AAVolve: Concatenated long-read deep sequencing enables whole capsid tracking during shuffled AAV library selection
Abstract
Gene therapies using recombinant adeno-associated virus (AAV) vectors have demonstrated considerable clinical success in the treatment of genetic disorders. Improved vectors with favorable tropism profiles, decreased immunogenicity, and enhanced manufacturability are poised to further improve the state of gene therapies. Such vectors can be identified through directed evolution, a process of subjecting a diverse capsid library to a selection pressure to identify individual variants with a desired trait. Currently, libraries that involve changes distributed throughout the AAV capsid coding region, such as DNA family shuffled libraries, are largely characterized using low-throughput Sanger sequencing of individual clones. However, improvements in long-read sequencing technologies have increased their applicability to capsid libraries and evaluation of the selection process. Here, we explore the application of Oxford Nanopore Technologies refined by a concatemeric consensus method for initial library characterization and monitoring selection of a shuffled AAV capsid library. Furthermore, we present AAVolve, a bioinformatic pipeline for processing long-read data from AAV-directed evolution experiments. Our approach allows high-throughput characterization of AAV capsids in a streamlined manner, facilitating deeper insights into library composition through multiple rounds of selection, and generalization through training of machine learning models.
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BibTeX
@article{scott2026aavolve,
title = {{AAVolve: Concatenated long-read deep sequencing enables whole capsid tracking during shuffled AAV library selection}},
author = {Suzanne Scott and Adrian Westhaus and Deborah Nazareth and Marti Cabanes-Creus and Renina Gale Navarro and Deborah Chandra and Erhua Zhu and Aravind Venkateswaran and Ian Alexander and Denis Bauer and Laurence Wilson and Leszek Lisowski and Laurence O.W. Wilson},
journal = {Molecular Therapy - Methods and Clinical Development},
year = {2026},
month = sep,
doi = {10.1016/j.omtm.2024.101351},
url = {https://hal.science/hal-05772052v1},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - AAVolve: Concatenated long-read deep sequencing enables whole capsid tracking during shuffled AAV library selection
AU - Suzanne Scott
AU - Adrian Westhaus
AU - Deborah Nazareth
AU - Marti Cabanes-Creus
AU - Renina Gale Navarro
AU - Deborah Chandra
AU - Erhua Zhu
AU - Aravind Venkateswaran
AU - Ian Alexander
AU - Denis Bauer
AU - Laurence Wilson
AU - Leszek Lisowski
AU - Laurence O.W. Wilson
PY - 2026
DA - 2026/09/30
JO - Molecular Therapy - Methods and Clinical Development
DO - 10.1016/j.omtm.2024.101351
UR - https://hal.science/hal-05772052v1
AB - Gene therapies using recombinant adeno-associated virus (AAV) vectors have demonstrated considerable clinical success in the treatment of genetic disorders. Improved vectors with favorable tropism profiles, decreased immunogenicity, and enhanced manufacturability are poised to further improve the state of gene therapies. Such vectors can be identified through directed evolution, a process of subjecting a diverse capsid library to a selection pressure to identify individual variants with a desired trait. Currently, libraries that involve changes distributed throughout the AAV capsid coding region, such as DNA family shuffled libraries, are largely characterized using low-throughput Sanger sequencing of individual clones. However, improvements in long-read sequencing technologies have increased their applicability to capsid libraries and evaluation of the selection process. Here, we explore the application of Oxford Nanopore Technologies refined by a concatemeric consensus method for initial library characterization and monitoring selection of a shuffled AAV capsid library. Furthermore, we present AAVolve, a bioinformatic pipeline for processing long-read data from AAV-directed evolution experiments. Our approach allows high-throughput characterization of AAV capsids in a streamlined manner, facilitating deeper insights into library composition through multiple rounds of selection, and generalization through training of machine learning models.
SN - 2329-0501
ER -