SQANTI-reads: a tool for the quality assessment of long read data in multi-sample lrRNA-seq experiments

Area of Science:

• Bioinformatics
• Genomics
• Molecular Biology

Background:

• Long-read RNA sequencing (RNA-seq) offers comprehensive transcriptomic insights.
• Assessing the quality of replicated RNA-seq data is crucial for reliable analysis.
• Existing tools primarily focus on transcript model quality, not read-level QC.

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 transcript splicing patterns (junction donors and acceptors).

Main Methods:

• Leveraging SQANTI3 for transcript model analysis to build a read-level QC framework.
• Analyzing read number, distribution, and unique junction chains within SQANTI3 structural categories.
• Developing multi-sample visualizations of QC metrics based on experimental design factors.
• Introducing new metrics for gene/transcript annotation 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 *Drosophila* developmental and LRGASP datasets demonstrated the framework's utility.
• Identified potential under-annotated genes and putative novel transcripts.

Conclusions:

• SQANTI-reads provides a robust framework for read-level quality control in long-read RNA-seq.
• The developed metrics enhance the identification of novel transcriptional elements and splicing variations.
• The open-source tool facilitates improved data quality assessment and downstream analysis.