Despite its many advantages, Clustal Omega has some limitations:
1. Sequence Similarity Requirement: It performs best with sequences that have a moderate to high degree of similarity. Highly divergent sequences may not align well. 2. Computational Resources: Handling very large datasets can be resource-intensive, requiring significant computational power. 3. Manual Adjustment: Sometimes, automatic alignments may need manual adjustments to be fully accurate.
