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Leveraging basecaller's move table to generate a lightweight k-mer model for nanopore sequencing analysis
Hiruna Samarakoon1,2,3, Yuk Kei Wan4,5, Sri Parameswaran6
1School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Bioinformatics (Oxford, England)
|March 14, 2025
Summary
Researchers developed Poregen, a new method for creating custom nanopore sequencing k-mer models. This lightweight model improves RNA004 chemistry basecalling accuracy and performance for tasks like RNA modification detection.
Area of Science:
- Genomics and Molecular Biology
- Bioinformatics and Computational Biology
Background:
- Nanopore sequencing captures electrical signals for direct DNA/RNA analysis.
- K-mer models are essential for accurate signal-to-sequence alignment in nanopore sequencing.
- Custom k-mer models are needed when official models are unavailable or unsuitable.
Purpose of the Study:
- To develop a method for creating custom, de novo k-mer models for nanopore sequencing.
- To address the need for tailored models for specific sequencing chemistries like RNA004.
- To improve the accuracy and efficiency of nanopore-based RNA analysis.
Main Methods:
- Leveraged the move table from Oxford Nanopore Technologies (ONT) basecalling software.
- Developed a lightweight de novo 5-mer k-mer model for RNA004 chemistry.
- Implemented the method as an open-source package named Poregen.
Main Results:
- Achieved high signal-to-sequence alignment rates (97.48%) using the custom k-mer model.
- Demonstrated comparable performance to default 9-mer models in tasks like m6A RNA modification detection.
- Validated the generalizability of the Poregen approach for custom k-mer model creation.
Conclusions:
- Poregen provides a generalizable method for creating custom de novo k-mer models.
- Custom k-mer models enhance the precision of nanopore signal data analysis.
- The developed 5-mer model offers an efficient alternative to larger default models for RNA analysis.
