seqtree

Fast fuzzy search over biological sequences (amino-acid or nucleotide), as a C++ core with a minimal Python binding. Build an immutable index once, then search single queries or massive batches in parallel.

Install

pip install seqtree       # prebuilt wheels for CPython 3.10–3.13

Prebuilt wheels cover Linux x86-64, macOS arm64 (Apple Silicon), and Windows x86-64. There are no Intel/x86-64 macOS wheels — Intel Macs build from source (see below), which just needs a C++20 compiler and CMake (pulled in automatically by the build).

Quickstart

import seqtree

idx = seqtree.Index.build(["CASSLAPGATNEKLFF", "CASSLELGATNEKLFF"], alphabet="aa")

p = seqtree.SearchParams(max_subs=2, engine="seqtm")
for hit in idx.search("CASSLAPGATNEKLFF", p):
    print(hit.ref_id, hit.score, hit.n_subs)

# parallel batch (releases the GIL)
results = idx.search_batch(queries, p, threads=0)   # 0 = all cores

# matrix-weighted budget. Name seqtrie -- engine="auto" always means seqtm.
# gap_open must follow the matrix: 2 * blosum62.scale() == 28, not the default 1.
pm = seqtree.SearchParams(matrix="blosum62", max_penalty=12, engine="seqtrie", gap_open=28)
top = idx.search_top("CASSLAPGATNEKLFF", pm, k=5)

# alignment on demand
aln = idx.align(0, "CASSLELGATNEKLFF", p)
print(aln.aligned_query, aln.aligned_ref, aln.ops)

# batch-vs-batch (auto-indexes the larger set)
pairs = seqtree.pairwise_batch(query_set, db_set, p, alphabet="aa")

# a short query against a long TEXT (a proteome), one index for every query length
tix = seqtree.TextIndex.build(proteome_records, alphabet="aa", k=4)
res = tix.search_batch(peptides, max_subs=2, threads=0)
for hit in res[0]:
    print(hit.ref_id, hit.offset, hit.n_subs, hit.mismatches)

That is the whole core loop. Significance, gap blocks, alignment and distances are in More examples below, and the docs explain the why.

Which piece do I need?

Results are payload-agnostic — (ref_id, score, n_subs, n_ins, n_dels). Downstream libraries map ref_id back to their own payloads (V gene, MHC, counts) and filter there.

Two search engines over one trie:

• seqtm — branch-and-bound enumeration. Exact per-type edit caps (max_subs / max_ins / max_dels) and a fast Hamming-only path. Best for small edit distances (UMI collapse, error correction, CDR3/epitope matching).
• seqtrie — full-width edit-distance DP carried down the trie. Honours the max_penalty score budget only; it ignores the per-type edit caps. Use it when the budget is the whole specification.

engine="auto" always picks seqtm, because it is the only engine that enforces the caps you asked for — seqtrie runs only when you name it.

Beyond search, seqtree ships:

• Substitution matrices — built-in identity, BLOSUM45, BLOSUM62, BLOSUM80, PAM250, PAM100, and structural — a Miyazawa–Jernigan interaction-strength matrix: each residue's strength q(a)=mean_b e(a,b) is read off the MJ contact potential, so substitutions between residues of like interaction strength are cheap. It separates strong (hydrophobic F W C L Y M I V) from weak (polar/charged S Q D E K) interactors — the strong/weak-interactor axis of TCR-recognition models (Košmrlj et al., PNAS 2008; MJ contact energies from Miyazawa & Jernigan, J Mol Biol 1996) — letting dissimilar-but-chemically-equivalent loops align. Plus custom matrices via SubstitutionMatrix.from_similarity (Gram penalty s(a,a)+s(b,b)−2·s(a,b)).
• Text search — TextIndex does exact k-mismatch (Hamming) search over a concatenated text — a proteome, a genome — where Index would need one build per query length. The human proteome has 68,389,335 nine-mer windows, so a query set spanning 45 distinct lengths costs 45 multi-gigabyte builds; here k belongs to the index and one build answers every length and every max_subs. Exact, not heuristic: one search scheme — b = min(m+1, L/k) disjoint blocks, block j probed at radius c_j, lossless exactly when Σc_j ≥ m − b + 1 — with completeness pinned by brute-force set equality over L 6–30 × max_subs 0–3 × k ∈ {3,4,5}, and by the answer being identical across k. On the human proteome (69,578,135 residues) one thread answers a 9-mer within 2 substitutions in 1.3 ms and a 15-mer within 3 in 1.5 ms. max_indels adds insertions and deletions on the same index and the same scheme — with max_subs + max_indels + 1 blocks the edit count is still below the block count, so pigeonhole leaves a zero-error block and the unchanged exact seed lookup finds gapped occurrences too; only verification becomes a banded DP. Results come back as flat CSR arrays with zero-copy numpy views, mismatches as (pos, query_aa, text_aa) pairs, an optional fold onto caller-supplied group ids that makes a tie explicit, and a cap that is always reported.
• E-values / significance — calibrate hit counts against a background control repertoire (load_control + evalues), the TCRNET approach on a finite-sample footing. See the E-value guide.
• Calibrated cutoffs — threshold_for_evalue inverts the E-value into the score cutoff that achieves it, per query. A fixed cutoff is not a calibrated one: a control repertoire is dense near germline and sparse among rare junctions, so the same threshold buys a common query far more chance neighbours than a rare one.
• Gap-block alignment — gapblock restricts alignment to one contiguous indel, which is the right model for a V(D)J junction and, measured against unrestricted affine alignment, is exactly optimal on 98.8% of genuinely related pairs at a calibrated gap_open. A gap prior (central_prior, profile_prior, frame_prior) chooses where the block goes — a sequence score alone cannot. score_matrix scores a whole query set against a whole reference set in one GIL-released C++ call (532 M pairs/s on 16 cores; numpy.asarray wraps the result with no copy), the shape a prototype-distance embedding needs.
• Pairwise alignment without BioPython — seqtree.pairwise is Needleman–Wunsch (mode="global") and Smith–Waterman (mode="local") with affine or linear gaps, on the raw log-odds scale. It is a drop-in for Bio.Align.PairwiseAligner — verified against it as an oracle across three matrices, ten gap/mode settings and sixty sequence shapes with zero disagreements — and 65–87× faster, since there is no Python in the per-pair loop. dist_matrix gives d = s(a,a) + s(b,b) − 2·s(a,b) directly. BioPython is a test-only dependency; seqtree still needs nothing at runtime.
• Plain edit distances — seqtree.distance is unweighted hamming and levenshtein (unit costs, no matrix, no alphabet) for when you just need a number, not a scored alignment. hamming_matrix / levenshtein_matrix score a whole set against a whole set in one GIL-released C++ call (numpy.asarray wraps the result with no copy) — no python-Levenshtein or rapidfuzz dependency needed. Hamming requires equal lengths (it raises otherwise); comparison is case-sensitive. The same module enumerates a Hamming ball as well as scoring one: neighbourhood(seq, r) lists its 19·L + 1 members, and neighbourhood_union(seqs, r) takes the union over many centres with each distinct sequence emitted once — deduplicated during the walk, so the Σ 19·L_i multiset never exists. For a tight specificity group that is a 41.7% saving, not a rounding correction.
• Island profiles — IslandProfile.fit builds a position weight matrix over a set of frame-aligned junctions (an island) and scores a query column by column against the island consensus, as a non-negative penalty that flows through threshold_for_evalue unchanged. At a repertoire-scale cutoff it recovers 48.5% of held-out members against 37.6% for min-over-members; at a loose cutoff the two are indistinguishable, so it earns its keep only where the cutoff is strict.

More examples

import numpy as np
import seqtree
from seqtree.pairwise import align, score, dist_matrix

mat = seqtree.SubstitutionMatrix.blosum62()

# E-values against a background control repertoire (TCRNET-style significance)
control = seqtree.load_control("human_trb_aa", size=1_000_000)
target = seqtree.Index.build(vdjdb_cdr3s, alphabet="aa")
for q, r in zip(queries, seqtree.evalues(target, control, queries, p)):
    if r["p_enrichment"] < 1e-3:
        print(q, r["E"], r["n_target"], r["n_control"])

# ...and the cutoff that achieves a target E, per query (-1 = unreachable at this control size)
ceiling = seqtree.SearchParams(max_subs=14, max_penalty=50, matrix="BLOSUM62", engine="seqtm")
thetas = seqtree.threshold_for_evalue(target, control, queries, ceiling, e_target=0.05)

# one contiguous gap block, placed by a prior rather than by the score alone
from seqtree.gapblock import GapBlockIndex, central_prior, embed_in_frame

gbi = GapBlockIndex(cdr3s, "aa", d_max=2)
for ref_id, score, block_len, block_pos in gbi.search(
        "CASSLGQAYEQYF", 40, mat, gap_open=2 * mat.scale(),
        gap_prior=central_prior(int(1.5 * mat.scale()))):
    ...

# a fixed frame column makes gap placement transitive -- and a column index, hence a PWM, possible
embed_in_frame("CASSGQAYEQYF", width=14, c=4)      # 'CASS--GQAYEQYF'

# a whole query set vs a whole reference set, in one GIL-released C++ call
from seqtree.gapblock import score_matrix, IslandProfile
sm = score_matrix(clonotypes, prototypes, mat, gap_open=2 * mat.scale(), threads=0)
distances = np.asarray(sm)                          # (n_clonotypes, n_prototypes) int32, zero-copy

# a position weight matrix over an island, still a non-negative penalty (feeds threshold_for_evalue)
profile = IslandProfile.fit(island_members)
profile.score("CASSLGQAYEQYF")                      # 0 on the consensus, > 0 for deviations

# ordinary pairwise alignment -- Needleman-Wunsch / Smith-Waterman, no BioPython
score("CASSLGQAYEQYF", "CASSPGQAYEQF", mat)                    # global, BLAST defaults (11/1)
score("WWWAAAWWW", "KKKAAAKKK", mat, mode="local")             # Smith-Waterman
score("AAA", "AAAAA", mat, gap_open=5, gap_extend=5)           # linear gaps: open == extend
aln = align("CASSLGQAYEQYF", "CASSPGQAYEQF", mat)              # + aligned strings and ops

d = np.asarray(dist_matrix(v_genes, v_genes, mat, threads=0))  # s(a,a)+s(b,b)-2s(a,b), zero diagonal

# plain edit distances -- unweighted Hamming / Levenshtein, no matrix, no dependency
from seqtree.distance import hamming, levenshtein, hamming_matrix, levenshtein_matrix
hamming("CASSLGQYF", "CASSPGQYF")                             # 1  (equal length only)
levenshtein("kitten", "sitting")                             # 3
h = np.asarray(hamming_matrix(umis, umis, threads=0))        # (len, len) int32, zero-copy

# enumerate the ball, deduplicated across centres (substitution only, fixed length)
from seqtree.distance import neighbourhood, neighbourhood_union, union_size
neighbourhood("CASSLGQYF")                                   # 172 = 19*9 + 1
union_size(junctions)                                        # size the job before running it
for variant in neighbourhood_union(junctions, r=1):          # each distinct sequence once
    ...

Build from source

Needs uv (brew install uv); setup.sh uses it for the venv and the editable install.

bash setup.sh            # uv-managed .venv + editable install
bash setup.sh --tests    # + pytest
bash setup.sh --bench    # + benchmark deps (huggingface_hub)

Tests

cmake -S . -B build -G Ninja -DSEQTREE_TESTS=ON
cmake --build build
ctest --test-dir build           # C++ unit tests
pytest tests/python              # Python tests

Benchmarks

python bench/bench_gnuplot.py        # throughput / scaling / matrix / collisions → SVG (needs gnuplot)
python bench/bench.py                # recall vs ground truth (real VDJdb data)
python bench/bench_evalue.py         # true E-value benchmark (target vs background control)
python bench/bench_evalue_matrix.py  # significance across reference/control/query/scope grid
python bench/bench_epitope.py        # epitope detection-complexity (GIL vs NLV)
python bench/bench_gapblock.py       # the gap-freedom ladder: fixed centre → prior → flat → affine
python bench/bench_score_matrix.py   # dense batch gap-block throughput (µs/pair, M pairs/s, RSS)

Figures (throughput, scaling, matrix-scoring overhead, collisions, E-value matrix, epitope detection) and the full methodology are in the benchmarks docs. Set RUN_BENCHMARK=1 for the large tiers.

Development

This repo follows git-flow:

• master — stable, release-ready; CI + docs deploy run here.
• dev — integration branch for day-to-day work.
• feature branches branch off dev and merge back via PR; releases merge dev → master.

Roadmap (affine gaps, position-specific matrices, succinct memory packing) lives in docs/roadmap.rst. Control-set E-values already ship — see the E-value guide.