Global Alignment vs. Local Alignment

Global Alignment

Goal: To align the entire length of two sequences, maximizing similarity across the entire length.  
Suitable for:
Comparing closely related sequences of similar length.  
Identifying overall similarity and evolutionary relationships.
Example: Aligning two full-length protein sequences to assess their overall evolutionary divergence.

Local Alignment

Goal: To identify regions of high similarity within the sequences, regardless of their position within the overall sequence.  
Suitable for:
Finding short conserved regions or motifs within longer sequences.  
Identifying conserved domains in proteins.  
Detecting distant evolutionary relationships where only small regions might be conserved.  
Example: Finding a short conserved DNA motif within a large genomic sequence.
Key Differences

Scope: Global aligns the entire length, while local focuses on regions of highest similarity.  
Gap Penalties: Global alignment often treats gaps equally throughout. Local alignment may penalize gaps more heavily near the ends of the alignment.  
Applications: Global alignment is better for closely related sequences, while local alignment is more suitable for detecting short conserved regions or distant relationships.  
Algorithms

Global: Needleman-Wunsch algorithm  
Local: Smith-Waterman algorithm 1

https://www.youtube.com/watch?v=99mpiXe1TvQ