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A general method to eliminate laboratory induced recombinants during massive, parallel sequencing of cDNA library

Overview of attention for article published in Virology Journal, April 2015
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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3 tweeters
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1 Facebook page

Citations

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8 Dimensions

Readers on

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27 Mendeley
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Title
A general method to eliminate laboratory induced recombinants during massive, parallel sequencing of cDNA library
Published in
Virology Journal, April 2015
DOI 10.1186/s12985-015-0280-x
Pubmed ID
Authors

Caryll Waugh, Deborah Cromer, Andrew Grimm, Abha Chopra, Simon Mallal, Miles Davenport, Johnson Mak

Abstract

Massive, parallel sequencing is a potent tool for dissecting the regulation of biological processes by revealing the dynamics of the cellular RNA profile under different conditions. Similarly, massive, parallel sequencing can be used to reveal the complexity of viral quasispecies that are often found in the RNA virus infected host. However, the production of cDNA libraries for next-generation sequencing (NGS) necessitates the reverse transcription of RNA into cDNA and the amplification of the cDNA template using PCR, which may introduce artefact in the form of phantom nucleic acids species that can bias the composition and interpretation of original RNA profiles. Using HIV as a model we have characterised the major sources of error during the conversion of viral RNA to cDNA, namely excess RNA template and the RNaseH activity of the polymerase enzyme, reverse transcriptase. In addition we have analysed the effect of PCR cycle on detection of recombinants and assessed the contribution of transfection of highly similar plasmid DNA to the formation of recombinant species during the production of our control viruses. We have identified RNA template concentrations, RNaseH activity of reverse transcriptase, and PCR conditions as key parameters that must be carefully optimised to minimise chimeric artefacts. Using our optimised RT-PCR conditions, in combination with our modified PCR amplification procedure, we have developed a reliable technique for accurate determination of RNA species using NGS technology.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 4%
Italy 1 4%
Unknown 25 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 26%
Student > Ph. D. Student 5 19%
Librarian 2 7%
Student > Postgraduate 2 7%
Student > Master 2 7%
Other 4 15%
Unknown 5 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 26%
Biochemistry, Genetics and Molecular Biology 6 22%
Medicine and Dentistry 4 15%
Immunology and Microbiology 3 11%
Computer Science 1 4%
Other 1 4%
Unknown 5 19%

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 22 April 2015.
All research outputs
#2,236,051
of 5,026,115 outputs
Outputs from Virology Journal
#596
of 1,360 outputs
Outputs of similar age
#71,098
of 153,988 outputs
Outputs of similar age from Virology Journal
#29
of 46 outputs
Altmetric has tracked 5,026,115 research outputs across all sources so far. This one has received more attention than most of these and is in the 54th percentile.
So far Altmetric has tracked 1,360 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 51% 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 153,988 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 51% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.