An optimized workflow of full-length transcriptome sequencing for accurate fusion transcript identification.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Next-generation sequencing (NGS) advances cancer genomics but struggles with reliable fusion gene detection.
• Long-read sequencing (LRS) shows promise for fusion transcript identification, yet faces persistent challenges.

Purpose of the Study:

• To develop and optimize a nanopore sequencing workflow for precise identification of fusion transcripts.
• To enhance the accuracy and mitigate biases in fusion gene analysis for clinical applications.

Main Methods:

• Utilized nanopore sequencing technology with a tailored library preparation protocol.
• Implemented a dedicated data processing and fusion gene analysis pipeline.
• Evaluated performance using Universal Human Reference RNA and human adenocarcinoma cell lines.

Main Results:

• Generated high-quality, full-length transcriptome data with extended length distribution and comprehensive coverage.
• Confirmed novel fusion events with potential clinical relevance through validation experiments.
• Demonstrated an optimized workflow for accurate fusion transcript identification.

Conclusions:

• The optimized nanopore sequencing workflow precisely identifies fusion transcripts, improving accuracy over existing methods.
• This protocol facilitates increased adoption in clinical diagnostics for cancer genomics.
• Advancements in LRS, including this workflow, promise deeper insights into fusion gene biology and enhanced cancer diagnostics.