adabmDCA

adabmDCA 2.0 — Direct Coupling Analysis in Python

adabmDCA 2.0 trains and analyzes Potts models for Direct Coupling Analysis (DCA). This repository contains the Python implementation, with support for CUDA, Apple Metal, and CPU execution. The “2.0” name refers to the method and paper; Python package releases use their own semantic version numbers.

The package provides command-line tools and a high-level Python API for model training, sequence generation, contact prediction, mutational-effect scoring, entropy estimation, experimental-data reintegration, and alignment processing.

Highlights

• Dense bmDCA and sparse eaDCA, edDCA, and edgeDCA training.
• Automatic detection of standard protein, DNA, and RNA alphabets; explicit custom alphabets remain available for nonstandard data.
• Metropolis and Gibbs sampling with mixing-time estimation.
• Sampling diagnostics for autocorrelation, Pearson correlation, connected correlations, and PCA projections with marginal distributions.
• FASTA and Stockholm input, auditable preprocessing, sequence reweighting, validation alignments, reproducible seeds, and resumable checkpoints.
• Structured result objects with JSON, CSV, FASTA, NumPy, and plot serializers.
• Optional BF16 sampling kernels on supported NVIDIA GPUs.

See the documentation for the complete guides, or open the Colab tutorial notebook for an interactive training and analysis workflow.

Installation

Install the released package with uv:

uv tool install adabmDCA
adabmDCA --help

For use as a Python dependency:

uv add adabmDCA

The equivalent pip installation is also supported:

python -m pip install adabmDCA

To work from source:

git clone https://github.com/spqb/adabmDCApy.git
cd adabmDCApy
uv sync --locked
uv run adabmDCA --help

See the installation guide for development, documentation, Julia, and C++ setup.

Command-line quick start

Train a dense Potts model. The sequence alphabet is detected automatically when it matches a standard protein, DNA, or RNA alphabet:

adabmDCA train \
    --data alignment.fasta \
    --output model \
    --model bmDCA

Generate sequences and save convergence and PCA diagnostics:

adabmDCA sample \
    --path_params model/params.dat \
    --data alignment.fasta \
    --output samples \
    --ngen 1000 \
    --plot

Run adabmDCA --help to list all workflows and adabmDCA <command> --help for command-specific options. The quick-reference page contains more examples.

Python API

The same workflows are available through reusable Python objects:

from adabmDCA import load_model, sample_sequences

model = load_model("model/params.dat", alphabet="protein", device="auto")

energies = model.compute_energies(["ACDEFGHIK", "ACDEYGHIK"])
contacts = model.compute_contact_map()
mutations = model.scan_mutations("ACDEFGHIK").to_dataframe()

result = sample_sequences(
    model=model,
    n_sequences=100,
    n_sweeps=1000,
    reference_fasta="alignment.fasta",
    collect_diagnostics=True,
    seed=42,
)
result.save_bundle("samples", label="family")
result.save_diagnostic_plots("samples", label="family")

The high-level API guide covers training, progress callbacks, input validation, serialization, and structured errors. Low-level tensor functions remain available for numerical workflows.

Documentation and development

Build the documentation locally with:

uv sync --locked --group docs
uv run --group docs mkdocs serve

Run the CPU test suite and lint checks with:

uv run pytest -q
uv run ruff check .

Release verification, including the GPU gate, is described in the release-verification guide.

Citation

If you use adabmDCA in your research, please cite:

Rosset, L., Netti, R., Muntoni, A. P., Weigt, M., & Zamponi, F. (2025). adabmDCA 2.0: A flexible but easy-to-use package for Direct Coupling Analysis. Preprint.

The original implementation is described in Muntoni et al. (2021).

License

adabmDCA is distributed under the Apache License 2.0.