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Updated: May 10, 2026
07:49
Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
Published on: August 16, 2017
A new greedy randomised adaptive search procedure for Multiple Sequence Alignment
Abdesslem Layeb1, Marwa Selmane, Maroua Bencheikh Elhoucine
1Computer Science Department, Mentouri University Constantine, Ain El Bey, Constantine 25017, Algeria. layeb@umc.edu.dz
Summary
This study introduces a new algorithm for Multiple Sequence Alignment (MSA), a complex bioinformatics problem. The novel approach demonstrates effectiveness and feasibility for aligning protein and nucleic sequences.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Multiple Sequence Alignment (MSA) is a fundamental yet computationally challenging problem in bioinformatics.
- Aligning multiple protein or nucleic sequences is crucial for understanding evolutionary relationships and functional characteristics.
- The MSA problem is NP-complete for more than two sequences, necessitating efficient algorithmic solutions.
Purpose of the Study:
- To present a novel, incomplete algorithm for addressing the NP-complete Multiple Sequence Alignment (MSA) problem.
- To introduce a new approach based on the Greedy Randomised Adaptive Search Procedure (GRASP).
Main Methods:
- The proposed algorithm utilizes a Greedy Randomised Adaptive Search Procedure (GRASP).
- The first phase involves a greedy algorithm incorporating a random progressive method and a hybrid global/local alignment strategy.
- The second phase employs an adaptive refinement method centered on consensus alignment.
Main Results:
- The algorithm demonstrated encouraging results in handling the MSA problem.
- The study validates the feasibility and effectiveness of the proposed GRASP-based approach.
Conclusions:
- The developed GRASP-based algorithm offers a promising solution for Multiple Sequence Alignment.
- The approach shows significant potential for advancing bioinformatics research and applications.
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