Sylphy 🧬

Sylphy is a Python toolkit for turning protein sequences into machine-learning-ready representations.

It covers three main workflows:

• Classical sequence encoders: one-hot, ordinal, frequency, k-mers, physicochemical, FFT
• Embedding extraction from pretrained protein models: ESM2, ProtT5, ProtBERT, Ankh2, Mistral-Prot, ESM-C
• Dimensionality reduction for downstream analysis and visualization

Installation

pip install sylphy

Install optional extras as needed:

• embeddings for PyTorch and Transformers-based embedding extraction
• reductions for UMAP and related optional reducers
• all for all optional runtime dependencies

The reductions extra may require a C++ compiler and Python development headers because of optional native dependencies such as ClustPy.

pip install 'sylphy[embeddings]'
pip install 'sylphy[all]'

On Debian or Ubuntu systems, install the build prerequisites with:

sudo apt-get install build-essential python3-dev

On Fedora or RHEL systems:

sudo dnf install gcc gcc-c++ python3-devel

Quick Start

Classical sequence encoding:

import polars as pl
from sylphy.sequence_encoder import create_encoder

df = pl.DataFrame({"sequence": ["MKTAYIAKQR", "GAVLIMPFWK", "PEPTIDE"]})

encoder = create_encoder(
    "one_hot",  # or: ordinal, kmers, frequency, physicochemical, fft
    dataset=df,
    sequence_column="sequence",
)
encoder.run_process()
encoded = encoder.coded_dataset

Embedding extraction:

import polars as pl
from sylphy.embedding_extractor import create_embedding

df = pl.DataFrame({"sequence": ["MKTAYIAKQR", "GAVLIMPFWK", "PEPTIDE"]})

embedder = create_embedding(
    model_name="facebook/esm2_t6_8M_UR50D",
    dataset=df,
    column_seq="sequence",
    name_device="cuda",
    precision="fp16",  # fp32, fp16, or bf16
)

embedder.run_process(batch_size=8, pool="mean")  # mean, cls, or eos
embeddings = embedder.coded_dataset
embedder.export_encoder("embeddings.parquet")

Dimensionality reduction:

from sylphy.reductions import reduce_dimensionality

model, reduced = reduce_dimensionality(
    method="pca",  # pca, truncated_svd, umap, tsne, isomap, etc.
    dataset=embeddings,
    n_components=2,
    random_state=42,
)

CLI

sylphy --help

# Extract embeddings (ESM2, GPU, fp16)
sylphy embed \
  -i sequences.csv -o embeddings.parquet \
  -m facebook/esm2_t6_8M_UR50D -d cuda -p fp16 -b 16

# Extract embeddings (ProtT5, last 4 layers averaged)
sylphy embed \
  -i sequences.csv -o embeddings.npy \
  -m Rostlab/prot_t5_xl_uniref50 -d cuda -p bf16 --layers last4 --layer-agg mean

# Classical encoding — one-hot
sylphy encode --method one_hot -i sequences.csv -o encoded.parquet

# Classical encoding — physicochemical (AAIndex)
sylphy encode --method physicochemical \
  -i sequences.csv -o phys.parquet --name-property ARGP820101

# Classical encoding — k-mers TF-IDF
sylphy encode --method kmers -i sequences.csv -o kmers.csv -k 4

# Cache management
sylphy cache stats
sylphy cache ls --recursive
sylphy cache prune --max-size 10GB --apply

Configuration

By default Sylphy stores cache data in the platform cache directory:

• Linux: ~/.cache/sylphy
• macOS: ~/Library/Caches/sylphy
• Windows: %LOCALAPPDATA%\\sylphy\\Cache

Useful environment variables:

• SYLPHY_CACHE_ROOT to override the cache location
• SYLPHY_DEVICE to force cpu or cuda
• SYLPHY_MODEL_<NAME> to override a registered model path

Learn More

• DEVELOPMENT.md for local setup, tests, architecture, and contribution notes
• examples/README.md for the examples index and runnable scripts/notebooks

Citation

If you use Sylphy in your research, please cite:

Medina-Ortiz, D.; Álvarez Saravia, D.; García-Vinuesa, J. Sylphy: Protein Sequence Representation: Encoders, Embeddings, and Reductions. Version 1.0.0, 2026. Zenodo. https://doi.org/10.5281/zenodo.21819735

@software{sylphy2026,
  author    = {Medina-Ortiz, David and Álvarez Saravia, Diego and García-Vinuesa, Julián},
  title     = {{Sylphy}: Protein Sequence Representation: Encoders, Embeddings, and Reductions},
  year      = {2026},
  version   = {1.0.0},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.21819735},
  url       = {https://doi.org/10.5281/zenodo.21819735}
}

To cite this exact release, use the version DOI 10.5281/zenodo.21819736. Machine-readable metadata lives in CITATION.cff.

License

MIT. See LICENSE.

Acknowledgements

Built with the Hugging Face Transformers ecosystem, the Meta ESM-C SDK, and the broader scientific Python stack including scikit-learn, PyTorch, UMAP, and ClustPy.

Developed by KREN AI Lab at Universidad de Magallanes, Chile.