Pairwise alingment and Multille sequence alingment

Blast for Data analysis

1. MSA&PSA

2. What is Sequence Alignment? Definition: The process of arranging sequences to identify regions of similarity Purpose: To infer evolutionary, structural, or functional relationships between sequences Types of Alignment Pairwise Sequence Alignment : Compares two sequences Multiple Sequence Alignment : Compares three or more sequences

3. Introduction to Pairwise Sequence Alignment Definition: Aligning two biological sequences to find regions of similarity Applications Identifying homologous sequences Detecting evolutionary changes Analyzing functional elements Methods: Global alignment, Local alignment

4. Global vs. Local Alignment Global Alignment Aligns the entire sequence length from end to end Example: Needleman-Wunsch Algorithm Local Alignment Aligns the most similar regions within sequences Example: Smith-Waterman Algorithm Use Cases: Global: Similar sequences; Local: Regions of high similarity

5. Needleman-Wunsch Algorithm Key Idea: Align two sequences optimally from beginning to end Steps Create a scoring matrix Fill matrix using a scoring scheme Trace back to find the optimal alignment Applications: Comparing entire genomes or long sequences

6. Smith-Waterman Algorithm Key Idea: Align two sequences by finding the highest scoring local region Steps Create a scoring matrix Use scoring scheme with penalties and stop at zero Trace back from the highest score to find the local alignment Applications: Searching for highly conserved motifs in divergent sequences

7. What is Multiple Sequence Alignment ? Definition: MSA aligns three or more sequences to reveal similarities across all sequences Applications Identifying conserved regions Phylogenetic analysis Structural and functional analysis of proteins Challenges: Computational complexity increases with the number of sequences

8. Importance of Multiple Sequence Alignment Conserved Regions: Helps identify functional and structurally important regions in proteins or genes Evolutionary Relationships: Useful for building phylogenetic trees Protein Structure Prediction: Predicts function and structure by aligning unknown sequences with known ones Clinical Applications: MSA is used in understanding mutations in diseases

9. Methods of Multiple Sequence Alignment Progressive Alignment Aligns the most similar sequences first Example: ClustalW Iterative Alignment Refines the alignment iteratively for better accuracy Example: MUSCLE Consistency-Based Alignment Integrates multiple pairwise alignments Example: T-Coffee

10. ClustalW: Progressive Alignment How it works: Aligns sequences progressively based on a guide tree Advantages: Easy to use; suitable for a wide range of sequences Limitations: Sensitive to the initial alignment order; less accurate for divergent sequences

11. MUSCLE: Iterative Alignment How it works: Iteratively refines the alignment by multiple rounds of pairwise alignment Advantages: High accuracy; faster than other iterative methods Limitations: More computationally expensive than progressive methods

12. Comparison Of PSA & MSA