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UGbS-Flex, a novel bioinformatics pipeline for imputation-free SNP discovery in polyploids without a reference genome: finger millet as a case study

Overview of attention for article published in BMC Plant Biology, June 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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Citations

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Title
UGbS-Flex, a novel bioinformatics pipeline for imputation-free SNP discovery in polyploids without a reference genome: finger millet as a case study
Published in
BMC Plant Biology, June 2018
DOI 10.1186/s12870-018-1316-3
Pubmed ID
Authors

Peng Qi, Davis Gimode, Dipnarayan Saha, Stephan Schröder, Debkanta Chakraborty, Xuewen Wang, Mathews M. Dida, Russell L. Malmberg, Katrien M. Devos

Abstract

Research on orphan crops is often hindered by a lack of genomic resources. With the advent of affordable sequencing technologies, genotyping an entire genome or, for large-genome species, a representative fraction of the genome has become feasible for any crop. Nevertheless, most genotyping-by-sequencing (GBS) methods are geared towards obtaining large numbers of markers at low sequence depth, which excludes their application in heterozygous individuals. Furthermore, bioinformatics pipelines often lack the flexibility to deal with paired-end reads or to be applied in polyploid species. UGbS-Flex combines publicly available software with in-house python and perl scripts to efficiently call SNPs from genotyping-by-sequencing reads irrespective of the species' ploidy level, breeding system and availability of a reference genome. Noteworthy features of the UGbS-Flex pipeline are an ability to use paired-end reads as input, an effective approach to cluster reads across samples with enhanced outputs, and maximization of SNP calling. We demonstrate use of the pipeline for the identification of several thousand high-confidence SNPs with high representation across samples in an F3-derived F2 population in the allotetraploid finger millet. Robust high-density genetic maps were constructed using the time-tested mapping program MAPMAKER which we upgraded to run efficiently and in a semi-automated manner in a Windows Command Prompt Environment. We exploited comparative GBS with one of the diploid ancestors of finger millet to assign linkage groups to subgenomes and demonstrate the presence of chromosomal rearrangements. The paper combines GBS protocol modifications, a novel flexible GBS analysis pipeline, UGbS-Flex, recommendations to maximize SNP identification, updated genetic mapping software, and the first high-density maps of finger millet. The modules used in the UGbS-Flex pipeline and for genetic mapping were applied to finger millet, an allotetraploid selfing species without a reference genome, as a case study. The UGbS-Flex modules, which can be run independently, are easily transferable to species with other breeding systems or ploidy levels.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 23%
Student > Ph. D. Student 8 14%
Student > Master 6 11%
Student > Doctoral Student 5 9%
Student > Bachelor 4 7%
Other 7 12%
Unknown 14 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 51%
Biochemistry, Genetics and Molecular Biology 8 14%
Linguistics 1 2%
Veterinary Science and Veterinary Medicine 1 2%
Immunology and Microbiology 1 2%
Other 1 2%
Unknown 16 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 07 December 2020.
All research outputs
#6,238,832
of 23,090,520 outputs
Outputs from BMC Plant Biology
#477
of 3,287 outputs
Outputs of similar age
#108,489
of 328,710 outputs
Outputs of similar age from BMC Plant Biology
#11
of 55 outputs
Altmetric has tracked 23,090,520 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 3,287 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done well, scoring higher than 85% 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 328,710 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.