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An SNP-based saturated genetic map and QTL analysis of fruit-related traits in cucumber using specific-length amplified fragment (SLAF) sequencing

Overview of attention for article published in BMC Genomics, December 2014
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Title
An SNP-based saturated genetic map and QTL analysis of fruit-related traits in cucumber using specific-length amplified fragment (SLAF) sequencing
Published in
BMC Genomics, December 2014
DOI 10.1186/1471-2164-15-1158
Pubmed ID
Authors

Qingzhen Wei, Yunzhu Wang, Xiaodong Qin, Yunxia Zhang, Zhentao Zhang, Jing Wang, Ji Li, Qunfeng Lou, Jinfeng Chen

Abstract

Cucumber, Cucumis sativus L., is an economically important vegetable crop which is processed or consumed fresh worldwide. However, the narrow genetic base in cucumber makes it difficult for constructing high-density genetic maps. The development of massively parallel genotyping methods and next-generation sequencing (NGS) technologies provides an excellent opportunity for developing single nucleotide polymorphisms (SNPs) for linkage map construction and QTL analysis of horticultural traits. Specific-length amplified fragment sequencing (SLAF-seq) is a recent marker development technology that allows large-scale SNP discovery and genotyping at a reasonable cost. In this study, we constructed a high-density SNP map for cucumber using SLAF-seq and detected fruit-related QTLs.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 63 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 20%
Student > Master 11 17%
Researcher 9 14%
Student > Bachelor 4 6%
Student > Postgraduate 4 6%
Other 5 8%
Unknown 18 28%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 48%
Biochemistry, Genetics and Molecular Biology 8 13%
Engineering 5 8%
Medicine and Dentistry 2 3%
Business, Management and Accounting 1 2%
Other 1 2%
Unknown 16 25%
Attention Score in Context

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 25 December 2014.
All research outputs
#15,313,289
of 22,775,504 outputs
Outputs from BMC Genomics
#6,684
of 10,642 outputs
Outputs of similar age
#208,782
of 353,034 outputs
Outputs of similar age from BMC Genomics
#156
of 250 outputs
Altmetric has tracked 22,775,504 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,642 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 29th percentile – i.e., 29% 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 353,034 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 250 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.