Quality assessment of long read data in multisample lrRNA-seq experiments using SQANTI-reads.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Accurate transcript model analysis is crucial for understanding gene expression.
• Long-read RNA sequencing (RNA-seq) offers comprehensive transcriptome insights.
• Quality control is essential for reliable RNA-seq data analysis.

Purpose of the Study:

• To develop a read-level quality control (QC) framework for replicated long-read RNA-seq experiments.
• To introduce novel metrics for identifying under-annotated genes and novel transcripts.
• To quantify variations in junction donor and acceptor sites.

Main Methods:

• Leveraging SQANTI3 for transcript model quality analysis.
• Developing a read-level QC framework using number and distribution of reads and unique junction chains.
• Implementing multisample visualizations of QC metrics based on experimental design factors.
• Introducing new metrics for under-annotated gene identification and junction variation quantification.

Main Results:

• SQANTI-reads effectively reveals the impact of read coverage on data quality.
• The tool readily identifies strong and weak splicing sites.
• Analysis of junction chains within SQANTI3 structural categories provides insights into raw data quality.
• Multisample visualizations aid in identifying outliers in replicated experiments.

Conclusions:

• SQANTI-reads offers a robust framework for assessing the quality of long-read RNA-seq data.
• The tool aids in the identification of potentially novel transcripts and under-annotated genes.
• It enhances the reliability of transcript model analysis in complex experimental designs.