Editing AI for Genomics and Bioinformatics (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Protein secondary structure prediction with a sequence transformer:'''
<syntaxhighlight lang="python">
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

# ESM-2: Facebook's protein language model
# Fine-tuned for secondary structure prediction (Helix/Sheet/Coil per residue)
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
model = AutoModelForTokenClassification.from_pretrained(
    "facebook/esm2_t33_650M_UR50D",
    num_labels=3  # H (helix), E (sheet), C (coil)
)

# Protein sequence
protein = "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDEDRLELEWHQALLRGEMPQTIGGGIGQSRLTMLLLQLPHIGQVQAGVWPAAVRESVPSLL"
inputs = tokenizer(protein, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs.logits.argmax(dim=-1)[0][1:-1]  # Remove CLS/EOS

labels = {0: "H", 1: "E", 2: "C"}
structure = "".join([labels[p.item()] for p in predictions])
print(f"Sequence: {protein[:50]}...")
print(f"Structure: {structure[:50]}...")

# AlphaFold for full 3D structure (use via ColabFold for accessible inference)
# from colabfold.run import run
# run(queries=[("protein", protein)], result_dir="./structures/")
</syntaxhighlight>

; Genomics AI tools and resources
: '''Protein structure''' → AlphaFold2/3 (ColabFold for easy access), ESMFold, RoseTTAFold
: '''Gene expression''' → Enformer (sequence → expression), Geneformer, scGPT
: '''Variant interpretation''' → DeepVariant (variant calling), PrimateAI (pathogenicity)
: '''CRISPR design''' → CRISPR-ML (off-target prediction), DeepCRISPR
: '''Genomics pipelines''' → Bioconductor (R), Scanpy/Seurat (single-cell), GATK (variant calling)
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