Sequence Alignment Tool

Align DNA, RNA, and protein sequences with multiple algorithms. Visualize alignments and calculate similarity scores.

You are viewing demo data. Enter your own sequences to perform custom alignment.
Enter sequence in FASTA format or as plain text
Alignment Parameters

Note: For best results, ensure sequences are in the correct format. DNA/RNA sequences should contain only A, T/U, G, C characters. Protein sequences should use standard amino acid codes.

Understanding Sequence Alignment

Sequence alignment is a fundamental bioinformatics technique for comparing biological sequences (DNA, RNA, or protein) to identify regions of similarity that may indicate functional, structural, or evolutionary relationships.

Key Insight: Sequence alignment helps researchers understand evolutionary relationships, predict protein structure and function, and identify conserved regions across species.

Alignment Algorithms

Algorithm Type Use Case Complexity
Needleman-Wunsch Global Aligning entire sequences end-to-end O(n*m)
Smith-Waterman Local Finding local regions of similarity O(n*m)
BLAST Heuristic Database searching O(n)
Clustal Multiple Aligning multiple sequences O(n²)

Frequently Asked Questions

Common questions about sequence alignment

Global alignment (Needleman-Wunsch algorithm) attempts to align entire sequences from end to end, suitable for sequences of similar length and overall similarity.

Local alignment (Smith-Waterman algorithm) finds the most similar regions within sequences, ideal for identifying conserved domains or motifs.

Selection guidelines:

  • Use global alignment when sequences have similar length and expected overall similarity
  • Use local alignment when sequences differ in length or you're only interested in local similarity

Gap penalties control the cost of introducing gaps in alignments:

  • Gap opening penalty: Cost for initiating a gap (typically higher)
  • Gap extension penalty: Cost for extending an existing gap (typically lower)

General recommendations:

  • DNA/RNA: Start with gap open = -2, gap extend = -1
  • Proteins: Use established matrices like BLOSUM62 with appropriate gap penalties
  • Lower penalties allow more gaps, useful for distantly related sequences

This tool supports multiple sequence formats:

  • FASTA format: Sequences with headers starting with ">"
  • Plain text: Raw sequence data without headers

Sequence type requirements:

  • DNA: Only A, T, G, C characters (case insensitive)
  • RNA: Only A, U, G, C characters (case insensitive)
  • Protein: Standard amino acid codes (A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y)

Key alignment statistics explained:

  • Alignment Score: Numerical value representing the quality of alignment based on your scoring parameters
  • Identity: Percentage of positions with identical residues in both sequences
  • Similarity: Percentage of positions with similar residues (for protein alignments with substitution matrices)
  • Gaps: Total number of gap characters introduced in the alignment

Higher identity/similarity percentages and alignment scores generally indicate better alignments.

Sequence limits for this tool:

  • Pairwise alignment: 2 sequences (global or local)
  • Multiple sequence alignment: Up to 6 sequences

For larger datasets or more complex analyses, consider specialized software like Clustal Omega, MAFFT, or MUSCLE.

Note: Performance may decrease with longer sequences or more complex alignment parameters.