CapTrap-Seq: A platform-agnostic and quantitative approach for high-fidelity full-length RNA transcript sequencing.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Accurate eukaryotic genome annotation relies on comprehensive RNA sequencing.
• Identifying full-length RNA transcripts remains a challenge for current long-read sequencing technologies.

Approach:

• Developed CapTrap-seq, a cDNA library preparation method combining Cap-trapping and oligo(dT) priming.
• Integrated CapTrap-seq with the LyRic data processing pipeline for enhanced transcript identification.
• Benchmarked CapTrap-seq against other protocols using human tissues and both ONT and PacBio sequencing platforms.

Key Points:

• CapTrap-seq effectively captures 5'capped, full-length transcripts.
• The LyRic pipeline, processing CapTrap-seq data, generates highly accurate transcript models.
• Up to 90% of transcript models derived from CapTrap-seq reads by LyRic are full-length.

Conclusions:

• CapTrap-seq significantly enhances the accuracy and completeness of RNA transcript annotation.
• This method enables highly reliable genome annotation with reduced manual curation.
• CapTrap-seq represents a substantial advancement for long-read RNA sequencing applications.