sylphy 0.2.0

Protein sequence representation: encoders, embeddings, and reductions.

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

Sylphy supports Python 3.11 and 3.12.

pip install sylphy

Install optional extras as needed:

• embeddings for PyTorch and Transformers-based embedding extraction
• parquet for Parquet export support
• 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,parquet]'
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 pandas as pd
from sylphy.sequence_encoder import create_encoder

df = pd.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 pandas as pd
from sylphy.embedding_extractor import create_embedding

df = pd.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

sylphy get-embedding \
  --model facebook/esm2_t6_8M_UR50D \
  --input-data sequences.csv \
  --sequence-identifier sequence \
  --output embeddings.parquet \
  --device cuda --precision fp16 --batch-size 16

sylphy encode-sequences \
  --encoder one_hot \
  --input-data sequences.csv \
  --sequence-identifier sequence \
  --output encoded.csv

sylphy cache stats

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

License

GPL-3.0-only. 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.