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PCR-based assays for validation of single nucleotide polymorphism markers in rice and mungbean

Overview of attention for article published in Hereditas, January 2017
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Title
PCR-based assays for validation of single nucleotide polymorphism markers in rice and mungbean
Published in
Hereditas, January 2017
DOI 10.1186/s41065-016-0024-y
Pubmed ID
Authors

Thu Giang Thi Bui, Nguyen Thi Lan Hoa, Jo-yi Yen, Roland Schafleitner

Abstract

Single nucleotide polymorphism (SNP) markers are the method of choice for genetic analyses including diversity and quantitative trait loci (QTL) studies. Marker validation is essential for QTL studies, but the cost and workload are considerable when large numbers of markers need to be verified. Marker systems with low development costs would be most suitable for this task. We have tested allele specific polymerase chain reaction (PCR), tetra markers and a genotyping tool based on the single strand specific nuclease CEL-I to verify randomly selected SNP markers identified previously either with a SNP array or by genotyping by sequencing in rice and mungbean, respectively. The genotyping capacity of allele-specific PCR and tetra markers was affected by the sequence context surrounding the SNP; SNPs located in repeated sequences and in GC-rich stretches could not be correctly identified. In contrast, CEL-I digestion of mixed fragments produced from test and reference DNA reliably pinpointed the correct genotypes, yet scoring of the genotypes became complicated when multiple SNPs were present in the PCR fragments. A cost analysis showed that as long the sample number remains small, CEL-I genotyping is more cost-effective than tetra markers. CEL-I genotyping performed better in terms of genotyping accuracy and costs than tetra markers. The method is highly useful for validating SNPs in small to medium size germplasm panels.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 26%
Student > Master 9 26%
Student > Bachelor 3 9%
Student > Postgraduate 2 6%
Other 2 6%
Other 4 12%
Unknown 5 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 53%
Biochemistry, Genetics and Molecular Biology 7 21%
Computer Science 1 3%
Unspecified 1 3%
Chemistry 1 3%
Other 0 0%
Unknown 6 18%

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 30 January 2017.
All research outputs
#6,844,626
of 8,982,225 outputs
Outputs from Hereditas
#139
of 179 outputs
Outputs of similar age
#220,893
of 309,238 outputs
Outputs of similar age from Hereditas
#1
of 2 outputs
Altmetric has tracked 8,982,225 research outputs across all sources so far. This one is in the 13th percentile – i.e., 13% of other outputs scored the same or lower than it.
So far Altmetric has tracked 179 research outputs from this source. They receive a mean Attention Score of 3.0. This one is in the 10th percentile – i.e., 10% of its peers scored the same or lower than it.
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