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Evaluation of calling algorithms for array-CGH

Overview of attention for article published in Frontiers in Genetics, January 2013
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Title
Evaluation of calling algorithms for array-CGH
Published in
Frontiers in Genetics, January 2013
DOI 10.3389/fgene.2013.00217
Pubmed ID
Authors

Siddharth Roy, Alison Motsinger Reif

Abstract

Copy number variation (CNV) detection has become an integral part many of genetic studies and new technologies promise to revolutionize our ability to detect and link them to disease. However, recent studies highlight discrepancies in the genome wide CNV profile when measured by different technologies and even by the same technology. Furthermore, the change point algorithms used to call CNVs can have substantial disagreement on the same data set. We focus this article on comparative genomic hybridization (CGH) arrays because this platform lends itself well to accurate statistical modeling. We describe some newer methodological developments in local statistics that are well suited for CNV detection and calling on CGH arrays. Then we use both simulation studies and public data to compare these new local methods with the global methods that currently dominate literature. These results offer suggestions for choosing a particular method and provide insight to the lack of reproducibility that has been seen in the field so far.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Cuba 1 5%
Portugal 1 5%
Unknown 18 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 35%
Researcher 5 25%
Student > Doctoral Student 2 10%
Student > Bachelor 2 10%
Lecturer > Senior Lecturer 1 5%
Other 2 10%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 55%
Biochemistry, Genetics and Molecular Biology 4 20%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Computer Science 1 5%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 1 5%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 2013.
All research outputs
#15,233,143
of 22,727,570 outputs
Outputs from Frontiers in Genetics
#5,326
of 11,757 outputs
Outputs of similar age
#180,925
of 280,760 outputs
Outputs of similar age from Frontiers in Genetics
#200
of 319 outputs
Altmetric has tracked 22,727,570 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,757 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 54% 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 280,760 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 319 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.