Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
DNA construct variants · weighted edit distances
Ubiquitin variants · global similarity scores
σ70 promoter motifs · local cross-comparison
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
DNA construct variants · weighted edit distances
Ubiquitin variants · global similarity scores
σ70 promoter motifs · local cross-comparison
StringZilla v5 is an open-source, high-performance string-processing library developed by Ash Vardanian. It accelerates search, hashing, sorting, edit distances, and biological sequence scoring by using hardware-specific SIMD, SWAR, and GPU backends across modern processor architectures.
ProteinIQ runs StringZilla's CPU-accelerated batch engines for Levenshtein distance, Needleman-Wunsch global scoring, and Smith-Waterman local scoring. These methods are designed for questions that need every comparison in a sequence set or every query-by-candidate combination, such as finding near-duplicate constructs, comparing variant libraries, or building a similarity matrix before clustering.
The result is a numeric matrix, not a set of aligned sequences. StringZilla does not return traceback paths, gap placement, percent identity, E-values, or statistical significance. For an inspectable multiple sequence alignment, use MAFFT or Clustal Omega. For searching a large database while reporting only meaningful hits, MMseqs2 avoids materializing the full cross-product matrix.
Run StringZilla online by submitting FASTA records as one self-comparison set or as separate query and candidate sets. Choose Levenshtein distance, Needleman-Wunsch global scoring, or Smith-Waterman local scoring. ProteinIQ returns every pair in a searchable table, a rectangular matrix CSV, a long-form CSV, and an execution log.
FASTA headers become the sequence identifiers in the output. Sequence lines must be non-empty and cannot contain internal whitespace.
| Mode | Input | Comparisons returned |
|---|---|---|
All pairs within one sequence set | One FASTA set containing one or more records | A square matrix, including the diagonal and both directions of each pair |
Queries against candidates | One query FASTA set and one candidate FASTA set | A rectangular matrix containing every query-candidate combination |
Accepted file extensions are .fasta, .fa, .fas, and .txt. A run may contain at most 100,000 sequence symbols across the active inputs and at most 100,000 matrix cells. A self comparison of 100 sequences therefore returns 10,000 rows, while 500 queries against 200 candidates reaches the 100,000-cell limit.
StringZilla treats the submitted sequence characters as symbols. Protein, DNA, and RNA records can be compared in the same tool, but molecule type is not inferred and biologically incompatible alphabets are not rejected. Mixed alphabets produce numeric results that usually have no useful biological interpretation.
| Algorithm | What it measures | Best suited to | Direction |
|---|---|---|---|
Levenshtein edit distance | Minimum cost of substitutions, insertions, and deletions needed to transform one sequence into another | Near-duplicate detection, construct verification, barcode comparison, and short variant sets | Lower is more similar |
Needleman-Wunsch global score | Best end-to-end alignment score under the selected match, mismatch, and affine-gap values | Sequences expected to correspond across their full lengths | Higher is more similar |
Smith-Waterman local score | Best-scoring pair of local subsequences | Shared motifs or domains within otherwise different sequences | Higher is more similar |
Levenshtein distance is the easiest choice when each change should have a direct cost. Needleman-Wunsch is more appropriate when terminal differences and internal gaps must affect a full-length comparison. Smith-Waterman ignores poorly matching flanks by selecting the strongest local region, so a high score can reflect one shared segment rather than overall sequence similarity.
| Setting | Range | Default | Effect |
|---|---|---|---|
Match cost | -128 to 127 | 0 | Cost assigned when the two symbols are equal |
Mismatch cost | -128 to 127 | 1 | Cost of substituting one symbol for another |
Gap-open cost | -128 to 127 | 1 | Cost of starting an insertion or deletion run |
Gap-extension cost | -128 to 127 | 1 | Cost of extending an insertion or deletion run |
All four costs must be integers because the StringZilla engine stores them as signed 8-bit values.
The defaults produce the familiar unit-cost edit distance: identical sequences have distance 0, and each substitution, insertion, or deletion increases the distance. Changing the four values creates a weighted edit cost. Negative costs are accepted by the engine, but the result no longer behaves like a conventional mathematical distance, so zero and lower values no longer have their usual interpretation.
| Setting | Range | Default | Effect |
|---|---|---|---|
Match score | -128 to 127 | 1 | Score on the diagonal of the identity-style substitution table |
Mismatch score | -128 to 127 | 0 | Score for every unequal symbol pair |
Gap-open score | -128 to 127 | -1 | Penalty for starting a gap |
Gap-extension score | -128 to 127 | -1 | Penalty for extending a gap |
All four scores must be integers because the StringZilla engine stores them as signed 8-bit values.
These controls define a compact identity-style substitution table. Every exact symbol match receives the same score, and every mismatch receives the same score. Protein substitutions therefore do not receive BLOSUM or PAM values, and transitions and transversions in nucleotide sequences are not distinguished. Needleman-Wunsch and Smith-Waterman runs support at most 32 distinct encoded symbols across all submitted sequences.
The default scoring rewards exact matches, leaves mismatches neutral, and penalizes gaps. Raising Match score increases the influence of exact identity. Making Mismatch score more negative discourages mismatched regions, while stronger negative gap values make insertions and deletions less favorable. The current tool does not accept a custom substitution matrix.
The Pairwise results table preserves query-major, candidate-minor order. It is not sorted by similarity, because every cell in the matrix is returned.
| Column | Description |
|---|---|
Query # | One-based position of the query record in the submitted FASTA |
Query ID | Text following the query FASTA header marker |
Candidate # | One-based position of the candidate record |
Candidate ID | Candidate FASTA identifier |
Edit distance | Weighted Levenshtein distance, present for Levenshtein runs |
Alignment score | Global or local alignment score, present for Needleman-Wunsch and Smith-Waterman runs |
Three files accompany the table:
stringzilla-distances.csv or stringzilla-scores.csv: Long-form rows matching the displayed results tablestringzilla-matrix.csv: The same values arranged with queries as rows and candidates as columnsstringzilla-run.log: Algorithm, comparison mode, sequence counts, matrix size, runtime, and detected CPU capabilitiesWith the default costs, a distance of 0 means the two sequences are identical. A distance of 1 means that one substitution, insertion, or deletion separates them. Raw distance grows with sequence length, so distance 5 is a much larger difference for a 20-residue peptide than for a 1,000-residue protein.
For default unit costs, a useful length-adjusted summary is:
Here, is Edit distance, and and are the two sequence lengths. The value ranges from 0 to 1 for standard unit-cost comparisons, with 1 indicating identical sequences. It should not be applied unchanged after assigning unusual or negative costs.
Needleman-Wunsch compares both sequences end to end. A high score indicates that many symbols can be matched while paying relatively few mismatch and gap penalties. Length strongly affects the result, so raw scores are most useful when all sequences have similar lengths and every run uses the same settings.
The diagonal of a self-comparison matrix gives each sequence scored against itself. Comparing an off-diagonal score with the corresponding diagonal values can provide context, but it is not a calibrated probability or percent identity. A lower score can result from substitutions, terminal length differences, internal indels, or a combination of all three.
Smith-Waterman reports the best local score. Unrelated flanking regions do not need to participate, which makes the method useful for detecting a shared motif or domain. The score alone does not reveal where that region occurs or how long it is. Two pairs can receive the same score from very different local segments.
Because StringZilla returns no traceback, a promising pair should be aligned with a method that reports aligned residues before making positional or mechanistic claims. MAFFT is suitable when the selected sequences belong in a common multiple alignment; Clustal Omega is another scalable option for homologous sets.
StringZilla was developed by Ash Vardanian as a high-performance string-processing library. Its StringZillas batch engines calculate Levenshtein matrices and Needleman-Wunsch or Smith-Waterman score matrices across collections of sequences. ProteinIQ runs the CPU backend and lets the library select the available SIMD or SWAR capabilities automatically.
Dynamic-programming sequence comparison normally fills a matrix whose cells depend on neighboring cells. StringZilla traverses independent anti-diagonals so multiple cells can be evaluated together with vector instructions. The batch interface also parallelizes work across many sequence pairs, which is particularly useful for complete self and cross comparisons.
Acceleration changes how quickly the score matrix is computed, not what the score means. Levenshtein still minimizes edit cost, Needleman-Wunsch still scores a global comparison, and Smith-Waterman still selects a local one. The output contains the final value for every sequence pair rather than each algorithm's internal dynamic-programming matrix.
| Goal | Better starting point | Reason |
|---|---|---|
| Compare every member of a moderate sequence set | StringZilla | Returns the complete pairwise matrix with no hit filtering |
| Find near-identical constructs or barcodes | StringZilla with Levenshtein | Edit counts have a direct interpretation under unit costs |
| Score full-length correspondence | StringZilla with Needleman-Wunsch | Both sequence ends participate in the global score |
| Detect a shared local segment | StringZilla with Smith-Waterman | Poorly matching flanks can be excluded from the score |
| Inspect residue correspondence and gap placement | MAFFT or Clustal Omega | These tools return aligned sequences rather than scores alone |
| Search a large sequence database for homologs | MMseqs2 | Prefiltering avoids calculating and storing every possible pair and adds hit statistics |
Complete matrices scale as in self mode and in cross mode. StringZilla is therefore most useful when every cell is genuinely needed. For a million-sequence database search, a filtered search method is both more practical and more informative than a full matrix.
Perform multiple sequence alignment using MAFFT (Multiple Alignment using Fast Fourier Transform). Supports multiple algorithms from fast progressive to highly accurate iterative methods.
Align protein, DNA, or RNA sequences with MUSCLE v5.3 using the native PPP or Super5 algorithm, with optional stratified and diversified ensembles.
USAlign (Universal Structure Alignment) aligns protein, RNA, and DNA structures to compute TM-scores and generate superposed structures. Compare 3D structures to assess structural similarity.
Perform multiple sequence alignment on protein or nucleotide sequences using the Clustal Omega algorithm.
Infer approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences.
Build phylogenetic trees using maximum likelihood with automatic model selection (ModelFinder) and ultrafast bootstrap support.
Ultra-fast sequence search and clustering. 10,000x faster than BLAST for database searches, with powerful sequence clustering capabilities for proteins and nucleotides.
Rapidly align and compare DNA sequences using MUMmer4 nucmer. Perform pairwise genome comparisons to identify SNPs, indels, and structural variants between reference and query genomes.
Analyze immunoglobulin (antibody) and T cell receptor variable domain sequences. Identifies V/D/J gene segments, delineates CDR regions, and analyzes rearrangement junctions.
Fast protein structure search, comparison, and clustering. Search your structure against 200M+ AlphaFold predictions, compare 2 structures, or cluster up to 2500.
StringZilla v5 is an open-source, high-performance string-processing library developed by Ash Vardanian. It accelerates search, hashing, sorting, edit distances, and biological sequence scoring by using hardware-specific SIMD, SWAR, and GPU backends across modern processor architectures.
ProteinIQ runs StringZilla's CPU-accelerated batch engines for Levenshtein distance, Needleman-Wunsch global scoring, and Smith-Waterman local scoring. These methods are designed for questions that need every comparison in a sequence set or every query-by-candidate combination, such as finding near-duplicate constructs, comparing variant libraries, or building a similarity matrix before clustering.
The result is a numeric matrix, not a set of aligned sequences. StringZilla does not return traceback paths, gap placement, percent identity, E-values, or statistical significance. For an inspectable multiple sequence alignment, use MAFFT or Clustal Omega. For searching a large database while reporting only meaningful hits, MMseqs2 avoids materializing the full cross-product matrix.
Run StringZilla online by submitting FASTA records as one self-comparison set or as separate query and candidate sets. Choose Levenshtein distance, Needleman-Wunsch global scoring, or Smith-Waterman local scoring. ProteinIQ returns every pair in a searchable table, a rectangular matrix CSV, a long-form CSV, and an execution log.
FASTA headers become the sequence identifiers in the output. Sequence lines must be non-empty and cannot contain internal whitespace.
| Mode | Input | Comparisons returned |
|---|---|---|
All pairs within one sequence set | One FASTA set containing one or more records | A square matrix, including the diagonal and both directions of each pair |
Queries against candidates | One query FASTA set and one candidate FASTA set | A rectangular matrix containing every query-candidate combination |
Accepted file extensions are .fasta, .fa, .fas, and .txt. A run may contain at most 100,000 sequence symbols across the active inputs and at most 100,000 matrix cells. A self comparison of 100 sequences therefore returns 10,000 rows, while 500 queries against 200 candidates reaches the 100,000-cell limit.
StringZilla treats the submitted sequence characters as symbols. Protein, DNA, and RNA records can be compared in the same tool, but molecule type is not inferred and biologically incompatible alphabets are not rejected. Mixed alphabets produce numeric results that usually have no useful biological interpretation.
| Algorithm | What it measures | Best suited to | Direction |
|---|---|---|---|
Levenshtein edit distance | Minimum cost of substitutions, insertions, and deletions needed to transform one sequence into another | Near-duplicate detection, construct verification, barcode comparison, and short variant sets | Lower is more similar |
Needleman-Wunsch global score | Best end-to-end alignment score under the selected match, mismatch, and affine-gap values | Sequences expected to correspond across their full lengths | Higher is more similar |
Smith-Waterman local score | Best-scoring pair of local subsequences | Shared motifs or domains within otherwise different sequences | Higher is more similar |
Levenshtein distance is the easiest choice when each change should have a direct cost. Needleman-Wunsch is more appropriate when terminal differences and internal gaps must affect a full-length comparison. Smith-Waterman ignores poorly matching flanks by selecting the strongest local region, so a high score can reflect one shared segment rather than overall sequence similarity.
| Setting | Range | Default | Effect |
|---|---|---|---|
Match cost | -128 to 127 | 0 | Cost assigned when the two symbols are equal |
Mismatch cost | -128 to 127 | 1 | Cost of substituting one symbol for another |
Gap-open cost | -128 to 127 | 1 | Cost of starting an insertion or deletion run |
Gap-extension cost | -128 to 127 | 1 | Cost of extending an insertion or deletion run |
All four costs must be integers because the StringZilla engine stores them as signed 8-bit values.
The defaults produce the familiar unit-cost edit distance: identical sequences have distance 0, and each substitution, insertion, or deletion increases the distance. Changing the four values creates a weighted edit cost. Negative costs are accepted by the engine, but the result no longer behaves like a conventional mathematical distance, so zero and lower values no longer have their usual interpretation.
| Setting | Range | Default | Effect |
|---|---|---|---|
Match score | -128 to 127 | 1 | Score on the diagonal of the identity-style substitution table |
Mismatch score | -128 to 127 | 0 | Score for every unequal symbol pair |
Gap-open score | -128 to 127 | -1 | Penalty for starting a gap |
Gap-extension score | -128 to 127 | -1 | Penalty for extending a gap |
All four scores must be integers because the StringZilla engine stores them as signed 8-bit values.
These controls define a compact identity-style substitution table. Every exact symbol match receives the same score, and every mismatch receives the same score. Protein substitutions therefore do not receive BLOSUM or PAM values, and transitions and transversions in nucleotide sequences are not distinguished. Needleman-Wunsch and Smith-Waterman runs support at most 32 distinct encoded symbols across all submitted sequences.
The default scoring rewards exact matches, leaves mismatches neutral, and penalizes gaps. Raising Match score increases the influence of exact identity. Making Mismatch score more negative discourages mismatched regions, while stronger negative gap values make insertions and deletions less favorable. The current tool does not accept a custom substitution matrix.
The Pairwise results table preserves query-major, candidate-minor order. It is not sorted by similarity, because every cell in the matrix is returned.
| Column | Description |
|---|---|
Query # | One-based position of the query record in the submitted FASTA |
Query ID | Text following the query FASTA header marker |
Candidate # | One-based position of the candidate record |
Candidate ID | Candidate FASTA identifier |
Edit distance | Weighted Levenshtein distance, present for Levenshtein runs |
Alignment score | Global or local alignment score, present for Needleman-Wunsch and Smith-Waterman runs |
Three files accompany the table:
stringzilla-distances.csv or stringzilla-scores.csv: Long-form rows matching the displayed results tablestringzilla-matrix.csv: The same values arranged with queries as rows and candidates as columnsstringzilla-run.log: Algorithm, comparison mode, sequence counts, matrix size, runtime, and detected CPU capabilitiesWith the default costs, a distance of 0 means the two sequences are identical. A distance of 1 means that one substitution, insertion, or deletion separates them. Raw distance grows with sequence length, so distance 5 is a much larger difference for a 20-residue peptide than for a 1,000-residue protein.
For default unit costs, a useful length-adjusted summary is:
Here, is Edit distance, and and are the two sequence lengths. The value ranges from 0 to 1 for standard unit-cost comparisons, with 1 indicating identical sequences. It should not be applied unchanged after assigning unusual or negative costs.
Needleman-Wunsch compares both sequences end to end. A high score indicates that many symbols can be matched while paying relatively few mismatch and gap penalties. Length strongly affects the result, so raw scores are most useful when all sequences have similar lengths and every run uses the same settings.
The diagonal of a self-comparison matrix gives each sequence scored against itself. Comparing an off-diagonal score with the corresponding diagonal values can provide context, but it is not a calibrated probability or percent identity. A lower score can result from substitutions, terminal length differences, internal indels, or a combination of all three.
Smith-Waterman reports the best local score. Unrelated flanking regions do not need to participate, which makes the method useful for detecting a shared motif or domain. The score alone does not reveal where that region occurs or how long it is. Two pairs can receive the same score from very different local segments.
Because StringZilla returns no traceback, a promising pair should be aligned with a method that reports aligned residues before making positional or mechanistic claims. MAFFT is suitable when the selected sequences belong in a common multiple alignment; Clustal Omega is another scalable option for homologous sets.
StringZilla was developed by Ash Vardanian as a high-performance string-processing library. Its StringZillas batch engines calculate Levenshtein matrices and Needleman-Wunsch or Smith-Waterman score matrices across collections of sequences. ProteinIQ runs the CPU backend and lets the library select the available SIMD or SWAR capabilities automatically.
Dynamic-programming sequence comparison normally fills a matrix whose cells depend on neighboring cells. StringZilla traverses independent anti-diagonals so multiple cells can be evaluated together with vector instructions. The batch interface also parallelizes work across many sequence pairs, which is particularly useful for complete self and cross comparisons.
Acceleration changes how quickly the score matrix is computed, not what the score means. Levenshtein still minimizes edit cost, Needleman-Wunsch still scores a global comparison, and Smith-Waterman still selects a local one. The output contains the final value for every sequence pair rather than each algorithm's internal dynamic-programming matrix.
| Goal | Better starting point | Reason |
|---|---|---|
| Compare every member of a moderate sequence set | StringZilla | Returns the complete pairwise matrix with no hit filtering |
| Find near-identical constructs or barcodes | StringZilla with Levenshtein | Edit counts have a direct interpretation under unit costs |
| Score full-length correspondence | StringZilla with Needleman-Wunsch | Both sequence ends participate in the global score |
| Detect a shared local segment | StringZilla with Smith-Waterman | Poorly matching flanks can be excluded from the score |
| Inspect residue correspondence and gap placement | MAFFT or Clustal Omega | These tools return aligned sequences rather than scores alone |
| Search a large sequence database for homologs | MMseqs2 | Prefiltering avoids calculating and storing every possible pair and adds hit statistics |
Complete matrices scale as in self mode and in cross mode. StringZilla is therefore most useful when every cell is genuinely needed. For a million-sequence database search, a filtered search method is both more practical and more informative than a full matrix.
Perform multiple sequence alignment using MAFFT (Multiple Alignment using Fast Fourier Transform). Supports multiple algorithms from fast progressive to highly accurate iterative methods.
Align protein, DNA, or RNA sequences with MUSCLE v5.3 using the native PPP or Super5 algorithm, with optional stratified and diversified ensembles.
USAlign (Universal Structure Alignment) aligns protein, RNA, and DNA structures to compute TM-scores and generate superposed structures. Compare 3D structures to assess structural similarity.
Perform multiple sequence alignment on protein or nucleotide sequences using the Clustal Omega algorithm.
Infer approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences.
Build phylogenetic trees using maximum likelihood with automatic model selection (ModelFinder) and ultrafast bootstrap support.
Ultra-fast sequence search and clustering. 10,000x faster than BLAST for database searches, with powerful sequence clustering capabilities for proteins and nucleotides.
Rapidly align and compare DNA sequences using MUMmer4 nucmer. Perform pairwise genome comparisons to identify SNPs, indels, and structural variants between reference and query genomes.
Analyze immunoglobulin (antibody) and T cell receptor variable domain sequences. Identifies V/D/J gene segments, delineates CDR regions, and analyzes rearrangement junctions.
Fast protein structure search, comparison, and clustering. Search your structure against 200M+ AlphaFold predictions, compare 2 structures, or cluster up to 2500.