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Meta-barcoding in combination with palynological inference is a potent diagnostic marker for honey floral composition

Overview of attention for article published in AMB Express, June 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

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4 tweeters

Citations

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17 Dimensions

Readers on

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47 Mendeley
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Title
Meta-barcoding in combination with palynological inference is a potent diagnostic marker for honey floral composition
Published in
AMB Express, June 2017
DOI 10.1186/s13568-017-0429-7
Pubmed ID
Authors

Rama Chandra Laha, Surajit Mandal, Lalhmanghai Ralte, Laldinfeli Ralte, Nachimuthu Senthil Kumar, Guruswami Gurusubramanian, Ramalingam Satishkumar, Raja Mugasimangalam, Nagesh Aswathnarayana Kuravadi, Laha, Rama Chandra, De Mandal, Surajit, Ralte, Lalhmanghai, Ralte, Laldinfeli, Kumar, Nachimuthu Senthil, Gurusubramanian, Guruswami, Satishkumar, Ramalingam, Mugasimangalam, Raja, Kuravadi, Nagesh Aswathnarayana

Abstract

Identification of floral samples present in honey is important in order to determine the medicinal value, enhance the production of honey as well as to conserve the honey bees. Traditional approaches for studying pollen samples are based on microscopic observation which is laborious, time intensive and requires specialized palynological knowledge. Present study compares two composite honey metagenome collected from 20 samples in Mizoram, Northeast India using three gene loci- rbcL, matK and ITS2 that was sequenced using a next-generation sequencing (NGS) platform (Illumina Miseq). Furthermore, a classical palynology study for all 20 samples was carried out to evaluate the NGS approach. NGS based approach and pollen microscopic studies were able to detect the most abundant floral components of honey. We investigated the plants that were frequently used by honey bees by examining the results obtained from both the techniques. Microscopic examination of pollens detected plants with a broad taxonomic range covering 26 families. NGS based multigene approach revealed diverse plant species, which was higher than in any other previously reported techniques using a single locus. Frequently found herbaceous species were from the family Poaceae, Myrtaceae, Fabaceae and Asteraceae. The future NGS based approach using multi-loci target, with the help of an improved and robust plant database, can be a potential replacement technique for tedious microscopic studies to identify the polleniferous plants.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 40%
Researcher 7 15%
Student > Bachelor 3 6%
Student > Master 3 6%
Professor 1 2%
Other 2 4%
Unknown 12 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 38%
Biochemistry, Genetics and Molecular Biology 7 15%
Environmental Science 3 6%
Chemical Engineering 1 2%
Computer Science 1 2%
Other 3 6%
Unknown 14 30%

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 28 June 2017.
All research outputs
#6,396,902
of 11,426,158 outputs
Outputs from AMB Express
#148
of 666 outputs
Outputs of similar age
#120,396
of 263,806 outputs
Outputs of similar age from AMB Express
#17
of 72 outputs
Altmetric has tracked 11,426,158 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 666 research outputs from this source. They receive a mean Attention Score of 2.2. This one has done well, scoring higher than 76% 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 263,806 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.