PanDelos-plus: A parallel algorithm for computing sequence homology in pangenomic analysis
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| Format: | Preprint |
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2025
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| _version_ | 1866911235202613248 |
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| author | Colli, Simone Maresi, Emiliano Bonnici, Vincenzo |
| author_facet | Colli, Simone Maresi, Emiliano Bonnici, Vincenzo |
| contents | The identification of homologous gene families across multiple genomes is a central task in bacterial pangenomics traditionally requiring computationally demanding all-against-all comparisons. PanDelos addresses this challenge with an alignment-free and parameter-free approach based on k-mer profiles, combining high speed, ease of use, and competitive accuracy with state-of-the-art methods. However, the increasing availability of genomic data requires tools that can scale efficiently to larger datasets. To address this need, we present PanDelos-plus, a fully parallel, gene-centric redesign of PanDelos. The algorithm parallelizes the most computationally intensive phases (Best Hit detection and Bidirectional Best Hit extraction) through data decomposition and a thread pool strategy, while employing lightweight data structures to reduce memory usage. Benchmarks on synthetic datasets show that PanDelos-plus achieves up to 14x faster execution and reduces memory usage by up to 96%, while maintaining accuracy. These improvements enable population-scale comparative genomics to be performed on standard multicore workstations, making large-scale bacterial pangenome analysis accessible for routine use in everyday research. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_23679 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PanDelos-plus: A parallel algorithm for computing sequence homology in pangenomic analysis Colli, Simone Maresi, Emiliano Bonnici, Vincenzo Genomics Distributed, Parallel, and Cluster Computing The identification of homologous gene families across multiple genomes is a central task in bacterial pangenomics traditionally requiring computationally demanding all-against-all comparisons. PanDelos addresses this challenge with an alignment-free and parameter-free approach based on k-mer profiles, combining high speed, ease of use, and competitive accuracy with state-of-the-art methods. However, the increasing availability of genomic data requires tools that can scale efficiently to larger datasets. To address this need, we present PanDelos-plus, a fully parallel, gene-centric redesign of PanDelos. The algorithm parallelizes the most computationally intensive phases (Best Hit detection and Bidirectional Best Hit extraction) through data decomposition and a thread pool strategy, while employing lightweight data structures to reduce memory usage. Benchmarks on synthetic datasets show that PanDelos-plus achieves up to 14x faster execution and reduces memory usage by up to 96%, while maintaining accuracy. These improvements enable population-scale comparative genomics to be performed on standard multicore workstations, making large-scale bacterial pangenome analysis accessible for routine use in everyday research. |
| title | PanDelos-plus: A parallel algorithm for computing sequence homology in pangenomic analysis |
| topic | Genomics Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2510.23679 |