Structural Analysis of Recombinant AAV Vector Genomes at Single-Molecule Resolution

Abstract

Recombinant adeno-associated virus vectors are essential tools for in vivo gene therapy, yet heterogeneity in their packaged genomes remains an important safety consideration. To systematically evaluate this heterogeneity, we developed a long-read, read-level analysis pipeline that directly classifies individual AAV genomes and their structural variants from PacBio sequencing data. The workflow combines two components: a tiling step that aligns each read to reference sequences to generate positional patterns, and a parsing step that applies a formal grammar to categorize reads into five structural classes: expected, truncated, snapback, truncated snapback, and others. Each molecule is annotated with strand orientation, breakpoint coordinates, and structural arrangement, enabling precise classification of genome heterogeneity at single-vector resolution. Applied to both single-stranded and self-complementary vector genome preparations, the pipeline achieved high classification accuracy and revealed distinct patterns of genome structure between different vector constructs. In both cases, the majority of genomes were classified as expected full-length species, consistent with the dominant full peaks observed by orthogonal methods. For snapback genomes, breakpoints frequently clustered at discrete sites, with some coinciding with regions predicted to form stable secondary structures and others occurring in less structured regions. This distribution suggests contributions from both sequence-driven folding and additional replication- or processing-related mechanisms. Together, these read-level insights highlight sequence and structural features that shape AAV genome heterogeneity. Importantly, the pipeline demonstrated strong performance in structural classification, maintaining high accuracy even in the presence of sequencing error profiles such as homopolymer-associated indels (insertion or deletion). By integrating structural classification, sequence context, and secondary-structure predictions, our pipeline provides a comprehensive framework for evaluating recombinant adeno-associated virus genome diversity. This approach not only improves resolution of vector genome architecture but also offers actionable insights to guide vector design and production processes for safer and more efficacious recombinant adeno-associated virus therapeutics.