SequenceLab: A Comprehensive Benchmark of Computational Methods for Comparing Genomic Sequences
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2023
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| author | Rumpf, Maximilian-David Alser, Mohammed Gollwitzer, Arvid E. Lindegger, Joel Almadhoun, Nour Firtina, Can Mangul, Serghei Mutlu, Onur |
| author_facet | Rumpf, Maximilian-David Alser, Mohammed Gollwitzer, Arvid E. Lindegger, Joel Almadhoun, Nour Firtina, Can Mangul, Serghei Mutlu, Onur |
| contents | Computational complexity is a key limitation of genomic analyses. Thus, over the last 30 years, researchers have proposed numerous fast heuristic methods that provide computational relief. Comparing genomic sequences is one of the most fundamental computational steps in most genomic analyses. Due to its high computational complexity, optimized exact and heuristic algorithms are still being developed. We find that these methods are highly sensitive to the underlying data, its quality, and various hyperparameters. Despite their wide use, no in-depth analysis has been performed, potentially falsely discarding genetic sequences from further analysis and unnecessarily inflating computational costs. We provide the first analysis and benchmark of this heterogeneity. We deliver an actionable overview of the 11 most widely used state-of-the-art methods for comparing genomic sequences. We also inform readers about their advantages and downsides using thorough experimental evaluation and different real datasets from all major manufacturers (i.e., Illumina, ONT, and PacBio). SequenceLab is publicly available at https://github.com/CMU-SAFARI/SequenceLab. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_16908 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SequenceLab: A Comprehensive Benchmark of Computational Methods for Comparing Genomic Sequences Rumpf, Maximilian-David Alser, Mohammed Gollwitzer, Arvid E. Lindegger, Joel Almadhoun, Nour Firtina, Can Mangul, Serghei Mutlu, Onur Genomics Hardware Architecture Quantitative Methods Computational complexity is a key limitation of genomic analyses. Thus, over the last 30 years, researchers have proposed numerous fast heuristic methods that provide computational relief. Comparing genomic sequences is one of the most fundamental computational steps in most genomic analyses. Due to its high computational complexity, optimized exact and heuristic algorithms are still being developed. We find that these methods are highly sensitive to the underlying data, its quality, and various hyperparameters. Despite their wide use, no in-depth analysis has been performed, potentially falsely discarding genetic sequences from further analysis and unnecessarily inflating computational costs. We provide the first analysis and benchmark of this heterogeneity. We deliver an actionable overview of the 11 most widely used state-of-the-art methods for comparing genomic sequences. We also inform readers about their advantages and downsides using thorough experimental evaluation and different real datasets from all major manufacturers (i.e., Illumina, ONT, and PacBio). SequenceLab is publicly available at https://github.com/CMU-SAFARI/SequenceLab. |
| title | SequenceLab: A Comprehensive Benchmark of Computational Methods for Comparing Genomic Sequences |
| topic | Genomics Hardware Architecture Quantitative Methods |
| url | https://arxiv.org/abs/2310.16908 |