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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

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IS-Seq: a bioinformatics pipeline for integration sites analysis with comprehensive abundance quantification methods.

Aimin Yan¹, Cristina Baricordi¹, Quoc Nguyen¹

¹AVROBIO, Inc., Cambridge, MA, USA.

BMC Bioinformatics

|July 18, 2023

Summary

Integration site (IS) analysis is crucial for gene therapy (GT) safety. IS-Seq is a new bioinformatics pipeline that improves IS abundance estimation using unique molecular identifiers (UMIs) for greater accuracy in GT studies.

Keywords:

Abundance estimation Fragment-length based IS-Seq Integration site analysis Read-based UMI-based

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Area of Science:

Bioinformatics
Gene Therapy
Genomics

Background:

Integration site (IS) analysis is essential for evaluating gene therapy (GT) safety and efficacy in preclinical and clinical studies.
Existing bioinformatics pipelines for IS analysis have limitations, particularly in incorporating unique molecular identifiers (UMIs) for accurate abundance estimation.
There is a need for integrated pipelines that can handle diverse IS retrieval methods and offer multiple quantification options.

Purpose of the Study:

To develop and present IS-Seq, a novel bioinformatics pipeline for comprehensive IS analysis.
To enable processing of data from both restriction site-based and sonication-based IS retrieval methods.
To provide and compare various abundance estimation methods, including UMI-based quantification.

Main Methods:

IS-Seq processes paired-end sequencing data from various IS retrieval methods.
The pipeline supports multiple abundance estimation techniques: read-based, fragment-based, and UMI-based.
Performance was validated against the INSPIIRED workflow and through simulation studies and wet-lab assessments.

Main Results:

IS-Seq successfully processes data from diverse IS collection methods.
The pipeline integrates read-based, fragment-based, and UMI-based abundance estimation.
UMI quantification demonstrated superior accuracy compared to sonication fragment counts in clinically relevant scenarios.

Conclusions:

IS-Seq offers a versatile and integrated platform for IS analysis in gene therapy research.
Unique molecular identifier (UMI) quantification provides more accurate IS abundance estimations than traditional sonication fragment counts.
The developed pipeline aids in the safety and efficacy assessment of gene therapy applications.