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H2dddhxh/
GenomeClip

Name: GenomeClip
Rating: 2.5 (5 reviews)

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GenomeClip: CLIP-style Dual-Encoder for DNA Sequence and Text Embeddings

GenomeClip is a contrastive learning model that aligns DNA sequence embeddings (from AlphaGenome) with text embeddings (from GenePT / OpenAI text-embedding-3-large) into a shared 512-dimensional space.

Model Overview

Property	Value
Architecture	Dual-tower (CLIP-style)
Sequence encoder	Linear projection → Transformer encoder → mean pooling → 512-d
Text encoder	Linear projection → MLP → 512-d
Input sequence dim	3072 (AlphaGenome `embeddings_128bp`)
Input text dim	3072 (OpenAI `text-embedding-3-large`)
Output dim	512 (L2-normalized)
Parameters	58.8M

How It Works

GenomeClip is a second-stage alignment model. It does not process raw DNA sequences or raw text directly. Instead, it takes pre-computed embeddings from upstream foundation models and projects them into a shared space:

DNA sequence "ATCG..."
    → AlphaGenome (embeddings_128bp)
    → per-gene tokens (L, 3072)
    → L2-normalize each token               ← important preprocessing step
    → GenomeClip sequence encoder
    → (512,) L2-normalized embedding
                                              ↕ cosine similarity
Gene description "This gene encodes..."
    → OpenAI text-embedding-3-large
    → (3072,) vector
    → GenomeClip text encoder
    → (512,) L2-normalized embedding

The two encoders are completely independent (no shared weights, no cross-attention), so you can encode sequences and text separately and compare them later via cosine similarity.

Quick Start

python

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained(
    "your-username/GenomeClip-v1",
    trust_remote_code=True,
)
model.eval()

# Encode DNA sequence embeddings (from AlphaGenome)
# seq_emb: (batch, num_tokens, 3072) — L2-normalized per token
# seq_lengths: (batch,) — number of valid tokens per sample
seq_emb = torch.randn(2, 50, 3072)
seq_emb = torch.nn.functional.normalize(seq_emb, dim=-1)  # L2-norm each token
seq_lengths = torch.tensor([50, 35])

with torch.no_grad():
    seq_repr = model.encode_sequence(seq_emb, seq_lengths)  # (2, 512)

# Encode text embeddings (from OpenAI text-embedding-3-large)
text_emb = torch.randn(2, 3072)

with torch.no_grad():
    text_repr = model.encode_text(text_emb)  # (2, 512)

# Cross-modal similarity
similarity = seq_repr @ text_repr.t()  # (2, 2) cosine similarity matrix
print(similarity)

Input Format

Sequence Embeddings

Source: AlphaGenome embeddings_128bp (3072-dim per 128bp window)
Shape: (batch, L, 3072) where L = ceil(gene_length_bp / 128)
Preprocessing: L2-normalize each token before feeding to GenomeClip
seq_emb = torch.nn.functional.normalize(seq_emb, dim=-1)
Also accepts pooled input (batch, 3072) (auto-expanded to L=1)

Text Embeddings

Source: OpenAI text-embedding-3-large applied to NCBI gene summaries (following the GenePT methodology)
Shape: (batch, 3072)
No preprocessing needed — use the embedding as-is from the API

Sequence Lengths

Shape: (batch,) — number of valid (non-padding) tokens per sample
Optional. If not provided, all positions are assumed valid

Usage Patterns

Encode sequence only

seq_repr = model.encode_sequence(seq_emb, seq_lengths) # (B, 512)

Encode text only

text_repr = model.encode_text(text_emb) # (B, 512)

Encode both and compute contrastive loss

python

out = model(
    seq_embeddings=seq_emb,
    text_embeddings=text_emb,
    seq_lengths=seq_lengths,
)
# out.seq_repr:  (B, 512)
# out.text_repr: (B, 512)
# out.loss:      scalar (symmetric InfoNCE)
# out.logits:    (B, B) similarity matrix

Cross-modal retrieval

python

# Pre-compute all gene embeddings (do this once)
all_seq_reprs = []
for batch in seq_dataloader:
    with torch.no_grad():
        all_seq_reprs.append(model.encode_sequence(batch["seq"], batch["lengths"]))
all_seq_reprs = torch.cat(all_seq_reprs)  # (N_genes, 512)

# Query: find genes matching a text description
query_text_repr = model.encode_text(query_text_emb)  # (1, 512)
similarities = query_text_repr @ all_seq_reprs.t()    # (1, N_genes)
top_matches = similarities.argsort(descending=True)[0, :10]

Upstream Model Setup

AlphaGenome (for DNA sequence embeddings)

pip install alphagenome-research

python

# See https://huggingface.co/google/alphagenome-all-folds for full setup
from alphagenome_research.model.one_hot_encoder import DNAOneHotEncoder

encoder = DNAOneHotEncoder()
one_hot = encoder.encode(dna_sequence)  # (seq_len, 4)
# ... run AlphaGenome model ...
# Extract: result.embeddings_128bp → (L, 3072)

GenePT text embeddings (via OpenAI API)

python

from openai import OpenAI

client = OpenAI()
response = client.embeddings.create(
    input="BRCA1 DNA repair associated. This gene encodes a nuclear phosphoprotein...",
    model="text-embedding-3-large",
)
text_emb = response.data[0].embedding  # list of 3072 floats

Citation

If you use GenomeClip in your research, please cite:

bibtex

@misc{genomeclip2025,
  title={GenomeClip: Contrastive Alignment of DNA Sequence and Text Embeddings},
  year={2025},
}

License

Apache 2.0

H2dddhxh/GenomeClip

GenomeClip: CLIP-style Dual-Encoder for DNA Sequence and Text Embeddings

Model Overview

How It Works

Quick Start

Input Format

Sequence Embeddings

Text Embeddings

Sequence Lengths

Usage Patterns

Encode sequence only

Encode text only

Encode both and compute contrastive loss

Cross-modal retrieval

Upstream Model Setup

AlphaGenome (for DNA sequence embeddings)

GenePT text embeddings (via OpenAI API)

Citation

License

H2dddhxh/
GenomeClip