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Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data

Overview of attention for article published in Giga Science, February 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
26 X users
peer_reviews
1 peer review site
facebook
2 Facebook pages

Citations

dimensions_citation
78 Dimensions

Readers on

mendeley
168 Mendeley
citeulike
2 CiteULike
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Title
Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data
Published in
Giga Science, February 2016
DOI 10.1186/s13742-016-0115-8
Pubmed ID
Authors

Robert L. Davidson, Ralf J. M. Weber, Haoyu Liu, Archana Sharma-Oates, Mark R. Viant

Abstract

Metabolomics is increasingly recognized as an invaluable tool in the biological, medical and environmental sciences yet lags behind the methodological maturity of other omics fields. To achieve its full potential, including the integration of multiple omics modalities, the accessibility, standardization and reproducibility of computational metabolomics tools must be improved significantly. Here we present our end-to-end mass spectrometry metabolomics workflow in the widely used platform, Galaxy. Named Galaxy-M, our workflow has been developed for both direct infusion mass spectrometry (DIMS) and liquid chromatography mass spectrometry (LC-MS) metabolomics. The range of tools presented spans from processing of raw data, e.g. peak picking and alignment, through data cleansing, e.g. missing value imputation, to preparation for statistical analysis, e.g. normalization and scaling, and principal components analysis (PCA) with associated statistical evaluation. We demonstrate the ease of using these Galaxy workflows via the analysis of DIMS and LC-MS datasets, and provide PCA scores and associated statistics to help other users to ensure that they can accurately repeat the processing and analysis of these two datasets. Galaxy and data are all provided pre-installed in a virtual machine (VM) that can be downloaded from the GigaDB repository. Additionally, source code, executables and installation instructions are available from GitHub. The Galaxy platform has enabled us to produce an easily accessible and reproducible computational metabolomics workflow. More tools could be added by the community to expand its functionality. We recommend that Galaxy-M workflow files are included within the supplementary information of publications, enabling metabolomics studies to achieve greater reproducibility.

X Demographics

X Demographics

The data shown below were collected from the profiles of 26 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
United States 2 1%
South Africa 1 <1%
Brazil 1 <1%
Unknown 162 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 27%
Researcher 40 24%
Student > Master 20 12%
Student > Bachelor 14 8%
Other 7 4%
Other 21 13%
Unknown 21 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 23%
Agricultural and Biological Sciences 34 20%
Chemistry 22 13%
Computer Science 11 7%
Medicine and Dentistry 10 6%
Other 22 13%
Unknown 31 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 15 December 2018.
All research outputs
#1,274,540
of 25,584,565 outputs
Outputs from Giga Science
#206
of 1,174 outputs
Outputs of similar age
#20,981
of 313,505 outputs
Outputs of similar age from Giga Science
#4
of 16 outputs
Altmetric has tracked 25,584,565 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,174 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.7. This one has done well, scoring higher than 82% 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 313,505 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 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.