AVROBIO, Inc., Cambridge, MA, USA.
Infection, Immunity and Inflammation Department, Great Ormond Street Institute of Child Health, University College London, London, UK.
BMC Bioinformatics. 2023 Jul 18;24(1):286. doi: 10.1186/s12859-023-05390-1.
Integration site (IS) analysis is a fundamental analytical platform for evaluating the safety and efficacy of viral vector based preclinical and clinical Gene Therapy (GT). A handful of groups have developed standardized bioinformatics pipelines to process IS sequencing data, to generate reports, and/or to perform comparative studies across different GT trials. Keeping up with the technological advances in the field of IS analysis, different computational pipelines have been published over the past decade. These pipelines focus on identifying IS from single-read sequencing or paired-end sequencing data either using read-based or using sonication fragment-based methods, but there is a lack of a bioinformatics tool that automatically includes unique molecular identifiers (UMI) for IS abundance estimations and allows comparing multiple quantification methods in one integrated pipeline.
Here we present IS-Seq a bioinformatics pipeline that can process data from paired-end sequencing of both old restriction sites-based IS collection methods and new sonication-based IS retrieval systems while allowing the selection of different abundance estimation methods, including read-based, Fragment-based and UMI-based systems.
We validated the performance of IS-Seq by testing it against the most popular analytical workflow available in the literature (INSPIIRED) and using different scenarios. Lastly, by performing extensive simulation studies and a comprehensive wet-lab assessment of our IS-Seq pipeline we could show that in clinically relevant scenarios, UMI quantification provides better accuracy than the currently most widely used sonication fragment counts as a method for IS abundance estimation.
整合位点（IS）分析是评估病毒载体基础的临床前和临床基因治疗（GT）安全性和有效性的基本分析平台。有少数几个小组已经开发了标准化的生物信息学管道来处理 IS 测序数据，以生成报告和/或在不同的 GT 试验中进行比较研究。为了跟上 IS 分析领域的技术进步，过去十年中已经发表了不同的计算管道。这些管道专注于使用基于读取或基于超声片段的方法从单读测序或配对末端测序数据中识别 IS，但缺乏一种生物信息学工具，该工具可自动包含独特的分子标识符（UMI）以进行 IS 丰度估计，并允许在一个集成管道中比较多种定量方法。
在这里，我们提出了 IS-Seq 生物信息学管道，该管道可以处理基于旧限制性内切酶位点的 IS 收集方法和新超声片段检索系统的配对末端测序数据，同时允许选择不同的丰度估计方法，包括基于读取、基于片段和基于 UMI 的系统。
我们通过将其与文献中最流行的分析工作流程（INSPIIRED）进行测试，并使用不同的场景来验证 IS-Seq 的性能。最后，通过进行广泛的模拟研究和对我们的 IS-Seq 管道的全面湿实验室评估，我们可以证明在临床相关场景中，UMI 定量比目前最广泛使用的超声片段计数作为 IS 丰度估计方法提供了更好的准确性。
