Improving accuracy of multiple sequence alignment algorithms based on alignment of neighboring residues

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Recent advancements in multiple sequence alignment primarily leverage vertical information, such as consistency-based pairwise and profile alignments.
• Existing methods have limitations in fully exploiting the contextual information between aligned residues.

Purpose of the Study:

• To enhance multiple sequence alignment accuracy by integrating horizontal information, specifically the alignment of neighboring residues.
• To demonstrate the effectiveness of this new strategy across various sequence types and identity levels.

Main Methods:

• Modification of existing multiple sequence alignment algorithms to incorporate horizontal information.
• Evaluation of the modified algorithms using established benchmark alignment datasets for proteins and DNA/RNA.

Main Results:

• Consistent accuracy improvements observed across all tested benchmark datasets.
• Average accuracy gains of 1-3% for protein sequence alignment and 5-10% for DNA/RNA sequence alignment.
• The proposed method achieves reliable improvements irrespective of sequence identity levels.

Conclusions:

• Incorporating horizontal information offers a significant improvement over traditional vertical information-based methods in multiple sequence alignment.
• This approach provides a robust strategy for enhancing alignment accuracy in both protein and nucleic acid sequences.
• The findings suggest a new direction for developing more accurate sequence alignment tools.