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Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes

Overview of attention for article published in PLoS Computational Biology, April 2009
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1 X user
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1 Wikipedia page

Citations

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72 Dimensions

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122 Mendeley
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7 CiteULike
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Title
Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes
Published in
PLoS Computational Biology, April 2009
DOI 10.1371/journal.pcbi.1000374
Pubmed ID
Authors

Curt Scharfe, Henry Horng-Shing Lu, Jutta K. Neuenburg, Edward A. Allen, Guan-Cheng Li, Thomas Klopstock, Tina M. Cowan, Gregory M. Enns, Ronald W. Davis

Abstract

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 122 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 2 2%
China 2 2%
Spain 2 2%
Singapore 1 <1%
Denmark 1 <1%
Switzerland 1 <1%
Japan 1 <1%
Luxembourg 1 <1%
Other 0 0%
Unknown 108 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 28%
Student > Ph. D. Student 25 20%
Student > Bachelor 17 14%
Student > Master 9 7%
Professor 6 5%
Other 17 14%
Unknown 14 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 42%
Medicine and Dentistry 20 16%
Biochemistry, Genetics and Molecular Biology 15 12%
Computer Science 9 7%
Psychology 2 2%
Other 11 9%
Unknown 14 11%
Attention Score in Context

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 01 June 2013.
All research outputs
#8,127,820
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#5,345
of 9,043 outputs
Outputs of similar age
#36,349
of 107,400 outputs
Outputs of similar age from PLoS Computational Biology
#28
of 54 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 9,043 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 107,400 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 65% of its contemporaries.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.