Journal article
QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data
BIOINFORMATICS, Vol.34(1), pp.163-170
01/01/2018
DOI: 10.1093/bioinformatics/btx402
PMCID: PMC6355096
PMID: 29304222
Abstract
Motivation: Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use.
Results: The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN.
Contact: pskums@gsu.edu or alexz@cs.gsu.edu or rahul@sfsu.eduoryek0@cdc.gov
Supplementary information: Supplementary data are available at Bioinformatics online.
Details
- Title: Subtitle
- QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data
- Creators
- Pavel Skums - Georgia State UniversityAlex Zelikovsky - Georgia State UniversityRahul Singh - San Francisco State UniversityWalker Gussler - Centers for Disease Control and PreventionZoya Dimitrova - Centers for Disease Control and PreventionSergey Knyazev - Georgia State UniversityIgor Mandric - Georgia State UniversitySumathi Ramachandran - Centers for Disease Control and PreventionDavid Campo - Centers for Disease Control and PreventionDeeptanshu Jha - San Francisco State UniversityLeonid Bunimovich - Georgia Institute of TechnologyElizabeth Costenbader - Family Health International 360Connie Sexton - Centers for Disease Control and PreventionSiobhan O'Connor - Centers for Disease Control and PreventionGuo-Liang Xia - Ctr Dis Control & Prevent, Div Viral Hepatitis, Atlanta, GA 30303 USAYury Khudyakov - Centers for Disease Control and Prevention
- Resource Type
- Journal article
- Publication Details
- BIOINFORMATICS, Vol.34(1), pp.163-170
- Publisher
- Oxford Univ Press
- DOI
- 10.1093/bioinformatics/btx402
- PMID
- 29304222
- PMCID
- PMC6355096
- ISSN
- 1367-4803
- eISSN
- 1460-2059
- Number of pages
- 8
- Grant note
- R01EB025022 / NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Biomedical Imaging & Bioengineering (NIBIB) 1564899; 16119110 / NSF; National Science Foundation (NSF) 1615407 / Division of Computing and Communication Foundations; National Science Foundation (NSF); NSF - Directorate for Computer & Information Science & Engineering (CISE) GSU Molecular Basis of Disease Fellowship
- Language
- English
- Date published
- 01/01/2018
- Academic Unit
- Computer Science
- Record Identifier
- 9984446521002771
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