Long-read RNA sequencing (lrRNA-seq) is revolutionizing transcriptomics by capturing full-length transcripts in single reads—eliminating the need for computational reconstruction. However, with this power comes a need for rigorous quality control. Issues like mRNA degradation, sequencing errors, and library prep inconsistencies can skew results, especially in experiments involving multiple samples.
Enter SQANTI-reads, a robust new tool designed specifically to assess data quality in complex, multisample lrRNA-seq experiments. Built on the foundation of the widely used SQANTI3, this extension shifts the focus from transcript models to individual reads—bringing read-level quality control into the spotlight.
Why SQANTI-reads Matters:
- Read & Splicing Quality: It evaluates read counts and splicing patterns (via junction chains) across structural categories to assess consistency and integrity.
- Multisample QC: With visualization tools tailored to experimental design, SQANTI-reads helps detect technical failures, outliers, and bias across datasets.
- Novel Feature Detection: It flags under-annotated genes and novel transcripts, supporting discovery-driven research.
- Splice Site Analysis: New metrics quantify donor and acceptor variation, highlighting strong or weak splice signals.
- Scalability: Designed for large-scale experiments, it handles millions of reads and many samples with ease.
As long-read technologies scale, tools like SQANTI-reads are essential for keeping data quality high and interpretations accurate. Whether testing a new basecaller or evaluating library prep methods, SQANTI-reads offers the insights researchers need to trust their results.