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High-throughput sequencing (HTS) for the analysis of viral populations

AuthorsPérez-Losada, Marcos; Arenas, Miguel ; Galán, Juan Carlos; Bracho, María Alma; Hillung, Julia ; García-González, Neri; González-Candelas, Fernando
KeywordsPopulation genomics
Complete genome sequences
Clinical virology
Transmission clusters
Molecular evolution
Issue Date2020
CitationInfection, Genetics and Evolution 80: 104208 (2020)
AbstractThe development of High-Throughput Sequencing (HTS) technologies is having a major impact on the genomic analysis of viral populations. Current HTS platforms can capture nucleic acid variation across millions of genes for both selected amplicons and full viral genomes. HTS has already facilitated the discovery of new viruses, hinted new taxonomic classifications and provided a deeper and broader understanding of their diversity, population and genetic structure. Hence, HTS has already replaced standard Sanger sequencing in basic and applied research fields, but the next step is its implementation as a routine technology for the analysis of viruses in clinical settings. The most likely application of this implementation will be the analysis of viral genomics, because the huge population sizes, high mutation rates and very fast replacement of viral populations have demonstrated the limited information obtained with Sanger technology. In this review, we describe new technologies and provide guidelines for the high-throughput sequencing and genetic and evolutionary analyses of viral populations and metaviromes, including software applications. With the development of new HTS technologies, new and refurbished molecular and bioinformatic tools are also constantly being developed to process and integrate HTS data. These allow assembling viral genomes and inferring viral population diversity and dynamics. Finally, we also present several applications of these approaches to the analysis of viral clinical samples including transmission clusters and outbreak characterization.
Publisher version (URL)https://doi.org/10.1016/j.meegid.2020.104208
Appears in Collections:(I2SysBio) Artículos
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