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Efficient downsampling of genome alignments with Rasusa
Achmad Dimas Cahyaning Furqon1, Leah W Roberts1, Michael B Hall1
1The University of Queensland, UQ Centre for Clinical Research, QLD 4029, Herston, Australia.
Gigabyte (Hong Kong, China)
|May 7, 2026
Summary
New software rasusa efficiently normalizes high-throughput sequencing data by capping read depth variation. This method ensures unbiased, reproducible results, significantly speeding up genomic analysis.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- High-throughput sequencing data often shows extreme read depth variation, which can bias downstream analyses.
- Current methods for normalizing sequencing coverage to a specific depth cap are computationally expensive or non-deterministic.
Purpose of the Study:
- To introduce a novel, efficient algorithm for sequencing coverage normalization.
- To provide an open-source software solution (rasusa) for reproducible read selection and coverage capping.
Main Methods:
- A coordinate-sorted sweep-line algorithm utilizing seeded random priority assignment.
- Implementation in the open-source software rasusa for strict coverage capping at every genomic position.
Main Results:
- The rasusa algorithm reduces runtimes by over 1,400-fold compared to legacy fetch-based methods.
- Achieves unbiased and reproducible read selection with minimal memory usage (8 MB for long-read data).
- Operates approximately four times faster than VariantBam.
Conclusions:
- Rasusa offers a highly efficient, scalable, and reproducible solution for sequencing coverage normalization.
- Addresses the critical need for accurate data processing in high-throughput sequencing analysis.
- Facilitates more reliable downstream genomic analyses by mitigating read depth biases.
