Fast noisy long read alignment with multi-level parallelism
Na minha lista:
| Publicado no: | BMC Bioinformatics vol. 26 (2025), p. 1 |
|---|---|
| Autor principal: | |
| Outros Autores: | , , , , , |
| Publicado em: |
Springer Nature B.V.
|
| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full Text Full Text - PDF |
| Tags: |
Sem tags, seja o primeiro a adicionar uma tag!
|
| Resumo: | 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. |
|---|---|
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-025-06129-w |
| Fonte: | Health & Medical Collection |