vdjtools

vdjtools — immune-repertoire analysis

PyPI CI docs python license

TCR/BCR immune-repertoire analysis — a clean-room Python + C++ rewrite of the legacy Groovy/Java vdjtools, standardised on the AIRR schema and polars DataFrames with minimal object-orientation.

Built on the antigenomics ecosystem: seqtree (fuzzy search / e-value engine), vdjmatch (overlap + TCRnet), arda (AIRR annotation + markup repair).

Status: v2.2.0 — the native V(D)J model engine plus the full analytics suite (diversity, overlap/TCRnet, preprocessing, biomarkers, single-cell), CDR features, and legacy-format ingestion (MiXcr, MiGec, immunoSEQ, IMGT/HighV-QUEST, Vidjil, RTCR, TRUST4, arda). Clonotype columns follow the AIRR junction convention (junction_nt / junction_aa). The legacy v1.x tool lives on the legacy-1.x branch and its releases remain available under the repository tags (v0.0.1 … 1.2.1).

Install

pip install vdjtools

Prebuilt wheels ship for CPython 3.10–3.13 on Linux, macOS (Apple Silicon), and Windows; the native _core C++ extension is bundled (the source distribution compiles it on install).

That one command gives you everything vdjtools advertises — no extras to opt into. The three antigenomics engines it delegates to are base dependencies:

Engine Powers
arda the germline reference (V/D/J + CDR3 anchors), model engine, annotation
seqtree fuzzy search / e-values → error correction, similarity overlap, TCRnet
vdjmatch sample overlap, TCRnet, metaclonotypes

So preprocess.correct(), overlap.tcrnet(), biomarker.metaclonotypes() and model.reference.load_germline() all just work from a plain install — and downstream libraries can depend on plain vdjtools and rely on them being there. All three are imported lazily, so import vdjtools stays light; between them they add only requests on top of what vdjtools already needs. arda fetches its IMGT reference once, on first use.

Two things are still optional, because each has a working alternative or is a side integration:

pip install "vdjtools[overlap]"   # scikit-learn — only for cluster_samples(method="mds");
                                  # method="hclust" works out of the box (scipy)
pip install "vdjtools[sc]"        # single-cell extras: anndata (scverse bridge), pyyaml

MMseqs2 is needed only for arda's alignment/annotation path (model.stitch.annotate) — never for germline lookup, Pgen, generation, or the analytics. Install it via conda/brew if you need it.

Development

conda env create -f environment.yml   # python + C++ toolchain + mmseqs2 (arda's aligner)
conda activate vdjtools
pip install -e ".[dev,test]"          # builds the _core C++ extension

The conda env is a convenience, not a requirement — pip install -e ".[dev,test]" in any venv works. It supplies MMseqs2 so the slow arda annotation round-trips in the test suite run too.

Or run the bootstrap script: bash setup.sh --dev-parents --tests.

Quickstart — recombination model engine

Precomputed models for all 7 human loci ship in the wheel — no OLGA or download needed:

from vdjtools.model import load_bundled, native
from vdjtools.model.generate import generate

model = load_bundled("TRB", source="olga")     # or source="learned" (fit to real repertoires)

native.pgen_nt(model, "TGTGCCAGCAGC...")        # nucleotide generation probability (native C++)
native.pgen_aa(model, "CASSLAPGATNEKLFF")       # amino-acid Pgen (codon-marginalised)
native.pgen_aa(model, "CASSLAPGATNEKLFF", mismatches=1)   # + the whole Hamming-1 ball
native.pgen_aa_batch(model, seqs, mismatches=1, threads=0)  # Pgen over many CDR3s, thread-parallel (~11×)
generate(model, 1000)                           # sample a repertoire -> polars DataFrame

Matches OLGA's Pgen to machine precision across all 7 loci, and adds tandem-D (D-D) support that OLGA/IGoR lack. Learn a model from your own non-functional reads (out-of-frame or stop-codon — both escaped selection, which is all a generative model needs) with model.infer.infer_native.

Explore any model's recombination Bayes net interactively (entropy, mutual information, marginals):

pip install "vdjtools[examples]"
marimo edit notebooks/model_explorer.py

Command line

pip install vdjtools installs the vdjtools command — the model engine (OLGA/IGoR-style) and the repertoire analytics (over sample files or a metadata table, like the legacy tool):

# recombination model engine — built-in models for all 7 loci (no download)
vdjtools models                                # list the bundled models
vdjtools generate -m TRB -n 1000 -o gen.tsv    # sample sequences   (cf. olga-generate_sequences)
vdjtools pgen seqs.tsv -m TRB -o pgen.tsv      # Pgen per CDR3       (cf. olga-compute_pgen)
vdjtools pgen seqs.tsv -m TRB --mismatches 1   # + the Hamming-1 ball; --v-col/--j-col to condition

# repertoire analytics — sample files, or a cohort via -m/--metadata + --base-dir
vdjtools diversity      sampleA.tsv sampleB.tsv -o diversity.tsv
vdjtools overlap        *.tsv -o overlap.tsv
vdjtools segment-usage  *.tsv --segment v -o usage.tsv
vdjtools spectratype    *.tsv -o spectra.tsv

Native vdjtools and AIRR Rearrangement inputs are auto-detected; every command writes TSV to -o (or stdout, so it pipes). Run vdjtools <command> --help for options.

Analytics (Python API)

Every reader returns one canonical polars clonotype frame (AIRR junction columns), and every analysis function takes and returns such frames — so results chain together and drop straight into plotting. A tour of the analysis modules (full runnable walkthrough in the User guide):

from vdjtools import io as vio, stats, features, overlap, preprocess

# load (auto-detects MiXcr / immunoSEQ / AIRR / native / … and converts), or a whole cohort:
sample = vio.read("clones.tsv")
cohort = vio.read_samples(vio.read_metadata("metadata.txt"), base_dir="samples/")

# diversity, rarefaction, segment usage, spectratype
stats.diversity_stats(sample)                 # observed, Chao1, Shannon, inverse-Simpson, d50, …
stats.inext(sample, q=(0, 1, 2))              # Hill-number rarefaction/extrapolation + bootstrap CIs
stats.segment_usage(sample, "v")              # V (or "j") usage;  stats.spectratype(sample)

# CDR3 physicochemistry & k-mers
features.physchem_profile(sample, region="all")

# repertoire overlap & TCRnet (fuzzy/similarity/TCRnet via the [overlap] engine)
overlap.overlap_metrics(sampleA, sampleB)     # F / D / Jaccard / Morisita-Horn …
overlap.tcrnet(sample)                         # per-clonotype neighbourhood enrichment

# preprocessing: downsample to a common depth, error-correct, filter, pool
preprocess.downsample(sample, 100_000)
preprocess.correct(preprocess.filter_functional(sample))

# cross-batch V/J-usage bias: batch-correct usage, then resample the clonotype table
usage = preprocess.correct_vj_usage(cohort, batch_col="batch", transform="sigmoid")  # Vlasova 2026
fixed = preprocess.apply_vj_correction(sampleA, usage, sample_id="A0")

Incidence-based clonotype association (Emerson 2017 / Howie 2015 / De Witt 2018 / Vlasova 2026) — a choice of test, condition, and co-occurrence — and single-cell paired-chain Pgen:

from vdjtools import biomarker, sc
from vdjtools.biomarker import association, condition

# feature vs condition: Fisher / chi2 / Bayesian / permutation; binary, per-HLA-allele, or CMH-stratified
association(cohort, condition.binary(meta, "cmv"), test=["fisher", "bayes_bf"])
association(cohort, condition.stratified(meta, "cmv", "hla"), stratum_col="_stratum")  # CMV | HLA (CMH)

# feature vs feature: in-silico α-β pairing / same-chain co-specificity (θ lift + Fisher + FDR)
biomarker.cooccurrence(cohort, chain_a="TRA", chain_b="TRB", evalue=True)

sc.paired_pgen(sc.pair_chains(sc.read_10x("filtered_contig_annotations.csv")))  # pgen_alpha·pgen_beta

Performance

The Pgen / generation / EM / diversity hot paths are a native C++ (pybind11) core; everything else is polars. Amino-acid Pgen matches OLGA to machine precision (1e-15) across all 7 loci while being several times faster, and the built-in models keep the resident set small. Single thread, Apple M3 (arm64), bundled human TRB model:

operation throughput vs OLGA
nucleotide Pgen (single-D VDJ) ~0.5 ms/seq 9×
amino-acid Pgen ~0.6–0.9 ms/seq 8.6×
Pgen + Hamming-1 ball (1 substitution) ~15 ms/seq 8.7×
sequence generation ~32 000 seq/s —

Nucleotide Pgen (via the same transfer-matrix DP as the aa path — an in-frame CDR3 is an aa query with one codon fixed per position) is exact vs OLGA across all loci. Batched Pgen / 1-mismatch over many CDR3s parallelises over sequences (native.pgen_aa_batch, ~11× on 16 cores, bitwise-identical to the serial result); the EM E-step parallelises over reads (~6.7× on 8 threads); diversity/rarefaction run on a native iNEXT kernel (bootstrap + parallel batch). Memory stays light — ~63 MB resident for import vdjtools plus one loaded model, ~123 MB with all seven bundled models resident. Reproduce with ~/vcs/projects/2026-vdjtools-benchmark/bench/bench_pgen.py and the test_*_benchmark.py suites (RUN_BENCHMARK=1).

Capabilities (see the User guide, the API reference, and ROADMAP.md)

  • IO — canonical clonotype frame on AIRR junction columns (junction_nt / junction_aa); readers for native vdjtools, AIRR Rearrangement TSV, and Parquet, plus format-detecting converters for MiXcr (v1/2 + v3/4, incl. C-gene / BCR isotype), MiGec, Adaptive immunoSEQ (v1/v2), IMGT/HighV-QUEST, Vidjil, RTCR, TRUST4, and arda AIRR output (vdjtools.io.convert); metadata-driven batch + hive-partitioned cohorts.
  • Model — native V(D)J recombination model: generation probability (Pgen — nt, aa, 1-mismatch, V/J-agnostic, thread-parallel batch), sequence generation, and EM inference, all in a native (pybind11) core. Supersedes OLGA and IGoR: arda-driven scenario enumeration, polars marginal tables, read-parallelised EM, and tandem-D (D-D) support. Concordant with OLGA across all 7 loci; precomputed OLGA + real-data-learned models bundled (load_bundled).
  • Stats — diversity (Chao1/Shannon/Simpson/…), spectratype, V/J/VJ usage.
  • Features — CDR physicochemical profiles, k-mer / V+k-mer summaries.
  • Overlap — sample overlap and TCRnet (via vdjmatch/seqtree), similarity-aware overlap, clustering.
  • Preprocess — downsampling, error-correction, VJ-usage batch-effect correction, pooling/joining.
  • Biomarker — incidence association (Fisher / χ² / Bayesian / permutation) vs binary / HLA-allele / CMH-stratified conditions; α-β & same-chain co-occurrence pairing; metaclonotypes.
  • Single-cell — AIRR Cell / 10x interoperability, chain pairing + QC, and paired α/β Pgen.

License

GPL-3.0-or-later.

Project links

Data verified by PyPI on Jul 18, 2026
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Credits

Author: Mikhail Shugay

GitHub Statistics

Data verified by PyPI on Jul 18, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

GNU General Public License v3 or later (GPLv3+) (GPL-3.0-or-later)

Requires

Python >=3.10

Provides Extra

model overlap preprocess sc examples oracle test bench docs dev all

Tags

immunology tcr bcr airr repertoire pgen olga igor bioinformatics

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Release history Release notifications | RSS feed

3.10.0

13 files

3.9.3

13 files

3.9.2

13 files

3.9.1

13 files

3.9.0

13 files

3.8.0

13 files

3.7.3

13 files

3.7.0

13 files

3.6.1

13 files

3.6.0

13 files

3.4.0

13 files

3.2.0

13 files

3.1.2

13 files

3.1.1

13 files

3.1.0

13 files

3.0.0

13 files

This release

2.9.0 This release

13 files

2.8.0

13 files

2.7.0

13 files

2.6.0

13 files

2.5.1

13 files

2.3.0

13 files

2.2.1

13 files

2.2.0

13 files

2.1.0

13 files

2.0.0

13 files

Pre-release

2.0.0a2

13 files

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