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Updated: Sep 10, 2025

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A List-Based Parallel Bacterial Foraging Algorithm for the Multiple Sequence Alignment Problem.

Ernesto Rios-Willars1, María Magdalena Delabra-Salinas2, Alfredo Reyes-Acosta1

  • 1Faculty of Systems, The Autonomous University of Coahuila, Saltillo 25000, Mexico.

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|August 27, 2025
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Summary
This summary is machine-generated.

A new bacterial foraging algorithm effectively solves the multiple sequence alignment problem, showing consistent efficiency. One version outperformed the Genetic Algorithm for Alzheimer's disease-related sequences.

Keywords:
BFOAMSAbacterial foraging algorithm

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Algorithm Development

Background:

  • Multiple sequence alignment (MSA) is crucial for understanding genetic and protein relationships.
  • Existing algorithms may face challenges with large or complex biological datasets.
  • The bacterial foraging algorithm (BFA) is a nature-inspired optimization technique.

Purpose of the Study:

  • To develop and evaluate a parallel bacterial foraging algorithm for the multiple sequence alignment problem.
  • To assess the suitability of BFA for MSA, particularly for biological sequences related to Alzheimer's disease.
  • To compare the performance of different BFA versions and with the Genetic Algorithm (GA).

Main Methods:

  • Developed a parallel bacterial foraging algorithm.
  • Collected four sets of homologous genetic and protein sequences related to Alzheimer's disease from NCBI.
  • Compared three BFA versions using t-tests and Mann-Whitney tests over 30 runs, measuring fitness, execution time, and function evaluations.
  • Benchmarked the best BFA version against the Genetic Algorithm.

Main Results:

  • The bacterial foraging algorithm demonstrated consistent efficiency across tested sequences.
  • A BFA version incorporating gap deletion showed increased function evaluations and execution time.
  • The first BFA version was more efficient than the second.
  • The third BFA version outperformed the Genetic Algorithm in specific experimental phases.

Conclusions:

  • The parallel bacterial foraging algorithm is a viable and efficient method for multiple sequence alignment.
  • BFA offers a competitive alternative to established algorithms like the Genetic Algorithm for biological sequence analysis.
  • The study provides open-source code and data for reproducibility and further research.