Abstract
Trimmomatic is a de-facto standard trimmer for Illumina sequencing data. However, limited by its sub-optimal implementation, it cannot fully exploit the computational power of common multi-core platforms. Therefore, we propose RabbitTrim, a highly optimized implementation of Trimmomatic based on efficient I/O strategies, parallel (de)compression engines, block-based memory pools, bitwise operations and vectorization techniques. RabbitTrim achieves speedups between 1.5x and 3.3x (3.7x and 8.0x) when processing plain (gzip-compressed) FASTQ files on a 48-core Intel server. Overall, RabbitTrim is able to process 101 GB gzip-compressed sequencing data in only 5 min while Trimmomatic requires at least 21 min. The source code is available at https://github.com/RabbitBio/RabbitTrim.
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Acknowledgement
This work is partially supported by NSFC Grants 62102231; Shandong Provincial Natural Science Foundation (ZR2021QF089); Engineering Research Center of Digital Media Technology, Ministry of Education, China.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Wang, M. et al. (2024). RabbitTrim: Highly Optimized Trimming of Illumina Sequencing Data on Multi-core Platforms. In: Peng, W., Cai, Z., Skums, P. (eds) Bioinformatics Research and Applications. ISBRA 2024. Lecture Notes in Computer Science(), vol 14955. Springer, Singapore. https://doi.org/10.1007/978-981-97-5131-0_3
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DOI: https://doi.org/10.1007/978-981-97-5131-0_3
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