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Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes

Overview of attention for article published in PLoS Computational Biology, September 2012
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3 X users

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112 Dimensions

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196 Mendeley
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20 CiteULike
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Title
Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes
Published in
PLoS Computational Biology, September 2012
DOI 10.1371/journal.pcbi.1002694
Pubmed ID
Authors

Yuanfang Guan, Dmitriy Gorenshteyn, Margit Burmeister, Aaron K. Wong, John C. Schimenti, Mary Ann Handel, Carol J. Bult, Matthew A. Hibbs, Olga G. Troyanskaya

Abstract

Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as "functionality" and "functional relationships" are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 196 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 6%
United Kingdom 2 1%
Japan 2 1%
India 2 1%
Germany 1 <1%
Sweden 1 <1%
Italy 1 <1%
Switzerland 1 <1%
Canada 1 <1%
Other 3 2%
Unknown 171 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 61 31%
Researcher 44 22%
Student > Master 25 13%
Student > Bachelor 13 7%
Student > Postgraduate 13 7%
Other 28 14%
Unknown 12 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 91 46%
Biochemistry, Genetics and Molecular Biology 30 15%
Computer Science 30 15%
Medicine and Dentistry 9 5%
Engineering 4 2%
Other 15 8%
Unknown 17 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 April 2015.
All research outputs
#15,184,741
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#6,529
of 8,964 outputs
Outputs of similar age
#110,876
of 191,019 outputs
Outputs of similar age from PLoS Computational Biology
#64
of 117 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 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 25th percentile – i.e., 25% 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 191,019 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 117 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.