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Joined 2026-09-21 Message
Incoming links: Local sequence alignment
Fitting sequence alignment 2026-10-20
A fitting sequence alignment aligns all of a short query to a substring of a longer target, leaving target flanks unpenalized. It differs from unrestricted local sequence alignment, which may also omit part of the query.
Local sequence alignment 2026-10-20
A local sequence alignment finds the highest-scoring pair of subsequences, allowing unmatched flanks.
Use local sequence alignment, such as the Smith-Waterman algorithm, to compare the globin fragment with the matching region of each full-length target. A global sequence alignment would penalize long target flanks absent from the fragment. For a linear gap penalty the dynamic programming recurrence includes a restart option:Initialize boundary entries to zero, start traceback at the largest entry anywhere, and stop at zero. If the entire query is known to be valid and must be retained, fitting sequence alignment is a suitable refinement: align the whole fragment while leaving target flanks unpenalized.
First check the sequence alphabet: the prompt calls the targets “genes”. If the FASTA format file contains DNA rather than protein, translate the relevant coding sequences into amino acids before a protein-to-protein sequence alignment. If coding frames are unknown, search translated frames. A file in FASTA format specifies storage syntax, not whether the contents are nucleotides or residues.
The gain comes from avoiding a full dynamic programming table for every target. BLAST searches for short, sufficiently high-scoring word matches between query and database, then extends promising seeds. Modern versions reserve expensive gapped refinement for candidates passing earlier filters. FASTA sequence search similarly starts from short word matches, collects promising runs along alignment diagonals, and refines or joins selected regions. Word lookup and database indexing make candidate discovery inexpensive compared with considering every pair of positions.[ref]
Basic local alignment search tool by Stephen F. Altschul and others. pp. 403–406, word matches and hit extension
[ref]Improved tools for biological sequence comparison by William R. Pearson and David J. Lipman. pp. 2444–2446, FASTA search stages
These are heuristic algorithms: a good local sequence alignment can be missed if it has no acceptable seed, contains many interruptions, or is filtered before extension. Longer words and higher seed-score thresholds usually speed the search but reduce sensitivity; shorter words and looser thresholds recover weaker similarities at greater cost. Repetitive or compositionally biased regions can create many uninformative hits and erode both speed and specificity. No guarantee remains of finding the optimal sequence alignment for the chosen scoring scheme.
The maximum-scoring cell records the inclusive end position in each sequence. During traceback, moving left consumes a reference residue, moving up consumes a query residue, and moving diagonally consumes both; when traceback stops, the remaining prefix lengths are one less than the corresponding one-based starts. Consequently
i+1 and j+1 are the correct starts returned in part (a), without counting any inserted gaps as sequence residues.The following report joins these coordinates to the alignments and sequence identity/sequence similarity values already calculated:
for name, a in alignments.items():
print(f"{name}: query length={a['query_length']}, "
f"reference length={a['reference_length']}, "
f"alignment columns={a['alignment_length']}, score={a['score']}")
print(f"Query {a['query_start']} {a['query']} {a['query_end']}")
print(f"Ref {a['reference_start']} {a['reference']} {a['reference_end']}")
print(f"Identity {a['identity_percent']:.2f}%; "
f"similarity {a['similarity_percent']:.2f}%; "
f"gaps {a['gap_percent']:.2f}%")All reference sequences have length 508. The following are one-based inclusive coordinates; the aligned strings and all percentages are given in parts (a) and (b).