Chronic hepatitis B (CHB) is prevalent worldwide. The infectious agent, hepatitis B virus (HBV) replicates via an RNA intermediate and is error-prone, leading to rapid generation of closely related but not identical viral variants, including those that can escape host immune responses and antiviral treatments. The complexity of CHB can be further enhanced by the presence of HBV variants with large deletions in the genome, generated via splicing (spHBV). Although spHBV variants are incapable of autonomous replication, their replication is rescued by wild-type HBV. SpHBV variants have been shown to enhance wild-type virus replication, and their prevalence increases with liver disease progression. Single-molecule deep sequencing was performed on whole HBV genomes extracted from longitudinal samples of a post-liver transplant CHB subject, collected over a 15-year period that included the liver explant. By employing novel bioinformatics methods, this analysis showed a complex dynamics of the viral population across a period of changing treatment regimens. The spHBV detected in the liver explant remained present post-transplantation, along with emergence of a highly diverse novel spHBV population as well as variants with multiple deletions in the preS genes. The identification of novel mutations outside the HBV reverse transcriptase gene that co-occur with known drug resistant mutations, highlight the relevance of using full genome deep sequencing and support the hypothesis that drug resistance involves interactions across the full-length HBV genome.Single-molecule sequencing allowed characterising, in unprecedented detail, the evolution of HBV populations and offered unique insights into the dynamics of defective and spHBV variants following liver transplantation and complex treatment regimes. This analysis also showed rapid adaptation of HBV populations to treatment regimens with evolving drug resistance phenotypes and evidence of purifying selection across the whole genome. Finally, the new open source bioinformatics tools are freely available, with the capacity to easily identify potential spliced variants from deep sequencing data. Copyright © 2016, American Society for Microbiology. All Rights Reserved.