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Parallel and Scalable Short-Read Alignment on Multi-Core Clusters Using UPC+.

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Summary
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This study introduces an efficient parallelization method for next-generation sequencing (NGS) read alignment, significantly reducing processing time on multi-core clusters. The new approach accelerates the alignment of large NGS datasets, making genomic analysis faster and more accessible.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) generates massive datasets, challenging existing read alignment tools.
  • Current aligners often trade alignment speed for accuracy, hindering large-scale genomic research.
  • Efficient alignment is crucial for timely analysis of growing NGS data.

Purpose of the Study:

  • To develop an efficient parallelization strategy for next-generation sequencing short-read alignment on multi-core clusters.
  • To improve both the speed and scalability of read alignment for large genomic datasets.
  • To leverage the UPC++ programming language for distributed shared memory parallelization.

Main Methods:

  • Implemented a parallelization approach for the CUSHAW3 aligner using the UPC++ programming language.
  • Utilized dynamic scheduling for efficient workload distribution across multi-core clusters.
  • Evaluated performance on large-scale datasets, including 246 million reads of 150 base-pairs.

Main Results:

  • Achieved significant speedups in read alignment compared to the original multi-threaded CUSHAW3 tool.
  • Completed alignment of 246 million reads in under 17 minutes using 32 nodes.
  • Demonstrated good scalability of the parallel implementation on multi-core clusters.
  • The parallel implementation processed reads approximately 14 times faster than the original tool.

Conclusions:

  • The developed parallelization approach offers a highly efficient solution for NGS read alignment.
  • This method significantly reduces execution time, enabling faster analysis of large genomic datasets.
  • The publicly available source code facilitates broader adoption and further research in accelerated genomic alignment.