ADAM-tRNA-seq: an optimized approach for demultiplexing and enhanced hierarchal mapping in direct tRNA sequencing

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Transfer RNAs (tRNAs) are crucial for protein synthesis and cellular health.
• Dysregulated tRNAs are linked to human diseases.
• Direct RNA sequencing offers simultaneous profiling of tRNA features but faces accuracy and scalability challenges due to sequence similarity and demultiplexing limitations.

Purpose of the Study:

• To develop a robust framework, ADAM-tRNA-seq, to overcome limitations in Nanopore-based direct tRNA sequencing.
• To improve the accuracy and scalability of tRNA profiling using direct RNA sequencing.

Main Methods:

• Developed an RNA-based barcode demultiplexing method using adapter-embedded barcodes recognized by the Dorado basecaller.
• Designed a hierarchy-based mapping strategy to classify reads at isodecoder, isoacceptor, or isotype levels, reducing read loss from multimapping.
• Validated the framework using synthetic and complex human tRNA pools.

Main Results:

• Achieved up to 99% classification precision.
• Significantly enhanced quantification accuracy by mitigating read loss.
• Demonstrated the framework's effectiveness in characterizing tRNA pools.

Conclusions:

• ADAM-tRNA-seq provides a more accurate and scalable solution for direct tRNA sequencing.
• This framework enables comprehensive characterization of tRNA pools across various sample types.
• Facilitates deeper understanding of tRNA roles in health and disease.