Inference of Genetic Relatedness Between Viral Quasispecies from Sequencing Data

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2017 Dec 16

PMID 29244009

Citations 14

Authors

Olga Glebova

Sergey Knyazev

Andrew Melnyk

Alexander Artyomenko

Yury Khudyakov

Alex Zelikovsky

Pavel Skums

Affiliations

Soon will be listed here.

Abstract

Background: RNA viruses such as HCV and HIV mutate at extremely high rates, and as a result, they exist in infected hosts as populations of genetically related variants. Recent advances in sequencing technologies make possible to identify such populations at great depth. In particular, these technologies provide new opportunities for inference of relatedness between viral samples, identification of transmission clusters and sources of infection, which are crucial tasks for viral outbreaks investigations.

Results: We present (i) an evolutionary simulation algorithm Viral Outbreak InferenCE (VOICE) inferring genetic relatedness, (ii) an algorithm MinDistB detecting possible transmission using minimal distances between intra-host viral populations and sizes of their relative borders, and (iii) a non-parametric recursive clustering algorithm Relatedness Depth (ReD) analyzing clusters' structure to infer possible transmissions and their directions. All proposed algorithms were validated using real sequencing data from HCV outbreaks.

Conclusions: All algorithms are applicable to the analysis of outbreaks of highly heterogeneous RNA viruses. Our experimental validation shows that they can successfully identify genetic relatedness between viral populations, as well as infer transmission clusters and outbreak sources.

Citing Articles

A general and biomedical perspective of viral quasispecies.

Domingo E, Martinez-Gonzalez B, Somovilla P, Garcia-Crespo C, Soria M, de Avila A RNA. 2024; 31(3):429-443.

PMID: 39689947 PMC: 11874995. DOI: 10.1261/rna.080280.124.

Reconstruction of Viral Variants via Monte Carlo Clustering.

Juyal A, Hosseini R, Novikov D, Grinshpon M, Zelikovsky A J Comput Biol. 2023; 30(9):1009-1018.

PMID: 37695837 PMC: 10518690. DOI: 10.1089/cmb.2023.0154.

SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework.

Skums P, Mohebbi F, Tsyvina V, Baykal P, Nemira A, Ramachandran S Cell Syst. 2022; 13(10):844-856.e4.

PMID: 36265470 PMC: 9590096. DOI: 10.1016/j.cels.2022.07.005.

Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review.

Duault H, Durand B, Canini L Pathogens. 2022; 11(2).

PMID: 35215195 PMC: 8875843. DOI: 10.3390/pathogens11020252.

Scale-Free Spanning Trees and Their Application in Genomic Epidemiology.

Orlovich Y, Kukharenko K, Kaibel V, Skums P J Comput Biol. 2021; 28(10):945-960.

PMID: 34491104 PMC: 8670573. DOI: 10.1089/cmb.2020.0500.

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