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MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data

Overview of attention for article published in Metabolomics, October 2014
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  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

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5 tweeters

Citations

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33 Dimensions

Readers on

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80 Mendeley
Title
MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data
Published in
Metabolomics, October 2014
DOI 10.1007/s11306-014-0734-y
Pubmed ID
Authors

Alexander Kaever, Manuel Landesfeind, Kirstin Feussner, Alina Mosblech, Ingo Heilmann, Burkhard Morgenstern, Ivo Feussner, Peter Meinicke

Abstract

A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite (http://marvis.gobics.de), which features the seamless highly interactive filtering, combination, clustering, and visualization of omics data sets. The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
Germany 2 3%
South Africa 1 1%
Spain 1 1%
Netherlands 1 1%
Unknown 73 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 31%
Student > Ph. D. Student 17 21%
Student > Master 11 14%
Student > Bachelor 10 13%
Unspecified 4 5%
Other 13 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 43%
Biochemistry, Genetics and Molecular Biology 18 23%
Chemistry 10 13%
Unspecified 4 5%
Engineering 4 5%
Other 10 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 09 August 2015.
All research outputs
#3,138,443
of 11,410,107 outputs
Outputs from Metabolomics
#211
of 718 outputs
Outputs of similar age
#57,771
of 205,677 outputs
Outputs of similar age from Metabolomics
#9
of 23 outputs
Altmetric has tracked 11,410,107 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 718 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 70% 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 205,677 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 71% of its contemporaries.
We're also able to compare this research output to 23 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.