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Estimating copy numbers of alleles from population-scale high-throughput sequencing data

Overview of attention for article published in BMC Bioinformatics, January 2015
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3 tweeters

Citations

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Title
Estimating copy numbers of alleles from population-scale high-throughput sequencing data
Published in
BMC Bioinformatics, January 2015
DOI 10.1186/1471-2105-16-s1-s4
Pubmed ID
Authors

Takahiro Mimori, Naoki Nariai, Kaname Kojima, Yukuto Sato, Yosuke Kawai, Yumi Yamaguchi-Kabata, Masao Nagasaki

Abstract

With the recent development of microarray and high-throughput sequencing (HTS) technologies, a number of studies have revealed catalogs of copy number variants (CNVs) and their association with phenotypes and complex traits. In parallel, a number of approaches to predict CNV regions and genotypes are proposed for both microarray and HTS data. However, only a few approaches focus on haplotyping of CNV loci.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Norway 1 7%
Unknown 14 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 33%
Other 4 27%
Student > Ph. D. Student 4 27%
Student > Bachelor 1 7%
Professor > Associate Professor 1 7%
Other 0 0%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 33%
Biochemistry, Genetics and Molecular Biology 3 20%
Computer Science 2 13%
Medicine and Dentistry 2 13%
Linguistics 1 7%
Other 1 7%
Unknown 1 7%

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 25 February 2015.
All research outputs
#2,551,013
of 4,804,615 outputs
Outputs from BMC Bioinformatics
#1,955
of 2,804 outputs
Outputs of similar age
#80,813
of 145,265 outputs
Outputs of similar age from BMC Bioinformatics
#90
of 119 outputs
Altmetric has tracked 4,804,615 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,804 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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 145,265 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 119 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.