seqme 0.1.0

Metrics for evaluating biological sequence generation models.

seqme is a modular and highly extendable python library containing model-agnostic metrics for evaluating biological sequences.

Installation

You need to have Python 3.10 or newer installed on your system. To install the base package do:

$ pip install seqme

To also install domain-specific models, add extras specifiers. Read the docs for more information on the supported models.

Quick start

Install seqme and the protein language model, ESM-2.

$ pip install seqme[esm2]

Run in a Jupyter notebook:

import seqme as sm

sequences = {
    "Random": ["MKQW", "RKSPL"],
    "UniProt": ["KKWQ", "RKSPL", "RASD"],
    "HydrAMP": ["MMRK", "RKSPL", "RRLSK", "RRLSK"],
}

cache = sm.ModelCache(
    models={"esm2": sm.models.Esm2(
        model_name="facebook/esm2_t6_8M_UR50D", batch_size=256, device="cpu")
    }
)

metrics = [
    sm.metrics.Uniqueness(),
    sm.metrics.Novelty(reference=sequences["UniProt"], reference_name="UniProt"),
    sm.metrics.FBD(reference=sequences["Random"], embedder=cache.model("esm2")),
]

df = sm.compute_metrics(sequences, metrics)
sm.show_table(df) # Note: Will only display the table in a notebook.

Read the docs for more tutorials and examples.

Citation

Preprint is coming soon.