Inference of viral transmission history and outbreak investigation using NGS data
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P. Skums, A. Zelikovsky, R. Singh, W. Gussler, Z. Dimitrova, S. Knyazev, I. Mandric, S. Ramachandran, D. Campo, D. Jha, L. Bunimovich, E. Costenbader, C. Sexton, S. O'Connor,G.-l. Xia and Y. Khudyakov, QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data , Bioinformatics, 2018, 34 (1), 163–-170
O. Glebova, S. Knyazev, A. Melnick, A. Artyomenko, Y. Khudyakov, A. Zelikovsky and P. Skums, Inference of genetic relatedness between viral quasispecies from sequencing data BMC Bioinformatics, 2017, 18 (10), 918
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 HIV and HCV, 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.
We propose a framework QUENTIN (QUasispecies Evolution, Network-based Transmission INference), which 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.