roychowdhuryresearch
/

dna2vec

@@ -4,197 +4,109 @@ license: mit
 pipeline_tag: sentence-similarity
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 pipeline_tag: sentence-similarity
 ---
+# DNA2Vec: Transformer-Based DNA Sequence Embedding
+This repository provides an implementation of `dna2vec`, a transformer-based model designed for DNA sequence embeddings. It includes both the Hugging Face (`hf_model`) and a locally trained model (`local_model`). The model can be used for DNA sequence alignment, classification, and other genomic applications.
+## Model Overview
+DNA sequence alignment is an essential genomic task that involves mapping short DNA reads to the most probable locations within a reference genome. Traditional methods rely on genome indexing and efficient search algorithms, while recent advances leverage transformer-based models to encode DNA sequences into vector representations.
+The `dna2vec` framework introduces a **Reference-Free DNA Embedding (RDE) Transformer model**, which encodes DNA sequences into a shared vector space, allowing for efficient similarity search and sequence alignment.
+### Key Features:
+- **Transformer-based architecture** trained on genomic data.
+- **Reference-free embeddings** that enable efficient sequence retrieval.
+- **Contrastive loss for self-supervised training**, ensuring robust sequence similarity learning.
+- **Support for Hugging Face and custom-trained local models**.
+- **Efficient search through a DNA vector store**, reducing genome-wide alignment to a local search.
+## Model Details
+### Model Architecture
+The transformer model consists of:
+- **12 attention heads**
+- **6 encoder layers**
+- **Embedding dimension:** 1020
+- **Vocabulary size:** 10,000
+- **Cosine similarity-based sequence matching**
+- **Dropout:** 0.1
+- **Training: Cosine Annealing learning rate scheduling**
+## Installation
+To use the model, install the required dependencies:
+```bash
+pip install transformers torch
+```
+## Usage
+### Load Hugging Face Model
+```python
+from transformers import AutoModel, AutoTokenizer
+import torch
+import torch.nn as nn
+def load_hf_model():
+    hf_model = AutoModel.from_pretrained("roychowdhuryresearch/dna2vec", trust_remote_code=True)
+    hf_tokenizer = AutoTokenizer.from_pretrained("roychowdhuryresearch/dna2vec", trust_remote_code=True)
+    class AveragePooler(nn.Module):
+        def forward(self, last_hidden, attention_mask):
+            return (last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
+    hf_model.pooler = AveragePooler()
+    return hf_model, hf_tokenizer, hf_model.pooler
+```
+###Using the Model
+    Once the model is loaded, you can use it to obtain embeddings for DNA sequences:
+```python
+def get_embedding(dna_sequence):
+    model, tokenizer, pooler = load_hf_model()
+    tokenized_input = tokenizer(dna_sequence, return_tensors="pt")
+    with torch.no_grad():
+        output = model(**tokenized_input)
+    embedding = pooler(output.last_hidden_state, tokenized_input.attention_mask)
+    return embedding.numpy()
+# Example usage
+dna_seq = "ATGCGTACGTAGCTAGCTAGC"
+embedding = get_embedding(dna_seq)
+print("Embedding shape:", embedding.shape)
+```
 ## Training Details
+### Dataset
+The training data consists of DNA sequences sampled from various chromosomes across species. The dataset covers **approximately 2% of the human genome**, ensuring generalization across different sequences. Reads are generated using **ART MiSeq** simulation, with variations in insertion and deletion rates.
 ### Training Procedure
+- **Self-Supervised Learning:** Contrastive loss-based training.
+- **Dynamic Length Sequences:** DNA fragments of length 800-2000 with reads sampled in [150, 500].
+- **Noise Augmentation:** 1-5% random base substitutions in 40% of training reads.
+- **Batch Size:** 16 with gradient accumulation.
 ## Evaluation
+The model was evaluated against traditional aligners (Bowtie-2) and other Transformer-based baselines (DNABERT-2, HyenaDNA). The evaluation metrics include:
+- **Alignment Recall:** >99% for high-quality reads.
+- **Cross-Species Transfer:** Successfully aligns sequences from different species, including *Thermus Aquaticus* and *Rattus Norvegicus*.
+## Citation
+If you use this model, please cite:
+```bibtex
+@article{holur2023dna2vec,
+  title={Embed-Search-Align: DNA Sequence Alignment using Transformer models},
+  author={Holur, Pavan and others},
+  journal={Bioinformatics},
+  year={2023}
+}
+```
+For more details, check the [full paper](https://arxiv.org/abs/2309.11087v6).