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Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment

Overview of attention for article published in BMC Bioinformatics, August 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
15 tweeters
peer_reviews
1 peer review site

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
103 Mendeley
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Title
Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1161-z
Pubmed ID
Authors

Hong Pan, Joanna D. Holbrook, Neerja Karnani, Chee Keong Kwoh

Abstract

The interplay among genetic, environment and epigenetic variation is not fully understood. Advances in high-throughput genotyping methods, high-density DNA methylation detection and well-characterized sample collections, enable epigenetic association studies at the genomic and population levels (EWAS). The field has extended to interrogate the interaction of environmental and genetic (GxE) influences on epigenetic variation. Also, the detection of methylation quantitative trait loci (methQTLs) and their association with health status has enhanced our knowledge of epigenetic mechanisms in disease trajectory. However analysis of this type of data brings computational challenges and there are few practical solutions to enable large scale studies in standard computational environments. GEM is a highly efficient R tool suite for performing epigenome wide association studies (EWAS). GEM provides three major functions named GEM_Emodel, GEM_Gmodel and GEM_GxEmodel to study the interplay of Gene, Environment and Methylation (GEM). Within GEM, the pre-existing "Matrix eQTL" package is utilized and extended to study methylation quantitative trait loci (methQTL) and the interaction of genotype and environment (GxE) to determine DNA methylation variation, using matrix based iterative correlation and memory-efficient data analysis. Benchmarking presented here on a publicly available dataset, demonstrated that GEM can facilitate reliable genome-wide methQTL and GxE analysis on a standard laptop computer within minutes. The GEM package facilitates efficient EWAS study in large cohorts. It is written in R code and can be freely downloaded from Bioconductor at https://www.bioconductor.org/packages/GEM/ .

Twitter Demographics

The data shown below were collected from the profiles of 15 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 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Canada 1 <1%
Unknown 101 98%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 26 25%
Researcher 21 20%
Student > Ph. D. Student 20 19%
Student > Bachelor 12 12%
Student > Master 7 7%
Other 9 9%
Unknown 8 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 41 40%
Agricultural and Biological Sciences 28 27%
Medicine and Dentistry 9 9%
Engineering 2 2%
Social Sciences 2 2%
Other 10 10%
Unknown 11 11%

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 20 June 2018.
All research outputs
#3,158,471
of 18,838,632 outputs
Outputs from BMC Bioinformatics
#1,264
of 6,443 outputs
Outputs of similar age
#55,141
of 273,834 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 27 outputs
Altmetric has tracked 18,838,632 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,443 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 80% 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 273,834 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 79% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.