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affy2sv: an R package to pre-process Affymetrix CytoScan HD and 750K arrays for SNP, CNV, inversion and mosaicism calling

Overview of attention for article published in BMC Bioinformatics, May 2015
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Title
affy2sv: an R package to pre-process Affymetrix CytoScan HD and 750K arrays for SNP, CNV, inversion and mosaicism calling
Published in
BMC Bioinformatics, May 2015
DOI 10.1186/s12859-015-0608-y
Pubmed ID
Authors

Carles Hernandez-Ferrer, Ines Quintela Garcia, Katharina Danielski, Ángel Carracedo, Luis A. Pérez-Jurado, Juan R. González

Abstract

The well-known Genome-Wide Association Studies (GWAS) had led to many scientific discoveries using SNP data. Even so, they were not able to explain the full heritability of complex diseases. Now, other structural variants like copy number variants or DNA inversions, either germ-line or in mosaicism events, are being studies. We present the R package affy2sv to pre-process Affymetrix CytoScan HD/750k array (also for Genome-Wide SNP 5.0/6.0 and Axiom) in structural variant studies. We illustrate the capabilities of affy2sv using two different complete pipelines on real data. The first one performing a GWAS and a mosaic alterations detection study, and the other detecting CNVs and performing an inversion calling. Both examples presented in the article show up how affy2sv can be used as part of more complex pipelines aimed to analyze Affymetrix SNP arrays data in genetic association studies, where different types of structural variants are considered.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Unknown 49 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 20%
Student > Ph. D. Student 9 18%
Other 8 16%
Student > Master 5 10%
Student > Bachelor 4 8%
Other 6 12%
Unknown 8 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 19 38%
Agricultural and Biological Sciences 9 18%
Medicine and Dentistry 7 14%
Computer Science 3 6%
Immunology and Microbiology 1 2%
Other 0 0%
Unknown 11 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 21 May 2015.
All research outputs
#6,195,829
of 7,188,283 outputs
Outputs from BMC Bioinformatics
#3,082
of 3,306 outputs
Outputs of similar age
#179,414
of 213,096 outputs
Outputs of similar age from BMC Bioinformatics
#113
of 116 outputs
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