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CoNVaQ: a web tool for copy number variation-based association studies

Overview of attention for article published in BMC Genomics, May 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
7 tweeters

Citations

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8 Dimensions

Readers on

mendeley
27 Mendeley
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1 CiteULike
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Title
CoNVaQ: a web tool for copy number variation-based association studies
Published in
BMC Genomics, May 2018
DOI 10.1186/s12864-018-4732-8
Pubmed ID
Authors

Simon Jonas Larsen, Luisa Matos do Canto, Silvia Regina Rogatto, Jan Baumbach

Abstract

Copy number variations (CNVs) are large segments of the genome that are duplicated or deleted. Structural variations in the genome have been linked to many complex diseases. Similar to how genome-wide association studies (GWAS) have helped discover single-nucleotide polymorphisms linked to disease phenotypes, the extension of GWAS to CNVs has aided the discovery of structural variants associated with human traits and diseases. We present CoNVaQ, an easy-to-use web-based tool for CNV-based association studies. The web service allows users to upload two sets of CNV segments and search for genomic regions where the occurrence of CNVs is significantly associated with the phenotype. CoNVaQ provides two models: a simple statistical model using Fisher's exact test and a novel query-based model matching regions to user-defined queries. For each region, the method computes a global q-value statistic by repeated permutation of samples among the populations. We demonstrate our platform by using it to analyze a data set of HPV-positive and HPV-negative penile cancer patients. CoNVaQ provides a simple workflow for performing CNV-based association studies. It is made available as a web platform in order to provide a user-friendly workflow for biologists and clinicians to carry out CNV data analysis without installing any software. Through the web interface, users are also able to analyze their results to find overrepresented GO terms and pathways. In addition, our method is also available as a package for the R programming language. CoNVaQ is available at https://convaq.compbio.sdu.dk .

Twitter Demographics

The data shown below were collected from the profiles of 7 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 30%
Student > Doctoral Student 3 11%
Other 3 11%
Student > Ph. D. Student 3 11%
Student > Master 3 11%
Other 6 22%
Unknown 1 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 52%
Agricultural and Biological Sciences 8 30%
Medicine and Dentistry 2 7%
Psychology 1 4%
Unknown 2 7%

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 11 June 2018.
All research outputs
#6,651,688
of 13,055,200 outputs
Outputs from BMC Genomics
#3,048
of 7,678 outputs
Outputs of similar age
#108,976
of 270,946 outputs
Outputs of similar age from BMC Genomics
#6
of 21 outputs
Altmetric has tracked 13,055,200 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 7,678 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 59% 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 270,946 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 59% of its contemporaries.
We're also able to compare this research output to 21 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 71% of its contemporaries.