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In silicosingle strand melting curve: a new approach to identify nucleic acid polymorphisms in Totiviridae

Overview of attention for article published in BMC Bioinformatics, July 2014
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2 X users

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34 Mendeley
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Title
In silicosingle strand melting curve: a new approach to identify nucleic acid polymorphisms in Totiviridae
Published in
BMC Bioinformatics, July 2014
DOI 10.1186/1471-2105-15-243
Pubmed ID
Authors

Raffael AC Oliveira, Ricardo VM Almeida, Márcia DA Dantas, Felipe N Castro, João Paulo MS Lima, Daniel CF Lanza

Abstract

The PCR technique and its variations have been increasingly used in the clinical laboratory and recent advances in this field generated new higher resolution techniques based on nucleic acid denaturation dynamics. The principle of these new molecular tools is based on the comparison of melting profiles, after denaturation of a DNA double strand. Until now, the secondary structure of single-stranded nucleic acids has not been exploited to develop identification systems based on PCR. To test the potential of single-strand RNA denaturation as a new alternative to detect specific nucleic acid variations, sequences from viruses of the Totiviridae family were compared using a new in silico melting curve approach. This family comprises double-stranded RNA virus, with a genome constituted by two ORFs, ORF1 and ORF2, which encodes the capsid/RNA binding proteins and an RNA-dependent RNA polymerase (RdRp), respectively.

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The data shown below were collected from the profiles of 2 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 6%
Unknown 32 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 18%
Researcher 6 18%
Student > Master 6 18%
Student > Bachelor 4 12%
Student > Postgraduate 3 9%
Other 5 15%
Unknown 4 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 38%
Biochemistry, Genetics and Molecular Biology 10 29%
Computer Science 3 9%
Medicine and Dentistry 1 3%
Unknown 7 21%
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 July 2014.
All research outputs
#14,720,444
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#4,813
of 7,400 outputs
Outputs of similar age
#120,125
of 228,528 outputs
Outputs of similar age from BMC Bioinformatics
#84
of 132 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 30th percentile – i.e., 30% 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 228,528 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
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 is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.