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CSGM Designer: a platform for designing cross-species intron-spanning genic markers linked with genome information of legumes

Overview of attention for article published in Plant Methods, April 2015
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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 (78th percentile)

Mentioned by

news
1 news outlet

Citations

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4 Dimensions

Readers on

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14 Mendeley
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Title
CSGM Designer: a platform for designing cross-species intron-spanning genic markers linked with genome information of legumes
Published in
Plant Methods, April 2015
DOI 10.1186/s13007-015-0074-6
Pubmed ID
Authors

Jin-Hyun Kim, Chaeyoung Lee, Daejin Hyung, Ye-Jin Jo, Joo-Seok Park, Douglas R Cook, Hong-Kyu Choi

Abstract

Genetic markers are tools that can facilitate molecular breeding, even in species lacking genomic resources. An important class of genetic markers is those based on orthologous genes, because they can guide hypotheses about conserved gene function, a situation that is well documented for a number of agronomic traits. For under-studied species a key bottleneck in gene-based marker development is the need to develop molecular tools (e.g., oligonucleotide primers) that reliably access genes with orthology to the genomes of well-characterized reference species. Here we report an efficient platform for the design of cross-species gene-derived markers in legumes. The automated platform, named CSGM Designer (URL: http://tgil.donga.ac.kr/CSGMdesigner), facilitates rapid and systematic design of cross-species genic markers. The underlying database is composed of genome data from five legume species whose genomes are substantially characterized. Use of CSGM is enhanced by graphical displays of query results, which we describe as "circular viewer" and "search-within-results" functions. CSGM provides a virtual PCR representation (eHT-PCR) that predicts the specificity of each primer pair simultaneously in multiple genomes. CSGM Designer output was experimentally validated for the amplification of orthologous genes using 16 genotypes representing 12 crop and model legume species, distributed among the galegoid and phaseoloid clades. Successful cross-species amplification was obtained for 85.3% of PCR primer combinations. CSGM Designer spans the divide between well-characterized crop and model legume species and their less well-characterized relatives. The outcome is PCR primers that target highly conserved genes for polymorphism discovery, enabling functional inferences and ultimately facilitating trait-associated molecular breeding.

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 7%
Unknown 13 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 29%
Student > Ph. D. Student 4 29%
Student > Doctoral Student 2 14%
Student > Bachelor 2 14%
Student > Master 1 7%
Other 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 71%
Engineering 2 14%
Immunology and Microbiology 1 7%
Computer Science 1 7%

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 18 April 2015.
All research outputs
#3,448,680
of 19,214,062 outputs
Outputs from Plant Methods
#191
of 911 outputs
Outputs of similar age
#47,800
of 239,822 outputs
Outputs of similar age from Plant Methods
#1
of 1 outputs
Altmetric has tracked 19,214,062 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 911 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. 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 239,822 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 78% of its contemporaries.
We're also able to compare this research output to 1 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