Long read sequencing reveals sequential complex rearrangements driven by Hepatitis B virus integration

Abstract

Integration of Hepatitis B (HBV) virus into human genome disrupts genetic structures and cellular functions. Here, we conducted multiplatform long read sequencing on two cell lines and five clinical samples of HBV-induced hepatocellular carcinomas (HCC). We resolved two types of complex viral integration induced genome rearrangements and established a Time-phased Integration and Rearrangement Model (TIRM) to depict their formation progress by differentiating inserted HBV copies with HiFi long reads. We showed that the two complex types were initialized from focal replacements and the fragile virus-human junctions triggered subsequent rearrangements. We further revealed that these rearrangements promoted a prevalent loss-of-heterozygosity at chr4q, accounting for 19.5% of HCC samples in ICGC cohort and contributing to immune and metabolic dysfunction. Overall, our long read based analysis reveals a novel sequential rearrangement progress driven by HBV integration, hinting the structural and functional implications on human genomes.

SVDSS: structural variation discovery in hard-to-call genomic regions using sample-specific strings from accurate long reads

“…With the advent of short-read and long-read high-throughput sequencing technologies in the past decade, significant progress has been made in our understanding of the abundance, complexity, and importance of SVs [28,29,30,31]. There are many methods developed for prediction of SVs using whole-genome sequencing (WGS) data produced from different sequencing technologies [32,33,34,35,36,37,38,39,40,41,42].…”

Improved structural variant discovery in hard-to-call regions using sample-specific string detection from accurate long reads

“…With the advent of short-read and long-read high-throughput sequencing technologies in the past decade, significant progress has been made in our understanding of the abundance, complexity, and importance of SVs [28, 29, 30, 31]. There are many methods developed for prediction of SVs using whole-genome sequencing (WGS) data produced from different sequencing technologies [32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 35, 42].…”