RabbitSketch: a high-performance sketching library for genome analysis
We present RabbitSketch, a highly optimized library of sketching algorithms such as MinHash, OrderMinHash, and HyperLogLog that can exploit the power of modern multi-core CPUs. It provides significant speedups compared to existing implementations, ranging from 2.30× to 49.55×, as well as flexible an...
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| Published in | Bioinformatics (Oxford, England) Vol. 41; no. 5 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
06.05.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4811 1367-4803 1367-4811 |
| DOI | 10.1093/bioinformatics/btaf249 |
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| Abstract | We present RabbitSketch, a highly optimized library of sketching algorithms such as MinHash, OrderMinHash, and HyperLogLog that can exploit the power of modern multi-core CPUs. It provides significant speedups compared to existing implementations, ranging from 2.30× to 49.55×, as well as flexible and easy-to-use interfaces for both Python and C++. As a result, the similarity analysis of 455GB genomic data can be completed in only 5 minutes using RabbitSketch with merely 20 lines of Python code. As a case study, we enhanced RabbitTClust by integrating RabbitSketch's Kssd algorithm, resulting in a 1.54× speedup with no loss in accuracy.
RabbitSketch is available at https://github.com/RabbitBio/RabbitSketch with an archived version at Zenodo: https://doi.org/10.5281/zenodo.14903962. Detailed API documentation is available at https://rabbitsketch.readthedocs.io/en/latest. |
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| AbstractList | We present RabbitSketch, a highly optimized library of sketching algorithms such as MinHash, OrderMinHash, and HyperLogLog that can exploit the power of modern multi-core CPUs. It provides significant speedups compared to existing implementations, ranging from 2.30× to 49.55×, as well as flexible and easy-to-use interfaces for both Python and C++. As a result, the similarity analysis of 455GB genomic data can be completed in only 5 minutes using RabbitSketch with merely 20 lines of Python code. As a case study, we enhanced RabbitTClust by integrating RabbitSketch's Kssd algorithm, resulting in a 1.54× speedup with no loss in accuracy.
RabbitSketch is available at https://github.com/RabbitBio/RabbitSketch with an archived version at Zenodo: https://doi.org/10.5281/zenodo.14903962. Detailed API documentation is available at https://rabbitsketch.readthedocs.io/en/latest. We present RabbitSketch, a highly optimized library of sketching algorithms such as MinHash, OrderMinHash, and HyperLogLog that can exploit the power of modern multi-core CPUs. It provides significant speedups compared to existing implementations, ranging from 2.30x to 49.55x, as well as flexible and easy-to-use interfaces for both Python and C ++. As a result the similarity analysis of 455GB genomic data can be completed in only 5 minutes using RabbitSketch with merely 20 lines of Python code. As a case study, we enhanced RabbitTClust by integrating RabbitSketch's Kssd algorithm, resulting in a 1.54× speedup with no loss in accuracy.SUMMARYWe present RabbitSketch, a highly optimized library of sketching algorithms such as MinHash, OrderMinHash, and HyperLogLog that can exploit the power of modern multi-core CPUs. It provides significant speedups compared to existing implementations, ranging from 2.30x to 49.55x, as well as flexible and easy-to-use interfaces for both Python and C ++. As a result the similarity analysis of 455GB genomic data can be completed in only 5 minutes using RabbitSketch with merely 20 lines of Python code. As a case study, we enhanced RabbitTClust by integrating RabbitSketch's Kssd algorithm, resulting in a 1.54× speedup with no loss in accuracy.RabbitSketch is available at https://github.com/RabbitBio/RabbitSketch with an archived version at Zenodo: https://doi.org/10.5281/zenodo.14903962. Detailed API documentation is available at https://rabbitsketch.readthedocs.io/en/latest.AVAILABILITY AND IMPLEMENTATIONRabbitSketch is available at https://github.com/RabbitBio/RabbitSketch with an archived version at Zenodo: https://doi.org/10.5281/zenodo.14903962. Detailed API documentation is available at https://rabbitsketch.readthedocs.io/en/latest.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. |
| Author | Duan, Xiaohui Schmidt, Bertil Zhang, Tong Xu, Xiaoming Yin, Zekun Zhu, Fangjin Liu, Weiguo Yan, Lifeng |
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| References | Xu (2025051307513050100_btaf249-B10) 2023; 24 Baker (2025051307513050100_btaf249-B1) 2019; 20 Xu (2025051307513050100_btaf249-B9) 2023; 39 Inoue (2025051307513050100_btaf249-B4) 2014; 8 Yi (2025051307513050100_btaf249-B11) 2021; 22 Yin (2025051307513050100_btaf249-B12) 2017; 15 Marçais (2025051307513050100_btaf249-B5) 2019; 35 Yin (2025051307513050100_btaf249-B13) 2021; 37 Zhang (2025051307513050100_btaf249-B14) 2023; 20 Ondov (2025051307513050100_btaf249-B6) 2016; 17 Schmidt (2025051307513050100_btaf249-B8) 2024; 25 Baker (2025051307513050100_btaf249-B2) 2023; 33 Elworth (2025051307513050100_btaf249-B3) 2020; 48 Rowe (2025051307513050100_btaf249-B7) 2019; 20 |
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| Title | RabbitSketch: a high-performance sketching library for genome analysis |
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