Abstract
With the advent of massively parallel sequencing, oncogenic viruses in tumours can now be detected in an unbiased and comprehensive manner. Additionally, new viruses or strains can be discovered based on sequence similarity with known viruses. Using this approach, the causative agent for Merkel cell carcinoma was identified. Subsequent studies using data from large collections of tumours have confirmed models built during decades of hypothesis-driven and low-throughput research, and a more detailed and comprehensive description of virus–tumour associations have emerged. Notably, large cohorts and high sequencing depth, in combination with newly developed bioinformatical techniques, have made it possible to rule out several suggested virus–tumour associations with a high degree of confidence. In this review we discuss possibilities, limitations and insights gained from using massively parallel sequencing to characterize tumours with viral content, with emphasis on detection of viral sequences and genomic integration events.This article is part of the themed issue ‘Human oncogenic viruses’.
Highlights
Seven known human tumour viruses, discovered using a variety of techniques, are causative agents for a large fraction of human cancers [1]
The most recently discovered human tumour virus, Merkel cell polyomavirus (MCV), responsible for the majority of Merkel cell carcinomas, was identified using a pioneering bioinformatical method, digital transcriptome subtraction of sequences generated by next-generation sequencing (NGS) [4]
The principle was reminiscent of earlier molecular biological techniques for enrichment and sequencing of viral genetic material, which led to the discovery of hepatitis C virus (HCV) and Kaposi’s sarcoma associated herpesvirus (KSHV or human herpesvirus 8 (HHV8)) [5,6]
Summary
Seven known human tumour viruses, discovered using a variety of techniques, are causative agents for a large fraction of human cancers [1]. The larger amounts of data generated by NGS allowed the enrichment process to be performed in silico using bioinformatics, by first removing human sequences followed by unbiased detection of viral traces in the remaining data. Variants of this approach have since been used in many subsequent NGS-based studies. DNA, polyA+ RNA or total RNA human databases subtraction digital removal of human reads viral databases detection alignment to viral sequences viral databases contig assembly allows more divergence, e.g. new viruses integrated virus genomic integration analysis bioinformatical identification of human-viral paired-end reads or chimeric single reads
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