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Related Experiment Video

Updated: May 21, 2026

Isolation of Adeno-Associated Viral Vectors Through a Single-Step and Semi-Automated Heparin Affinity Chromatography Protocol
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Isolation of Adeno-Associated Viral Vectors Through a Single-Step and Semi-Automated Heparin Affinity Chromatography Protocol

Published on: April 5, 2024

RAAVioli: A comprehensive approach to characterizing AAV vector integrations and rearrangements.

Carlo Cipriani1,2, Laura Rudilosso2, Dhwanil A Dalwadi3

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.

Molecular Therapy. Advances
|May 20, 2026
PubMed
Summary

We validated RAAVioli, a computational tool for analyzing adeno-associated virus (AAV) integration sites and rearrangements. RAAVioli accurately identifies AAV-genome junctions and complex vector structures, proving superior for gene therapy safety assessments.

Keywords:
AAVbioinformaticsgene therapyintegrationslong- and short read sequencingpipeline

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Engineering and Evolution of Synthetic Adeno-Associated Virus (AAV) Gene Therapy Vectors via DNA Family Shuffling

Published on: April 2, 2012

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Gene Therapy

Background:

  • Adeno-associated virus (AAV) vectors are crucial for gene therapy, but understanding their integration into host genomes is vital for safety and efficacy.
  • Accurate analysis of AAV integration sites (ISs) and vector rearrangements is essential for preclinical and clinical gene therapy studies.

Purpose of the Study:

  • To validate RAAVioli, a computational method for comprehensive analysis of AAV vector integration sites and rearrangements.
  • To assess RAAVioli's performance across different sequencing platforms (long- and short-read) and experimental models.

Main Methods:

  • In silico benchmarking of RAAVioli against existing tools.
  • In vivo validation using a xenogeneic human hepatocyte model transduced with recombinant AAV (rAAV).
  • Analysis of AAV-genome junctions, vector rearrangements, microhomologies, indels, and complex structures like concatemers.

Main Results:

  • RAAVioli accurately identifies AAV-genome junctions and reconstructs complex vector rearrangements.
  • Short-read sequencing demonstrated higher throughput, identifying more ISs and enabling precise clonal abundance quantification compared to long-read sequencing.
  • RAAVioli exhibited higher sensitivity and accuracy than existing tools in benchmarking analyses.

Conclusions:

  • RAAVioli is a robust, versatile, and scalable computational resource for analyzing AAV integration sites and vector rearrangements.
  • The tool is valuable for evaluating gene therapy safety, characterizing AAV vectors, and assessing vector quality.
  • RAAVioli's capabilities support advancements in AAV-based gene therapy and genome-editing research.