Uploaded October 2025 | Updated September 2026, 23 minutes ago
*How can you assess the quality of multiple long-read RNA sequencing (RNA-Seq) studies beyond basic metrics?* The SQANTI-reads tool helps evaluate transcript structure, identify technical library artifacts, and compare quality across samples to ensure robust experimental results.
SQANTI-reads applies SQANTI3 structural categories to individual reads. It groups reads by unique junction chains (UJCs) to track splicing patterns and quantify variation. This process helps identify novel transcripts and splice sites from the raw data.
*What you’ll learn*
* How to compare quality across multiple samples.
* A method to identify novel, unannotated transcripts.
* How to quantify variation around splice junctions.
* Ways to interpret multi-sample reports and visualizations.
00:00 Introduction to SQANTI-reads
00:30 Why quality control is needed for long-read RNA-Seq
01:21 Key features of the SQANTI-reads tool
03:15 Understanding SQANTI3 structural categories
04:06 Feature 1: Unique Junction Chains (UJCs)
04:39 Feature 2: Identifying unannotated transcripts
05:03 Feature 3: Quantifying junction variation
05:26 Feature 4: Multi-sample reports and visualization
11:14 Running SQANTI-reads: Simple vs. Fast mode
12:20 How to create the design file for analysis
13:28 Example command and interpreting output reports
21:22 Conclusion and summary
What quality control challenges have you faced in your RNA-Seq experiments? *Comment below.*
*Subscribe for more PacBio genomics content:*
youtube.com/@PacificBiosciences?sub_confirmation=1
Learn more about PacBio at pacb.com
Legal & Trademarks: pacb.com/legal-and-trademarks
#LongReadSequencing #Transcriptomics #Bioinformatics
*How can you assess the quality of multiple long-read RNA sequencing (RNA-Seq) studies beyond basic metrics?* The SQANTI-reads tool helps evaluate transcript structure, identify technical library artifacts, and compare quality across samples to ensure robust experimental results.
SQANTI-reads applies SQANTI3 structural categories to individual reads. It groups reads by unique junction chains (UJCs) to track splicing patterns and quantify variation. This process helps identify novel transcripts and splice sites from the raw data.
*What you’ll learn*
* How to compare quality across multiple samples.
* A method to identify novel, unannotated transcripts.
* How to quantify variation around splice junctions.
* Ways to interpret multi-sample reports and visualizations.
00:00 Introduction to SQANTI-reads
00:30 Why quality control is needed for long-read RNA-Seq
01:21 Key features of the SQANTI-reads tool
03:15 Understanding SQANTI3 structural categories
04:06 Feature 1: Unique Junction Chains (UJCs)
04:39 Feature 2: Identifying unannotated transcripts
05:03 Feature 3: Quantifying junction variation
05:26 Feature 4: Multi-sample reports and visualization
11:14 Running SQANTI-reads: Simple vs. Fast mode
12:20 How to create the design file for analysis
13:28 Example command and interpreting output reports
21:22 Conclusion and summary
What quality control challenges have you faced in your RNA-Seq experiments? *Comment below.*
*Subscribe for more PacBio genomics content:*
youtube.com/@PacificBiosciences?sub_confirmation=1
Learn more about PacBio at pacb.com
Legal & Trademarks: pacb.com/legal-and-trademarks
#LongReadSequencing #Transcriptomics #Bioinformatics
