Fast noisy long read alignment with multi-level parallelism
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| Pubblicato in: | BMC Bioinformatics vol. 26 (2025), p. 1 |
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| Autore principale: | |
| Altri autori: | , , , , , |
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Springer Nature B.V.
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| Accesso online: | Citation/Abstract Full Text Full Text - PDF |
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|---|---|---|---|
| 001 | 3201517842 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1471-2105 | ||
| 024 | 7 | |a 10.1186/s12859-025-06129-w |2 doi | |
| 035 | |a 3201517842 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 58459 |2 nlm | ||
| 100 | 1 | |a Xia, Zeyu | |
| 245 | 1 | |a Fast noisy long read alignment with multi-level parallelism | |
| 260 | |b Springer Nature B.V. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundThe advent of Single Molecule Real-Time (SMRT) sequencing has overcome many limitations of second-generation sequencing, such as limited read lengths, PCR amplification biases. However, longer reads increase data volume exponentially and high error rates make many existing alignment tools inapplicable. Additionally, a single CPU’s performance bottleneck restricts the effectiveness of alignment algorithms for SMRT sequencing.ResultsTo address these challenges, we introduce ParaHAT, a parallel alignment algorithm for noisy long reads. ParaHAT utilizes vector-level, thread-level, process-level, and heterogeneous parallelism. We redesign the dynamic programming matrices layouts to eliminate data dependency in the base-level alignment, enabling effective vectorization. We further enhance computational speed through heterogeneous parallel technology and implement the algorithm for multi-node computing using MPI, overcoming the computational limits of a single node.ConclusionsPerformance evaluations show that ParaHAT got a 10.03x speedup in base-level alignment, with a parallel acceleration ratio and weak scalability metric of 94.61 and 98.98% on 128 nodes, respectively. | |
| 653 | |a Background noise | ||
| 653 | |a Parallel processing | ||
| 653 | |a DNA sequencing | ||
| 653 | |a Dynamic programming | ||
| 653 | |a Alignment | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Algorithms | ||
| 653 | |a Communication | ||
| 653 | |a Redesign | ||
| 653 | |a Seeds | ||
| 653 | |a Effectiveness | ||
| 653 | |a Regions | ||
| 653 | |a Genomes | ||
| 653 | |a Computer applications | ||
| 653 | |a Efficiency | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Yang, Canqun | |
| 700 | 1 | |a Peng, Chenchen | |
| 700 | 1 | |a Guo, Yifei | |
| 700 | 1 | |a Guo, Yufei | |
| 700 | 1 | |a Tang, Tao | |
| 700 | 1 | |a Cui, Yingbo | |
| 773 | 0 | |t BMC Bioinformatics |g vol. 26 (2025), p. 1 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3201517842/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3201517842/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3201517842/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |