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FORAlign: accelerating gap-affine DNA pairwise sequence alignment using FOR-blocks based on Four Russians approach

Yanming Wei1,2, Tong Zhou2,3, Yixiao Zhai2,3

  • 1School of Computer Science and Technology, No. 266, Xinglong Section of Xifeng Road, Chang'an Zone, Xidian University, Xi'an 710126, China.

Briefings in Bioinformatics
|February 23, 2025
PubMed
Summary
This summary is machine-generated.

FORAlign accelerates pairwise sequence alignment (PSA) using the Four Russians algorithm, achieving significant speedups for low-similarity sequences. This bioinformatics tool enhances phylogenetic analysis and multiple sequence alignment capabilities.

Keywords:
dynamic programmingfour Russians speedupparallel algorithm designsequence alignment

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Area of Science:

  • Computational Bioinformatics
  • Bioinformatics Algorithms
  • Sequence Analysis

Background:

  • Pairwise sequence alignment (PSA) is fundamental for bioinformatics tasks like multiple sequence alignment and phylogenetic analysis.
  • Existing methods, such as Needleman-Wunsch, can be computationally intensive, especially for sequences with low similarity.
  • Accelerating PSA is crucial for advancing large-scale biological data analysis.

Purpose of the Study:

  • Introduce the FORAlign algorithm for accelerated pairwise sequence alignment.
  • Evaluate FORAlign's performance against established methods, particularly for sequences with low similarity.
  • Provide a practical library for PSA and downstream bioinformatics applications.

Main Methods:

  • Developed the FORAlign algorithm, adapting the Four Russians algorithm for PSA.
  • Achieved identical upper-bound time and space complexity to the Hirschberg algorithm for parallel acceleration.
  • Implemented FORAlign as a library supporting PSA, multiple sequence alignment, and phylogenetic tree construction.

Main Results:

  • FORAlign demonstrates up to 16.79 times speedup compared to the Needleman-Wunsch method for low-similarity sequences.
  • Empirical evaluations show FORAlign outperforms existing wavefront alignment (WFA) software in low-similarity PSA tasks.
  • The algorithm successfully aligned monkeypox sequences using non-heuristic methods.

Conclusions:

  • FORAlign offers a significant advancement in accelerating pairwise sequence alignment, especially for challenging low-similarity datasets.
  • The FORAlign library provides a valuable, freely available tool for computational bioinformatics research.
  • This work contributes to more efficient phylogenetic analysis and multiple sequence alignment.