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Inferring viral quasispecies spectra from 454 pyrosequencing reads

Overview of attention for article published in BMC Bioinformatics, July 2011
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1 tweeter

Citations

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90 Dimensions

Readers on

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153 Mendeley
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3 CiteULike
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Title
Inferring viral quasispecies spectra from 454 pyrosequencing reads
Published in
BMC Bioinformatics, July 2011
DOI 10.1186/1471-2105-12-s6-s1
Pubmed ID
Authors

Irina Astrovskaya, Bassam Tork, Serghei Mangul, Kelly Westbrooks, Ion Măndoiu, Peter Balfe, Alex Zelikovsky

Abstract

RNA viruses infecting a host usually exist as a set of closely related sequences, referred to as quasispecies. The genomic diversity of viral quasispecies is a subject of great interest, particularly for chronic infections, since it can lead to resistance to existing therapies. High-throughput sequencing is a promising approach to characterizing viral diversity, but unfortunately standard assembly software was originally designed for single genome assembly and cannot be used to simultaneously assemble and estimate the abundance of multiple closely related quasispecies sequences.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 7 5%
United States 4 3%
Sweden 2 1%
Switzerland 2 1%
France 2 1%
Canada 2 1%
United Kingdom 1 <1%
Netherlands 1 <1%
Belgium 1 <1%
Other 3 2%
Unknown 128 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 31%
Researcher 35 23%
Student > Master 25 16%
Student > Bachelor 11 7%
Professor > Associate Professor 8 5%
Other 22 14%
Unknown 4 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 89 58%
Biochemistry, Genetics and Molecular Biology 22 14%
Computer Science 19 12%
Medicine and Dentistry 6 4%
Engineering 4 3%
Other 9 6%
Unknown 4 3%

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 27 November 2013.
All research outputs
#13,844,820
of 17,351,915 outputs
Outputs from BMC Bioinformatics
#5,231
of 6,150 outputs
Outputs of similar age
#194,116
of 271,781 outputs
Outputs of similar age from BMC Bioinformatics
#373
of 424 outputs
Altmetric has tracked 17,351,915 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 6,150 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
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We're also able to compare this research output to 424 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.