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Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits

Overview of attention for article published in BMC Research Notes, July 2017
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Title
Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits
Published in
BMC Research Notes, July 2017
DOI 10.1186/s13104-017-2577-x
Pubmed ID
Authors

Anjan Hazra, Nirjhar Dasgupta, Chandan Sengupta, Sauren Das

Abstract

Tea (Camellia sinensis, (L.) Kuntze) is considered as most popular drink across the world and it is widely consumed beverage for its several health-benefit characteristics. These positive traits primarily rely on its regulatory networks of different metabolic pathways. Development of microsatellite markers from the conserved genomic regions are being worthwhile for reviewing the genetic diversity of closely related species or self-pollinated species. Although several SSR markers have been reported, in tea, the trait-specific Simple Sequence Repeat (SSR) markers, leading to be useful in marker assisted breeding technique, are yet to be identified. Micro RNAs are short, non-coding RNA molecules, involved in post transcriptional mode of gene regulation and thus effects on related phenotype. Present study deals with identification of the microsatellite motifs within the reported and predicted miRNA precursors that are effectively followed by designing of primers from SSR flanking regions in order to PCR validation. In addition to the earlier reports, two new miRNAs are predicting here from tea expressed tag sequence database. Furthermore, 18 SSR motifs are found to be in 13 of all 33 predicted miRNAs. Trinucleotide motifs are most abundant among all followed by dinucleotides. Since, miRNA based SSR markers are evidenced to have significant role on genetic fingerprinting study, these outcomes would pave the way in developing novel markers for tagging tea specific agronomic traits as well as substantiating non-conventional breeding program.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 25%
Researcher 2 13%
Student > Master 2 13%
Student > Postgraduate 1 6%
Unspecified 1 6%
Other 0 0%
Unknown 6 38%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 38%
Biochemistry, Genetics and Molecular Biology 2 13%
Unspecified 1 6%
Medicine and Dentistry 1 6%
Unknown 6 38%
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 08 July 2017.
All research outputs
#18,559,907
of 22,986,950 outputs
Outputs from BMC Research Notes
#3,037
of 4,284 outputs
Outputs of similar age
#239,953
of 313,513 outputs
Outputs of similar age from BMC Research Notes
#89
of 136 outputs
Altmetric has tracked 22,986,950 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,284 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.