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Towards Better Precision Medicine: PacBio Single-Molecule Long Reads Resolve the Interpretation of HIV Drug Resistant Mutation Profiles at Explicit Quasispecies (Haplotype) Level

Overview of attention for article published in Journal of data mining in genomics proteomics, January 2016
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  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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8 X users

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Title
Towards Better Precision Medicine: PacBio Single-Molecule Long Reads Resolve the Interpretation of HIV Drug Resistant Mutation Profiles at Explicit Quasispecies (Haplotype) Level
Published in
Journal of data mining in genomics proteomics, January 2016
DOI 10.4172/2153-0602.1000182
Pubmed ID
Authors

Da Wei Huang, Castle Raley, Min Kang Jiang, Xin Zheng, Dun Liang, M Tauseef Rehman, Helene C Highbarger, Xiaoli Jiao, Brad Sherman, Liang Ma, Xiaofeng Chen, Thomas Skelly, Jennifer Troyer, Robert Stephens, Tomozumi Imamichi, Alice Pau, Richard A Lempicki, Bao Tran, Dwight Nissley, H Clifford Lane, Robin L Dewar

Abstract

Development of HIV-1 drug resistance mutations (HDRMs) is one of the major reasons for the clinical failure of antiretroviral therapy. Treatment success rates can be improved by applying personalized anti-HIV regimens based on a patient's HDRM profile. However, the sensitivity and specificity of the HDRM profile is limited by the methods used for detection. Sanger-based sequencing technology has traditionally been used for determining HDRM profiles at the single nucleotide variant (SNV) level, but with a sensitivity of only ≥ 20% in the HIV population of a patient. Next Generation Sequencing (NGS) technologies offer greater detection sensitivity (~ 1%) and larger scope (hundreds of samples per run). However, NGS technologies produce reads that are too short to enable the detection of the physical linkages of individual SNVs across the haplotype of each HIV strain present. In this article, we demonstrate that the single-molecule long reads generated using the Third Generation Sequencer (TGS), PacBio RS II, along with the appropriate bioinformatics analysis method, can resolve the HDRM profile at a more advanced quasispecies level. The case studies on patients' HIV samples showed that the quasispecies view produced using the PacBio method offered greater detection sensitivity and was more comprehensive for understanding HDRM situations, which is complement to both Sanger and NGS technologies. In conclusion, the PacBio method, providing a promising new quasispecies level of HDRM profiling, may effect an important change in the field of HIV drug resistance research.

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

Geographical breakdown

Country Count As %
France 1 2%
Unknown 53 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 15%
Researcher 7 13%
Student > Bachelor 7 13%
Student > Master 7 13%
Other 6 11%
Other 11 20%
Unknown 8 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 24%
Agricultural and Biological Sciences 9 17%
Medicine and Dentistry 7 13%
Immunology and Microbiology 7 13%
Engineering 4 7%
Other 5 9%
Unknown 9 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 01 December 2019.
All research outputs
#6,753,656
of 25,371,288 outputs
Outputs from Journal of data mining in genomics proteomics
#8
of 83 outputs
Outputs of similar age
#97,693
of 399,662 outputs
Outputs of similar age from Journal of data mining in genomics proteomics
#3
of 17 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 83 research outputs from this source. They receive a mean Attention Score of 2.6. This one has done well, scoring higher than 89% 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 399,662 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 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.