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Bioinformatics. 2016 May 1;32(9):1308-15. doi: 10.1093/bioinformatics/btv749. Epub 2015 Dec 31.

SPMM: estimating infection duration of multivariant HIV-1 infections.

Author information

1
Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, 14642, USA.
2
Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, 90089, USA.
3
Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA and.
4
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, 90089, USA.

Abstract

MOTIVATION:

Illustrating how HIV-1 is transmitted and how it evolves in the following weeks is an important step for developing effective vaccination and prevention strategies. It is currently possible through DNA sequencing to account for the diverse array of viral strains within an infected individual. This provides an unprecedented opportunity to pinpoint when each patient was infected and which viruses were transmitted.

RESULTS:

Here we develop a mathematical tool for early HIV-1 evolution within a subject whose infection originates either from a single or multiple viral variants. The shifted Poisson mixture model (SPMM) provides a quantitative guideline for segregating viral lineages, which in turn enables us to assess when a subject was infected. The infection duration estimated by SPMM showed a statistically significant linear relationship with that by Fiebig laboratory staging (P = 0.00059) among 37 acutely infected subjects. Our tool provides a functional approach to understanding early genetic diversity, one of the most important parameters for deciphering HIV-1 transmission and predicting the rate of disease progression.

AVAILABILITY AND IMPLEMENTATION:

SPMM, webserver, is available at http://www.hayounlee.org/web-tools.html.

CONTACT:

hayoun@usc.edu

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26722117
PMCID:
PMC4907399
DOI:
10.1093/bioinformatics/btv749
[Indexed for MEDLINE]
Free PMC Article

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