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Evaluation of variant detection software for pooled next-generation sequence data

Overview of attention for article published in BMC Bioinformatics, July 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

news
1 news outlet
twitter
29 tweeters
facebook
1 Facebook page

Citations

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23 Dimensions

Readers on

mendeley
119 Mendeley
citeulike
2 CiteULike
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Title
Evaluation of variant detection software for pooled next-generation sequence data
Published in
BMC Bioinformatics, July 2015
DOI 10.1186/s12859-015-0624-y
Pubmed ID
Authors

Howard W. Huang, James C. Mullikin, Nancy F. Hansen

Abstract

Despite the tremendous drop in the cost of nucleotide sequencing in recent years, many research projects still utilize sequencing of pools containing multiple samples for the detection of sequence variants as a cost saving measure. Various software tools exist to analyze these pooled sequence data, yet little has been reported on the relative accuracy and ease of use of these different programs. In this manuscript we evaluate five different variant detection programs-The Genome Analysis Toolkit (GATK), CRISP, LoFreq, VarScan, and SNVer-with regard to their ability to detect variants in synthetically pooled Illumina sequencing data, by creating simulated pooled binary alignment/map (BAM) files using single-sample sequencing data from varying numbers of previously characterized samples at varying depths of coverage per sample. We report the overall runtimes and memory usage of each program, as well as each program's sensitivity and specificity to detect known true variants. GATK, CRISP, and LoFreq all gave balanced accuracy of 80 % or greater for datasets with varying per-sample depth of coverage and numbers of samples per pool. VarScan and SNVer generally had balanced accuracy lower than 80 %. CRISP and LoFreq required up to four times less computational time and up to ten times less physical memory than GATK did, and without filtering, gave results with the highest sensitivity. VarScan and SNVer had generally lower false positive rates, but also significantly lower sensitivity than the other three programs.

Twitter Demographics

The data shown below were collected from the profiles of 29 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 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 6%
Australia 2 2%
France 2 2%
Sweden 1 <1%
Netherlands 1 <1%
United Kingdom 1 <1%
China 1 <1%
Spain 1 <1%
Germany 1 <1%
Other 0 0%
Unknown 102 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 40 34%
Student > Ph. D. Student 31 26%
Student > Master 11 9%
Other 7 6%
Professor 5 4%
Other 14 12%
Unknown 11 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 44%
Biochemistry, Genetics and Molecular Biology 29 24%
Computer Science 11 9%
Medicine and Dentistry 6 5%
Veterinary Science and Veterinary Medicine 1 <1%
Other 5 4%
Unknown 15 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 31 March 2016.
All research outputs
#883,471
of 16,232,676 outputs
Outputs from BMC Bioinformatics
#145
of 5,874 outputs
Outputs of similar age
#15,768
of 236,658 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 2 outputs
Altmetric has tracked 16,232,676 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,874 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 97% 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 236,658 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them