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A high-throughput pipeline for detecting locus-specific polymorphism in hexaploid wheat (Triticum aestivum L.)

Overview of attention for article published in Plant Methods, August 2015
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Title
A high-throughput pipeline for detecting locus-specific polymorphism in hexaploid wheat (Triticum aestivum L.)
Published in
Plant Methods, August 2015
DOI 10.1186/s13007-015-0082-6
Pubmed ID
Authors

Jian Ma, Jiri Stiller, Zhi Zheng, Ya-Xi Liu, Yuming Wei, You-Liang Zheng, Chunji Liu

Abstract

Bread wheat (Triticum aestivum L., 2n = 6x = 42) is an allohexaploid with a huge genome. Due to the presence of extensive homoeologs and paralogs, generating locus-specific sequences can be challenging, especially when a large number of sequences are required. Traditional methods of generating locus-specific sequences are rather strenuous and time-consuming if large numbers of sequences are to be handled. To improve the efficiency of isolating sequences for targeted loci, a time-saving and high-throughput pipeline integrating orthologous sequence alignment, genomic sequence retrieving, and multiple sequence alignment was developed. This pipeline was successfully employed in retrieving and aligning homoeologous sequences and 83% of the primers designed based on the pipeline successfully amplified fragments from the targeted subgenomes. The high-throughput pipeline developed in this study makes it feasible to efficiently identify locus-specific sequences for large numbers of sequences. It could find applications in all research projects where locus-specific sequences are required. In addition to generating locus-specific markers, the pipeline was also used in our laboratory to identify differentially expressed genes among the three subgenomes of bread wheat. Importantly, the pipeline is not only valuable for research in wheat but should also be applicable to other allopolyploid species.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 41%
Student > Ph. D. Student 3 18%
Professor 1 6%
Other 1 6%
Student > Doctoral Student 1 6%
Other 1 6%
Unknown 3 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 71%
Biochemistry, Genetics and Molecular Biology 2 12%
Unknown 3 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 06 August 2015.
All research outputs
#14,170,999
of 22,818,766 outputs
Outputs from Plant Methods
#704
of 1,081 outputs
Outputs of similar age
#135,020
of 264,230 outputs
Outputs of similar age from Plant Methods
#6
of 8 outputs
Altmetric has tracked 22,818,766 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,081 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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 264,230 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.