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MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

Overview of attention for article published in Journal of Cheminformatics, August 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

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14 tweeters

Citations

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107 Dimensions

Readers on

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201 Mendeley
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2 CiteULike
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Title
MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics
Published in
Journal of Cheminformatics, August 2015
DOI 10.1186/s13321-015-0087-1
Pubmed ID
Authors

James G Jeffryes, Ricardo L Colastani, Mona Elbadawi-Sidhu, Tobias Kind, Thomas D Niehaus, Linda J Broadbelt, Andrew D Hanson, Oliver Fiehn, Keith E J Tyo, Christopher S Henry

Abstract

In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 201 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
Brazil 3 1%
Sweden 1 <1%
Germany 1 <1%
Switzerland 1 <1%
South Africa 1 <1%
United Kingdom 1 <1%
Iceland 1 <1%
Hungary 1 <1%
Other 0 0%
Unknown 188 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 62 31%
Researcher 44 22%
Professor 16 8%
Student > Master 15 7%
Student > Bachelor 11 5%
Other 32 16%
Unknown 21 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 23%
Biochemistry, Genetics and Molecular Biology 35 17%
Chemistry 25 12%
Computer Science 14 7%
Engineering 12 6%
Other 35 17%
Unknown 34 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 16 February 2017.
All research outputs
#1,685,271
of 12,010,397 outputs
Outputs from Journal of Cheminformatics
#186
of 467 outputs
Outputs of similar age
#41,900
of 238,941 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 11 outputs
Altmetric has tracked 12,010,397 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 467 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. This one has gotten more attention than average, scoring higher than 59% 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 238,941 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 11 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 63% of its contemporaries.