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Highly Sensitive and Specific Detection of Rare Variants in Mixed Viral Populations from Massively Parallel Sequence Data

Overview of attention for article published in PLoS Computational Biology, March 2012
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186 Mendeley
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5 CiteULike
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Title
Highly Sensitive and Specific Detection of Rare Variants in Mixed Viral Populations from Massively Parallel Sequence Data
Published in
PLoS Computational Biology, March 2012
DOI 10.1371/journal.pcbi.1002417
Pubmed ID
Authors

Alexander R. Macalalad, Michael C. Zody, Patrick Charlebois, Niall J. Lennon, Ruchi M. Newman, Christine M. Malboeuf, Elizabeth M. Ryan, Christian L. Boutwell, Karen A. Power, Doug E. Brackney, Kendra N. Pesko, Joshua Z. Levin, Gregory D. Ebel, Todd M. Allen, Bruce W. Birren, Matthew R. Henn

Abstract

Viruses diversify over time within hosts, often undercutting the effectiveness of host defenses and therapeutic interventions. To design successful vaccines and therapeutics, it is critical to better understand viral diversification, including comprehensively characterizing the genetic variants in viral intra-host populations and modeling changes from transmission through the course of infection. Massively parallel sequencing technologies can overcome the cost constraints of older sequencing methods and obtain the high sequence coverage needed to detect rare genetic variants (< 1%) within an infected host, and to assay variants without prior knowledge. Critical to interpreting deep sequence data sets is the ability to distinguish biological variants from process errors with high sensitivity and specificity. To address this challenge, we describe V-Phaser, an algorithm able to recognize rare biological variants in mixed populations. V-Phaser uses covariation (i.e. phasing) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity. Overall, V-Phaser achieved > 97% sensitivity and > 97% specificity on control read sets. On data derived from a patient after four years of HIV-1 infection, V-Phaser detected 2,015 variants across the -10 kb genome, including 603 rare variants (< 1% frequency) detected only using phase information. V-Phaser identified variants at frequencies down to 0.2%, comparable to the detection threshold of allele-specific PCR, a method that requires prior knowledge of the variants. The high sensitivity and specificity of V-Phaser enables identifying and tracking changes in low frequency variants in mixed populations such as RNA viruses.

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X Demographics

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 186 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
United Kingdom 3 2%
France 2 1%
Brazil 2 1%
Kenya 1 <1%
Sweden 1 <1%
Colombia 1 <1%
Singapore 1 <1%
Switzerland 1 <1%
Other 2 1%
Unknown 168 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 52 28%
Student > Ph. D. Student 36 19%
Professor > Associate Professor 21 11%
Student > Master 19 10%
Professor 12 6%
Other 29 16%
Unknown 17 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 86 46%
Biochemistry, Genetics and Molecular Biology 24 13%
Computer Science 17 9%
Medicine and Dentistry 10 5%
Immunology and Microbiology 9 5%
Other 23 12%
Unknown 17 9%
Attention Score in Context

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 21 August 2012.
All research outputs
#15,739,529
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#6,754
of 8,960 outputs
Outputs of similar age
#101,369
of 169,061 outputs
Outputs of similar age from PLoS Computational Biology
#67
of 111 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 22nd percentile – i.e., 22% 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 169,061 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.