Sequence analysis
Local sequence alignment
Score the best-matching subsequences without forcing unrelated flanks into an end-to-end comparison.
Inputs
1 required
Methods
1 connected
- 01StringZilla v5 · Smith–Waterman Scores
StringZilla calculates all-pairs Smith–Waterman local scores and explicitly returns scores rather than alignment paths.
Use this templateWhat is local sequence alignment?
Local sequence alignment is the process of finding and arranging the highest-scoring matching region within two sequences instead of comparing them end to end. Smith–Waterman dynamic programming allows an alignment to start and end internally under a substitution and gap model, making it appropriate for shared domains, motifs, fragments, or partial homology. A local score does not by itself provide aligned strings, coordinates, significance, or biological function.
Choose local alignment when only part of each sequence is expected to correspond. Examples include a conserved domain embedded in different architectures, a short motif, a sequencing fragment, or a query against longer candidates. If complete lengths should correspond, global alignment provides a more appropriate penalty model.
ProteinIQ’s StringZilla workflow returns Smith–Waterman score matrices, not traceback paths. Use those scores for rapid comparison and ranking, then use a method that emits alignments and coordinates when residue-level interpretation is required. Preserve scoring parameters because local scores are not comparable across arbitrary settings and lengths.
When to use local sequence alignment
- Best fit. Shared domains, motifs, fragments, and partial homology
- Required input. Sequences expected to share only a subsection and explicit scoring parameters
Benefits of local sequence alignment
- Clear correspondence. Ignores unrelated flanking regions
- Connected evidence. Finds partial similarity
- Reusable output. Supports fast pair prioritization
Primary limitations
- Method dependence. Score-only output lacks traceback
- Input dependence. Short motifs can occur by chance
- Interpretive limit. Significance needs additional context
Local sequence alignment methods
Smith–Waterman resets negative dynamic-programming cells to zero, so the best path can begin and end inside the matrix. The maximum cell gives the optimal local score under the selected parameters.
Database-search methods add heuristics, composition handling, and statistical calibration for large searches. A raw all-pairs score matrix is useful, but it should not be described as an E-value or a complete homology search.
Local sequence alignment applications
Local sequence alignment is best suited to shared domains, motifs, fragments, and partial homology. The result can support comparative review, sequence curation, annotation, profile construction, phylogenetic preparation, structural interpretation, or experimental planning when those downstream uses match the alignment scope.
Keep the alignment as evidence rather than a conclusion. Downstream claims should remain tied to sequence provenance, coverage, method agreement, relevant biological context, and any independent structural, evolutionary, or experimental support.
How to run local sequence alignment online
Use the connected workflow to keep input records, method settings, native outputs, warnings, and exports together. Review every stage before using the result for annotation, phylogeny, variant interpretation, or experimental decisions.
- Define local question. Identify why only a subsection, rather than each full length, should correspond.
- Prepare sequences. Remove avoidable low-complexity or boundary artifacts and verify molecule type.
- Set score model. Record match, mismatch, gap, and normalization choices.
- Calculate scores. Calculate the Smith–Waterman local score matrix for prioritization.
- Trace selected hits. Use an alignment-producing method for selected pairs that need coordinates.
How to interpret local sequence alignment results
Compare scores only under the same algorithm and settings. Sequence length and composition affect raw values, and repetitive or low-complexity segments can create high local scores.
For selected pairs, inspect aligned coordinates, coverage, substitutions, gaps, and biological context. A strong local match may represent one shared domain rather than full-protein homology.
How local sequence alignment works
StringZilla calculates all-pairs Smith–Waterman local scores and explicitly returns scores rather than alignment paths.
- Define local question. Identify why only a subsection, rather than each full length, should correspond.
- Prepare sequences. Remove avoidable low-complexity or boundary artifacts and verify molecule type.
- Set score model. Record match, mismatch, gap, and normalization choices.
- Calculate scores. Calculate the Smith–Waterman local score matrix for prioritization.
- Trace selected hits. Use an alignment-producing method for selected pairs that need coordinates.
Inputs and outputs
Check formats before running, then inspect and download the result from every workflow step.
Inputs
- Alignment input.
FASTAPDBmmCIFProtein or nucleotide FASTA sequences for all-pairs local scoring.
Outputs
- Alignment outputs.
FASTACSVTSVPDBJSONSmith–Waterman local score matrices and downloadable tabular results; no traceback paths.
Tools for local sequence alignment
Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow.
StringZilla v5
Calculate pairwise global, local, or edit-distance score matrices
HMMER
Find homologs with profile hidden Markov models
MMseqs2
Search and cluster large protein or nucleotide sequence sets
MAFFT
Create configurable protein, DNA, or RNA alignments
Clustal Omega
Create scalable protein or nucleotide multiple-sequence alignments
MUSCLE5
Generate conventional or ensemble multiple-sequence alignments
FoldSeek
Find and compare structurally similar proteins
USAlign
Align macromolecular structures and derive residue correspondence
PDB to FASTA converter
Extract sequences from structures before comparison
ANARCI
Number and annotate antibody or T-cell receptor sequences
IgBLAST
Annotate immunoglobulin and T-cell receptor rearrangements
MUMmer4
Align genome assemblies and report coordinates and variants
Other sequence analysis workflows
Compare related approaches based on the molecular system, available evidence, required inputs, and decision you need to support.
Pairwise sequence alignment
Compares two biological sequences and reports their residue-to-residue correspondence or a defined pairwise score.
Multiple sequence alignment
Aligns three or more homologous sequences to identify shared positions, insertions, deletions, and conserved regions.
Global sequence alignment
Compares sequences end to end, including terminal differences and gaps across their full lengths.
Protein sequence alignment
Aligns amino-acid sequences using substitution-aware methods suited to protein evolution and function.
DNA sequence alignment
Aligns nucleotide sequences to compare homologous genes, amplicons, loci, transcripts, or constructs.
Whole genome alignment
Maps large homologous regions between genome assemblies and reports coordinates, rearrangements, and sequence differences.
Structure-based sequence alignment
Uses three-dimensional correspondence to align residues whose sequence similarity alone may be weak.
Frequently asked questions
Protein or nucleotide FASTA sequences for all-pairs local scoring.
Smith–Waterman local score matrices and downloadable tabular results; no traceback paths.
Start from the scientific scope: global or local, pairwise or multiple, sequence or structure, and conventional or genome scale. Then record the method, substitution model, gap settings, sequence type, and any filtering rather than relying on defaults without provenance.
No. Scores and identities quantify similarity under a defined model. Homology is an evolutionary interpretation, and shared function requires additional evidence such as domain context, conserved residues, structure, phylogeny, experiments, or curated annotation.
Retain the score model and never represent a score-only matrix as aligned sequences, coordinates, or statistical significance.
A complete local sequence alignment project is usually quote-based because providers scope sequence curation, method selection, alignment review, interpretation, and downstream analysis together. Harvard’s FY26 bioinformatics core first defines deliverables and a time estimate, then charges $180–$265 per hour; MSU lists $84–$110 per hour and expects at least eight consultant hours for custom analysis.
The total depends on sequence count and length, input cleanup, molecular type, the number of methods compared, manual review, genome scale, figures, phylogenetic or structural follow-up, and whether the deliverable includes interpretation or only alignment files.
ProteinIQ self-service starts at $29 per month for academic Plus and $99 per month for commercial Pro, with the configured local sequence alignment run estimated in credits before submission. Done-for-you analysis is scoped separately and can include data preparation, method comparison, interpretation, and a reproducible handoff.
Start with a workflow you can inspect and edit
Add your inputs, review the settings, and keep every structure, score, table, and file connected to the step that produced it.