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MetaGenyo: a web tool for meta-analysis of genetic association studies

Overview of attention for article published in BMC Bioinformatics, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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17 X users

Citations

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98 Dimensions

Readers on

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70 Mendeley
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1 CiteULike
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Title
MetaGenyo: a web tool for meta-analysis of genetic association studies
Published in
BMC Bioinformatics, December 2017
DOI 10.1186/s12859-017-1990-4
Pubmed ID
Authors

Jordi Martorell-Marugan, Daniel Toro-Dominguez, Marta E. Alarcon-Riquelme, Pedro Carmona-Saez

Abstract

Genetic association studies (GAS) aims to evaluate the association between genetic variants and phenotypes. In the last few years, the number of this type of study has increased exponentially, but the results are not always reproducible due to experimental designs, low sample sizes and other methodological errors. In this field, meta-analysis techniques are becoming very popular tools to combine results across studies to increase statistical power and to resolve discrepancies in genetic association studies. A meta-analysis summarizes research findings, increases statistical power and enables the identification of genuine associations between genotypes and phenotypes. Meta-analysis techniques are increasingly used in GAS, but it is also increasing the amount of published meta-analysis containing different errors. Although there are several software packages that implement meta-analysis, none of them are specifically designed for genetic association studies and in most cases their use requires advanced programming or scripting expertise. We have developed MetaGenyo, a web tool for meta-analysis in GAS. MetaGenyo implements a complete and comprehensive workflow that can be executed in an easy-to-use environment without programming knowledge. MetaGenyo has been developed to guide users through the main steps of a GAS meta-analysis, covering Hardy-Weinberg test, statistical association for different genetic models, analysis of heterogeneity, testing for publication bias, subgroup analysis and robustness testing of the results. MetaGenyo is a useful tool to conduct comprehensive genetic association meta-analysis. The application is freely available at http://bioinfo.genyo.es/metagenyo/ .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 20%
Student > Ph. D. Student 10 14%
Student > Bachelor 7 10%
Student > Master 4 6%
Student > Doctoral Student 3 4%
Other 11 16%
Unknown 21 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 20%
Medicine and Dentistry 7 10%
Agricultural and Biological Sciences 6 9%
Computer Science 3 4%
Social Sciences 2 3%
Other 11 16%
Unknown 27 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 25 October 2018.
All research outputs
#3,801,655
of 23,509,982 outputs
Outputs from BMC Bioinformatics
#1,381
of 7,404 outputs
Outputs of similar age
#80,664
of 442,670 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 137 outputs
Altmetric has tracked 23,509,982 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 7,404 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 81% 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 442,670 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 81% of its contemporaries.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.