lib310 Python package ![CircleCI](https://camo.githubusercontent.com/e6fa78e86385e3d9699983c2ada6f9763b373f503c2ade3e3bfaf5b1f502eb52/68747470733a2f2f646c2e636972636c6563692e636f6d2f7374617475732d62616467652f696d672f67682f3331302d61692f6c69623331302f747265652f6d61737465722e7376673f7374796c653d736869656c64)
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Protein Functional Annotation
# 1. import lib310
import lib310
# 2. Get Spike SARS2 related proteins from database
seqs = lib310.db.fetch(
name="SPIKE_SARS2",
feature='sequence',
limit=500
)
# 3. Instantiate a pre-trained GO Annotation machine learning model (e.g. TALE)
goa = lib310.ml.GoAnnotation.from_pretrained(model="prot_bert", v="latest")
# 4. Predict!
results = goa.run(seqs)
# 5. Visualization
lib310.plot.umap(results, color='protein_families')
# 1. import lib310
import lib310
# 2. Instantiate a pre-trained Generative Machine Learning model (e.g. GPT3)
lm = lib310.ml.AutoRegressiveLM.from_pretrained(model="ProtGPT3", v="latest")
# 3. Predict!
generated_sequences = lm.run(num_samples=1024)
# 4. Downstream Analysis...
clusters = lib310.tools.cluster(generated_sequences, method='kcluster')
# 5. Visualization
lib310.plot.umap(generated_sequences, clusters)