Related Experiment Video
Updated: May 9, 2025
Ultra-long Read Sequencing for Whole Genomic DNA Analysis
Published on: March 15, 2019
Fast noisy long read alignment with multi-level parallelism
Zeyu Xia1, Canqun Yang1,2,3, Chenchen Peng1
1College of Computer Science and Technology, National University of Defense Technology, 410073, Changsha, China.
Background:
The 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.
Results:
To 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.
Conclusions:
Performance 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.
