacts Linked Data Api 2.1.0

Fundatureanu-Sever, Randy Kerber, Stian Soiland-Reyes, Ian Dunlop, Antonis Loizou, Ronald Siebes, Egon Willighagen & Paul Groth
This release improves and extends the earlier versions. New API methods are included around interactions.

Deliverable Report D6.2 Enanomapper Year 2 Dissemination Report

Friederike Ehrhart Bengt Fadeel
The key objectives of WP6 are to disseminate and raise awareness of the scientific results, tools and applications developed in the eNanoMapper project among the user communities in academia and industry, and to provide training on these eNanoMapper tools, through online seminars, and other training events. In the second year of the project, the project partners have produced additional online seminars or webinars on selected topics as well as tutorials on tools developed in other...

Additional file 3: of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Examples in Python. This zip archive contains the data and python script for the three python examples. (ZIP 15714 kb)

Additional file 10: of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
R packages. This zip archive contains both the RPathVisio package and the BridgeDbR package. The RPathVisio and BridgeDbR packages can be used in R. The jar files are being provided for archival purposes, for use please obtain a recent version of the software from the project websites. (ZIP 14914 kb)

Additional file 4: of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
R scripts for the case study.This zip archive contains the R scripts used in the case study including the arrayQC workflow. (ZIP 46 kb)

Additional file 1: Table S1. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Java API for PathVisioRPC. The table lists and provides brief descriptions of all functions implemented in PathVisioRPC, which can be called from the client languages to execute tasks. (TXT 6 kb)

Additional file 8: Figure S2. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Oxidative stress pathway [52] for splenocytes showing the logFC and P-value for day 1, day 2 and day 5. (PDF 104 kb)

Additional file 9: Figure S3. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Oxidative stress pathway [52] for bone marrow cells showing the logFC and P-value for day 1, day 2, and day 5. (PDF 105 kb)

Additional file 5: Figure S1. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Box plots showing array quality control and normalisation results. Box plots showing array quality control and normalization results using different normalization methods (BGcorrected, Loess, Loess + Scale, Loess + Quantile). (PDF 424 kb)

Additional file 2: of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
PathVisioRPC jar files. This zip archive contains both the plugin and standalone versions of the PathVisioRPC program packaged as jar files. The plugin can be installed in the PathVisio program and the standalone jar can be run in the command line to launch the PathVisioRPC server. The jar files are being provided for archival purposes, for use please obtain a recent version of the software from the project websites. (ZIP 11950 kb)

Additional file 6: Table S2. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Gene level statistics. The lists of differentially expressed genes for each of the nine comparisons were collated into a single file, which was used for further analysis. (TXT 722 kb)

Additional file 7: Table S3. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Table containing highly enriched GO terms for bone marrow cells, PBMCs, and splenocytes. (TXT 10 kb)

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

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
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...

Additional file 7: Table S3. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Table containing highly enriched GO terms for bone marrow cells, PBMCs, and splenocytes. (TXT 10 kb)

Additional file 6: Table S2. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Gene level statistics. The lists of differentially expressed genes for each of the nine comparisons were collated into a single file, which was used for further analysis. (TXT 722 kb)

Additional file 2: of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
PathVisioRPC jar files. This zip archive contains both the plugin and standalone versions of the PathVisioRPC program packaged as jar files. The plugin can be installed in the PathVisio program and the standalone jar can be run in the command line to launch the PathVisioRPC server. The jar files are being provided for archival purposes, for use please obtain a recent version of the software from the project websites. (ZIP 11950 kb)

Additional file 5: Figure S1. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Box plots showing array quality control and normalisation results. Box plots showing array quality control and normalization results using different normalization methods (BGcorrected, Loess, Loess + Scale, Loess + Quantile). (PDF 424 kb)

Additional file 9: Figure S3. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Oxidative stress pathway [52] for bone marrow cells showing the logFC and P-value for day 1, day 2, and day 5. (PDF 105 kb)

Additional file 8: Figure S2. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Oxidative stress pathway [52] for splenocytes showing the logFC and P-value for day 1, day 2 and day 5. (PDF 104 kb)

Additional file 1: Table S1. of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Java API for PathVisioRPC. The table lists and provides brief descriptions of all functions implemented in PathVisioRPC, which can be called from the client languages to execute tasks. (TXT 6 kb)

Additional file 4: of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
R scripts for the case study.This zip archive contains the R scripts used in the case study including the arrayQC workflow. (ZIP 46 kb)

Additional file 10: of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
R packages. This zip archive contains both the RPathVisio package and the BridgeDbR package. The RPathVisio and BridgeDbR packages can be used in R. The jar files are being provided for archival purposes, for use please obtain a recent version of the software from the project websites. (ZIP 14914 kb)

Additional file 3: of Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
Examples in Python. This zip archive contains the data and python script for the three python examples. (ZIP 15714 kb)

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

Anwesha Bohler, Lars Eijssen, Martijn Van Iersel, Christ Leemans, Egon Willighagen, Martina Kutmon, Magali Jaillard & Chris Evelo
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...

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