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An internet-based bioinformatics toolkit for plant biosecurity diagnosis and surveillance of viruses and viroids

Overview of attention for article published in BMC Bioinformatics, January 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

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8 X users
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1 Facebook page

Citations

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47 Dimensions

Readers on

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81 Mendeley
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3 CiteULike
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Title
An internet-based bioinformatics toolkit for plant biosecurity diagnosis and surveillance of viruses and viroids
Published in
BMC Bioinformatics, January 2017
DOI 10.1186/s12859-016-1428-4
Pubmed ID
Authors

Roberto A. Barrero, Kathryn R. Napier, James Cunnington, Lia Liefting, Sandi Keenan, Rebekah A. Frampton, Tamas Szabo, Simon Bulman, Adam Hunter, Lisa Ward, Mark Whattam, Matthew I. Bellgard

Abstract

Detection and preventing entry of exotic viruses and viroids at the border is critical for protecting plant industries trade worldwide. Existing post entry quarantine screening protocols rely on time-consuming biological indicators and/or molecular assays that require knowledge of infecting viral pathogens. Plants have developed the ability to recognise and respond to viral infections through Dicer-like enzymes that cleave viral sequences into specific small RNA products. Many studies reported the use of a broad range of small RNAs encompassing the product sizes of several Dicer enzymes involved in distinct biological pathways. Here we optimise the assembly of viral sequences by using specific small RNA subsets. We sequenced the small RNA fractions of 21 plants held at quarantine glasshouse facilities in Australia and New Zealand. Benchmarking of several de novo assembler tools yielded SPAdes using a kmer of 19 to produce the best assembly outcomes. We also found that de novo assembly using 21-25 nt small RNAs can result in chimeric assemblies of viral sequences and plant host sequences. Such non-specific assemblies can be resolved by using 21-22 nt or 24 nt small RNAs subsets. Among the 21 selected samples, we identified contigs with sequence similarity to 18 viruses and 3 viroids in 13 samples. Most of the viruses were assembled using only 21-22 nt long virus-derived siRNAs (viRNAs), except for one Citrus endogenous pararetrovirus that was more efficiently assembled using 24 nt long viRNAs. All three viroids found in this study were fully assembled using either 21-22 nt or 24 nt viRNAs. Optimised analysis workflows were customised within the Yabi web-based analytical environment. We present a fully automated viral surveillance and diagnosis web-based bioinformatics toolkit that provides a flexible, user-friendly, robust and scalable interface for the discovery and diagnosis of viral pathogens. We have implemented an automated viral surveillance and diagnosis (VSD) bioinformatics toolkit that produces improved viruses and viroid sequence assemblies. The VSD toolkit provides several optimised and reusable workflows applicable to distinct viral pathogens. We envisage that this resource will facilitate the surveillance and diagnosis viral pathogens in plants, insects and invertebrates.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 1%
South Africa 1 1%
Unknown 79 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 23%
Researcher 17 21%
Student > Master 9 11%
Professor > Associate Professor 4 5%
Student > Doctoral Student 4 5%
Other 17 21%
Unknown 11 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 46%
Biochemistry, Genetics and Molecular Biology 12 15%
Computer Science 6 7%
Engineering 3 4%
Immunology and Microbiology 2 2%
Other 5 6%
Unknown 16 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 21 August 2017.
All research outputs
#5,534,245
of 22,940,083 outputs
Outputs from BMC Bioinformatics
#1,990
of 7,307 outputs
Outputs of similar age
#102,440
of 422,106 outputs
Outputs of similar age from BMC Bioinformatics
#32
of 132 outputs
Altmetric has tracked 22,940,083 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,307 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 72% 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 422,106 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.