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Multiple sequence alignment with affine gap by using multi-objective genetic algorithm.

Mehmet Kaya1, Abdullah Sarhan2, Reda Alhajj3

  • 1Department of Computer Engineering, Firat University, 23119 Elazig, Turkey.

Computer Methods and Programs in Biomedicine
|February 19, 2014
PubMed
Summary
This summary is machine-generated.

We introduce a novel multi-objective genetic algorithm (MSAGMOGA) for efficient multiple sequence alignment. This method optimizes affine gap penalties and maximizes similarity, offering superior accuracy and speed compared to existing approaches.

Keywords:
BioinformaticsMulti-objective genetic algorithmMultiple sequence alignment

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Algorithms

Background:

  • Multiple sequence alignment is crucial for bioinformatics and computational biology.
  • Efficient computation of accurate and statistically significant alignments remains a challenge.
  • Existing algorithms often struggle with optimizing multiple conflicting objectives simultaneously.

Purpose of the Study:

  • To propose an efficient multi-objective genetic algorithm (MSAGMOGA) for multiple sequence alignment.
  • To discover optimal alignments with affine gaps by balancing conflicting objectives.
  • To provide a flexible method applicable to various sequential data and similarity measures.

Main Methods:

  • Utilized a multi-objective genetic algorithm (MSAGMOGA) for sequence alignment.
  • Incorporated three objectives: affine gap penalty minimization, similarity maximization, and support maximization.
  • Applied the method to sequential data and evaluated using the BAliBASE 2.0 database.

Main Results:

  • MSAGMOGA generated a set of non-dominated alignments, revealing trade-offs between objectives.
  • Achieved higher accuracy and statistical significance compared to MUSCLE, SAGA, and MSA-GA.
  • Demonstrated faster solution finding than other evolutionary algorithms.

Conclusions:

  • MSAGMOGA offers an effective and efficient approach for multiple sequence alignment with affine gaps.
  • The method provides valuable insights into objective trade-offs for decision-makers.
  • This work represents a novel, three-objective approach in the field of multiple sequence alignment.