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Pathomx: an interactive workflow-based tool for the analysis of metabolomic data

Overview of attention for article published in BMC Bioinformatics, December 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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1 Google+ user

Citations

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56 Mendeley
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2 CiteULike
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Title
Pathomx: an interactive workflow-based tool for the analysis of metabolomic data
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0396-9
Pubmed ID
Authors

Martin A Fitzpatrick, Catherine M McGrath, Stephen P Young

Abstract

BackgroundMetabolomics is a systems approach to the analysis of cellular processes through small-molecule metabolite profiling. Standardisation of sample handling and acquisition approaches has contributed to reproducibility. However, the development of robust methods for the analysis of metabolomic data is a work-in-progress. The tools that do exist are often not well integrated, requiring manual data handling and custom scripting on a case-by-case basis. Furthermore, existing tools often require experience with programming environments such as MATLAB or R to use, limiting accessibility. Here we present Pathomx, a workflow-based tool for the processing, analysis and visualisation of metabolomic and associated data in an intuitive and extensible environment.ResultsThe core application provides a workflow editor, IPython kernel and a HumanCycTM-derived database of metabolites, proteins and genes. Toolkits provide reusable tools that may be linked together to create complex workflows. Pathomx is released with a base set of plugins for the import, processing and visualisation of data. The IPython backend provides integration with existing platforms including MATLAB and R, allowing data to be seamlessly transferred. Pathomx is supplied with a series of demonstration workflows and datasets. To demonstrate the use of the software we here present an analysis of 1D and 2D 1H NMR metabolomic data from a model system of mammalian cell growth under hypoxic conditions.ConclusionsPathomx is a useful addition to the analysis toolbox. The intuitive interface lowers the barrier to entry for non-experts, while scriptable tools and integration with existing tools supports complex analysis. We welcome contributions from the community.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 4%
Brazil 2 4%
Sweden 1 2%
United Kingdom 1 2%
Canada 1 2%
Unknown 49 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 30%
Student > Master 7 13%
Student > Doctoral Student 5 9%
Student > Bachelor 5 9%
Student > Ph. D. Student 5 9%
Other 10 18%
Unknown 7 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 25%
Chemistry 7 13%
Computer Science 6 11%
Biochemistry, Genetics and Molecular Biology 5 9%
Medicine and Dentistry 5 9%
Other 9 16%
Unknown 10 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 04 January 2015.
All research outputs
#7,449,539
of 22,774,233 outputs
Outputs from BMC Bioinformatics
#3,020
of 7,276 outputs
Outputs of similar age
#108,011
of 361,216 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 135 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 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 gotten more attention than average, scoring higher than 50% 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 361,216 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.