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

Overview of attention for article published in BMC Bioinformatics, August 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

blogs
1 blog
twitter
21 tweeters
wikipedia
2 Wikipedia pages
googleplus
4 Google+ users

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
62 Mendeley
citeulike
3 CiteULike
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Title
Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment
Published in
BMC Bioinformatics, August 2015
DOI 10.1186/s12859-015-0708-8
Pubmed ID
Authors

Anwesha Bohler, Lars M. T. Eijssen, Martijn P. van Iersel, Christ Leemans, Egon L. Willighagen, Martina Kutmon, Magali Jaillard, Chris T. Evelo

Abstract

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. 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. 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 .

Twitter Demographics

The data shown below were collected from the profiles of 21 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 3 5%
United Kingdom 2 3%
Switzerland 1 2%
Brazil 1 2%
Korea, Republic of 1 2%
Unknown 54 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 26%
Student > Ph. D. Student 14 23%
Student > Bachelor 7 11%
Other 5 8%
Student > Postgraduate 5 8%
Other 13 21%
Unknown 2 3%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 19 31%
Agricultural and Biological Sciences 17 27%
Computer Science 9 15%
Engineering 3 5%
Chemistry 3 5%
Other 8 13%
Unknown 3 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 29. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 November 2021.
All research outputs
#1,134,328
of 22,400,534 outputs
Outputs from BMC Bioinformatics
#145
of 7,171 outputs
Outputs of similar age
#16,159
of 254,881 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 22,400,534 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,171 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 254,881 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them