Efficient downsampling of genome alignments with Rasusa.

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.