Smith-Waterman Local Alignment

Find the best local alignment between two sequences using dynamic programming. Ideal for identifying similar regions in divergent sequences.

Scoring Parameters

Dynamic Programming Matrix

Local Alignment Results

Alignment Location:
0
Score
0%
Identity
0
Length
-
Region

What is Smith-Waterman Local Alignment?

The Smith-Waterman algorithm is a dynamic programming method for finding the optimal local alignment between two sequences. Unlike global alignment, it identifies the best matching subsequences, making it ideal for finding similar regions in otherwise divergent sequences.

How to Use This Local Alignment Tool

Simple steps to find local alignments:

  1. Enter your two sequences in the input boxes
  2. Adjust scoring parameters if needed
  3. View the best local alignment and matrix
  4. Download or copy the results

When to Use Local Alignment

This tool is essential for:

  • Finding conserved domains in proteins
  • Identifying similar regions in divergent sequences
  • Database searches (BLAST uses similar approach)
  • Detecting homologous regions
  • Finding functional motifs

Example Input

Sample sequences for alignment:

Sequence 1: AAAAACGTACGTTTAAA
Sequence 2: CCCCCGTACGTCCCCC

The algorithm will find "CGTACGT" as the best local match.

Example Output

Expected local alignment:

Seq1: CGTACGT
      |||||||
Seq2: CGTACGT
Score: 14, Identity: 100%

Only the best matching region is aligned.

FAQ

Q: How does this differ from Needleman-Wunsch?
A: Smith-Waterman finds the best local alignment (subsequence), while Needleman-Wunsch aligns entire sequences globally.

Q: Why are negative scores set to zero?
A: This allows the algorithm to start fresh alignments, finding only the best local regions without penalty from surrounding mismatches.