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Fast noisy long read alignment with multi-level parallelism

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Publicat a:BMC Bioinformatics vol. 26 (2025), p. 1
Autor principal: Xia, Zeyu
Altres autors: Yang, Canqun, Peng, Chenchen, Guo, Yifei, Guo, Yufei, Tang, Tao, Cui, Yingbo
Publicat:
Springer Nature B.V.
Matèries:
Background noise
Parallel processing
DNA sequencing
Dynamic programming
Alignment
Performance evaluation
Algorithms
Communication
Redesign
Seeds
Effectiveness
Regions
Genomes
Computer applications
Efficiency
Economic
Accés en línia:Citation/Abstract
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Resum: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
Font:Health & Medical Collection