↓ Skip to main content

MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, December 2014
Altmetric Badge

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)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
32 Dimensions

Readers on

mendeley
115 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments
Published in
Frontiers in Bioengineering and Biotechnology, December 2014
DOI 10.3389/fbioe.2014.00072
Pubmed ID
Authors

Pietro Franceschi, Roman Mylonas, Nir Shahaf, Matthias Scholz, Panagiotis Arapitsas, Domenico Masuero, Georg Weingart, Silvia Carlin, Urska Vrhovsek, Fulvio Mattivi, Ron Wehrens

Abstract

Due to their sensitivity and speed, mass-spectrometry based analytical technologies are widely used to in metabolomics to characterize biological phenomena. To address issues like metadata organization, quality assessment, data processing, data storage, and, finally, submission to public repositories, bioinformatic pipelines of a non-interactive nature are often employed, complementing the interactive software used for initial inspection and visualization of the data. These pipelines often are created as open-source software allowing the complete and exhaustive documentation of each step, ensuring the reproducibility of the analysis of extensive and often expensive experiments. In this paper, we will review the major steps which constitute such a data processing pipeline, discussing them in the context of an open-source software for untargeted MS-based metabolomics experiments recently developed at our institute. The software has been developed by integrating our metaMS R package with a user-friendly web-based application written in Grails. MetaMS takes care of data pre-processing and annotation, while the interface deals with the creation of the sample lists, the organization of the data storage, and the generation of survey plots for quality assessment. Experimental and biological metadata are stored in the ISA-Tab format making the proposed pipeline fully integrated with the Metabolights framework.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belarus 1 <1%
South Africa 1 <1%
Brazil 1 <1%
Unknown 112 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 27%
Student > Ph. D. Student 27 23%
Student > Doctoral Student 9 8%
Student > Bachelor 9 8%
Student > Master 8 7%
Other 17 15%
Unknown 14 12%
Readers by discipline Count As %
Chemistry 25 22%
Agricultural and Biological Sciences 25 22%
Biochemistry, Genetics and Molecular Biology 17 15%
Computer Science 8 7%
Medicine and Dentistry 5 4%
Other 15 13%
Unknown 20 17%
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 18 December 2014.
All research outputs
#12,615,710
of 22,774,233 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#1,320
of 6,524 outputs
Outputs of similar age
#160,782
of 354,373 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#16
of 41 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 6,524 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 79% 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 354,373 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 41 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.