RabbitBAM: Accelerating BAM File Manipulation on Multi-Core Platforms

Abstract

With the continuous advancement of sequencing technology, the scale of biological data has rapidly increased. BAM format, widely used for storing aligned sequence data, is very popular due to its ease of use and good compression ratio. However, existing BAM-format file I/O libraries often fail to fully leverage the computational power of modern multi-core platforms, resulting in low CPU utilization. To address this, we introduce RabbitBAM, a fast BAM-format file I/O library. RabbitBAM employs pre-parsing and parallel parsing techniques to eliminate parsing bottlenecks and improve parallel efficiency. Additionally, we optimize multi-threaded data handling through the use of dedicated lock-free queues and memory pools. RabbitBAM achieves 2.1-3.3x speedups on next-generation sequencing data and 1-2.2x speedups on third-generation sequencing data compared to state-of-the-art SAMtools (HTSlib). We also present two case studies (BAM file quality control and sorting) using RabbitBAM, demonstrating 1.4-2.4x speedups compared to other implementations. RabbitBAM and case studies are freely available at https://github.com/RabbitBio , with usage documentation at https://rabbitbam.readthedocs.io/en/latest.