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The role of the Human Metabolome Database in inborn errors of metabolism

Overview of attention for article published in Journal of Inherited Metabolic Disease, April 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

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Title
The role of the Human Metabolome Database in inborn errors of metabolism
Published in
Journal of Inherited Metabolic Disease, April 2018
DOI 10.1007/s10545-018-0137-8
Pubmed ID
Authors

Rupasri Mandal, Danuta Chamot, David S. Wishart

Abstract

Metabolomics holds considerable promise to advance our understanding of human disease, including our understanding of inborn errors of metabolism (IEM). The application of metabolomics in IEM research has already led to the discovery of several novel IEMs and the identification of novel IEM biomarkers. However, with hundreds of known IEMs and more than 700 associated IEM metabolites, it is becoming increasingly challenging for clinical researchers to keep track of IEMs, their associated metabolites, and their corresponding metabolic mechanisms. Furthermore, when using metabolomics to assist in IEM biomarker discovery or even in IEM diagnosis, it is becoming much more difficult to properly identify metabolites from the complex NMR and MS spectra collected from IEM patients. To that end, comprehensive, open access metabolite databases that provide up-to-date referential information about metabolites, metabolic pathways, normal/abnormal metabolite concentrations, and reference NMR or MS spectra for compound identification are essential. Over the last few years, a number of compound databases, including the Human Metabolome Database (HMDB), have been developed to address these challenges. First described in 2007, the HMDB is now the world's largest and most comprehensive metabolomic resource for human metabolic studies. The latest release of the HMDB contains 114,100 metabolite entries (with 247 being relevant to IEMs), thousands of metabolite concentrations (with 600 being relevant to IEMs), and ~33,000 metabolic and disease-associated pathways (with 202 being relevant to IEMs). Here we provide a summary of the HMDB and offer some guidance on how it can be used in metabolomic studies of IEMs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 26%
Student > Ph. D. Student 13 21%
Student > Bachelor 4 6%
Student > Postgraduate 4 6%
Student > Doctoral Student 3 5%
Other 11 18%
Unknown 11 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 35%
Agricultural and Biological Sciences 9 15%
Medicine and Dentistry 7 11%
Chemistry 7 11%
Engineering 2 3%
Other 4 6%
Unknown 11 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 27 February 2019.
All research outputs
#4,733,068
of 23,344,526 outputs
Outputs from Journal of Inherited Metabolic Disease
#300
of 1,880 outputs
Outputs of similar age
#83,155
of 297,558 outputs
Outputs of similar age from Journal of Inherited Metabolic Disease
#3
of 39 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,880 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 83% 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 297,558 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 72% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.