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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models
|
|---|---|
| Published in |
Frontiers in Physiology, August 2016
|
| DOI | 10.3389/fphys.2016.00327 |
| Pubmed ID | |
| Authors |
Maike K. Aurich, Ronan M. T. Fleming, Ines Thiele |
| Abstract |
Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community. |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Ireland | 2 | 22% |
| India | 1 | 11% |
| Switzerland | 1 | 11% |
| Unknown | 5 | 56% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 56% |
| Scientists | 3 | 33% |
| Unknown | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| United Kingdom | 1 | <1% |
| Colombia | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 165 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 47 | 27% |
| Student > Master | 27 | 16% |
| Researcher | 25 | 15% |
| Student > Bachelor | 12 | 7% |
| Student > Doctoral Student | 7 | 4% |
| Other | 25 | 15% |
| Unknown | 28 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 38 | 22% |
| Biochemistry, Genetics and Molecular Biology | 37 | 22% |
| Computer Science | 15 | 9% |
| Engineering | 14 | 8% |
| Medicine and Dentistry | 7 | 4% |
| Other | 24 | 14% |
| Unknown | 36 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 22 January 2020.
All research outputs
#6,169,308
of 22,881,964 outputs
Outputs from Frontiers in Physiology
#2,866
of 13,671 outputs
Outputs of similar age
#105,810
of 367,231 outputs
Outputs of similar age from Frontiers in Physiology
#32
of 165 outputs
Altmetric has tracked 22,881,964 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 13,671 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done well, scoring higher than 78% 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 367,231 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 70% of its contemporaries.
We're also able to compare this research output to 165 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.