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GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data

Overview of attention for article published in BMC Bioinformatics, January 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

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11 tweeters
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1 Facebook page

Citations

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79 Dimensions

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225 Mendeley
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Title
GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-016-0879-y
Pubmed ID
Authors

Arthur T. O. Melo, Radhika Bartaula, Iago Hale

Abstract

With its simple library preparation and robust approach to genome reduction, genotyping-by-sequencing (GBS) is a flexible and cost-effective strategy for SNP discovery and genotyping, provided an appropriate reference genome is available. For resource-limited curation, research, and breeding programs of underutilized plant genetic resources, however, even low-depth references may not be within reach, despite declining sequencing costs. Such programs would find value in an open-source bioinformatics pipeline that can maximize GBS data usage and perform high-density SNP genotyping in the absence of a reference. The GBS SNP-Calling Reference Optional Pipeline (GBS-SNP-CROP) developed and presented here adopts a clustering strategy to build a population-tailored "Mock Reference" from the same GBS data used for downstream SNP calling and genotyping. Designed for libraries of paired-end (PE) reads, GBS-SNP-CROP maximizes data usage by eliminating unnecessary data culling due to imposed read-length uniformity requirements. Using 150 bp PE reads from a GBS library of 48 accessions of tetraploid kiwiberry (Actinidia arguta), GBS-SNP-CROP yielded on average three times as many SNPs as TASSEL-GBS analyses (32 and 64 bp tag lengths) and over 18 times as many as TASSEL-UNEAK, with fewer genotyping errors in all cases, as evidenced by comparing the genotypic characterizations of biological replicates. Using the published reference genome of a related diploid species (A. chinensis), the reference-based version of GBS-SNP-CROP behaved similarly to TASSEL-GBS in terms of the number of SNPs called but had an improved read depth distribution and fewer genotyping errors. Our results also indicate that the sets of SNPs detected by the different pipelines above are largely orthogonal to one another; thus GBS-SNP-CROP may be used to augment the results of alternative analyses, whether or not a reference is available. By achieving high-density SNP genotyping in populations for which no reference genome is available, GBS-SNP-CROP is worth consideration by curators, researchers, and breeders of under-researched plant genetic resources. In cases where a reference is available, especially if from a related species or when the target population is particularly diverse, GBS-SNP-CROP may complement other reference-based pipelines by extracting more information per sequencing dollar spent. The current version of GBS-SNP-CROP is available at https://github.com/halelab/GBS-SNP-CROP.git.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 225 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Norway 2 <1%
Germany 1 <1%
France 1 <1%
Italy 1 <1%
Brazil 1 <1%
Argentina 1 <1%
Belgium 1 <1%
Spain 1 <1%
United States 1 <1%
Other 0 0%
Unknown 215 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 64 28%
Researcher 55 24%
Student > Master 28 12%
Student > Bachelor 16 7%
Student > Doctoral Student 10 4%
Other 25 11%
Unknown 27 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 151 67%
Biochemistry, Genetics and Molecular Biology 23 10%
Computer Science 5 2%
Engineering 4 2%
Business, Management and Accounting 2 <1%
Other 6 3%
Unknown 34 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 29 July 2016.
All research outputs
#1,705,494
of 10,444,782 outputs
Outputs from BMC Bioinformatics
#892
of 4,169 outputs
Outputs of similar age
#67,989
of 330,976 outputs
Outputs of similar age from BMC Bioinformatics
#36
of 134 outputs
Altmetric has tracked 10,444,782 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,169 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 78% 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 330,976 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 134 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 72% of its contemporaries.