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A network based covariance test for detecting multivariate eQTL in saccharomyces cerevisiae

Overview of attention for article published in BMC Systems Biology, January 2016
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

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6 tweeters

Citations

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1 Dimensions

Readers on

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12 Mendeley
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Title
A network based covariance test for detecting multivariate eQTL in saccharomyces cerevisiae
Published in
BMC Systems Biology, January 2016
DOI 10.1186/s12918-015-0245-0
Pubmed ID
Authors

Huili Yuan, Zhenye Li, Nelson L.S. Tang, Minghua Deng

Abstract

Expression quantitative trait locus (eQTL) analysis has been widely used to understand how genetic variations affect gene expressions in the biological systems. Traditional eQTL is investigated in a pair-wise manner in which one SNP affects the expression of one gene. In this way, some associated markers found in GWAS have been related to disease mechanism by eQTL study. However, in real life, biological process is usually performed by a group of genes. Although some methods have been proposed to identify a group of SNPs that affect the mean of gene expressions in the network, the change of co-expression pattern has not been considered. So we propose a process and algorithm to identify the marker which affects the co-expression pattern of a pathway. Considering two genes may have different correlations under different isoforms which is hard to detect by the linear test, we also consider the nonlinear test. When we applied our method to yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 166 modules, with each module consisting of a group of genes and one eQTL where the eQTL regulate the co-expression patterns of the group of genes. We found that many of these modules have biological significance. We propose a network based covariance test to identify the SNP which affects the structure of a pathway. We also consider the nonlinear test as considering two genes may have different correlations under different isoforms which is hard to detect by linear test.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Israel 1 8%
United States 1 8%
Unknown 10 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 33%
Student > Doctoral Student 2 17%
Student > Bachelor 2 17%
Student > Ph. D. Student 1 8%
Professor 1 8%
Other 2 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 50%
Agricultural and Biological Sciences 4 33%
Computer Science 1 8%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 29 January 2016.
All research outputs
#6,634,863
of 12,859,717 outputs
Outputs from BMC Systems Biology
#353
of 1,073 outputs
Outputs of similar age
#123,030
of 357,554 outputs
Outputs of similar age from BMC Systems Biology
#20
of 62 outputs
Altmetric has tracked 12,859,717 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,073 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 66% 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 357,554 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 62 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 67% of its contemporaries.