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From genomics to functional markers in the era of next-generation sequencing

Overview of attention for article published in Biotechnology Techniques, October 2013
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Title
From genomics to functional markers in the era of next-generation sequencing
Published in
Biotechnology Techniques, October 2013
DOI 10.1007/s10529-013-1377-1
Pubmed ID
Authors

R. K. Salgotra, B. B. Gupta, C. N. Stewart

Abstract

The availability of complete genome sequences, along with other genomic resources for Arabidopsis, rice, pigeon pea, soybean and other crops, has revolutionized our understanding of the genetic make-up of plants. Next-generation DNA sequencing (NGS) has facilitated single nucleotide polymorphism discovery in plants. Functionally-characterized sequences can be identified and functional markers (FMs) for important traits can be developed at an ever-increasing ease. FMs are derived from sequence polymorphisms found in allelic variants of a functional gene. Linkage disequilibrium-based association mapping and homologous recombinants have been developed for identification of "perfect" markers for their use in crop improvement practices. Compared with many other molecular markers, FMs derived from the functionally characterized sequence genes using NGS techniques and their use provide opportunities to develop high-yielding plant genotypes resistant to various stresses at a fast pace.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
Chile 1 1%
Italy 1 1%
Malaysia 1 1%
Belgium 1 1%
China 1 1%
Japan 1 1%
United States 1 1%
Unknown 81 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 30%
Student > Ph. D. Student 15 17%
Student > Master 12 13%
Student > Doctoral Student 10 11%
Student > Bachelor 6 7%
Other 8 9%
Unknown 12 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 58 64%
Biochemistry, Genetics and Molecular Biology 6 7%
Medicine and Dentistry 3 3%
Computer Science 2 2%
Environmental Science 1 1%
Other 4 4%
Unknown 16 18%
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 17 October 2013.
All research outputs
#20,656,161
of 25,373,627 outputs
Outputs from Biotechnology Techniques
#2,353
of 2,762 outputs
Outputs of similar age
#168,837
of 223,620 outputs
Outputs of similar age from Biotechnology Techniques
#23
of 28 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,762 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 9th percentile – i.e., 9% 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 223,620 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.