This model, BacteriaTIS-DNABERT-K6-89M, is a DNA sequence classifier based on DNABERT trained for Translation Initiation Site (TIS) classification in bacterial genomes. It operates on 6-mer tokenized sequences derived from a 60 bp window (30 bp upstream + 30 bp downstream) around the TIS. The model was fine-tuned using 89M trainable parameters.

Model Details

Base Model: DNABERT
Task: Translation Initiation Site (TIS) Classification
K-mer Size: 6
Input Sequence Window: 60 bp (30 upstream + 30 downstream) of TIS site in ORF sequence
Number of Trainable Parameters: 89M
Max Sequence Length: 512
Precision Used: AMP (Automatic Mixed Precision)

Install Dependencies

Ensure you have transformers and torch installed:

pip install torch transformers

Load Model & Tokenizer

python

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load Model
model_checkpoint = "Genereux-akotenou/BacteriaTIS-DNABERT-K6-89M"
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

Inference Example

To classify a TIS, extract a 60 bp sequence window (30 bp upstream + 30 bp downstream) of the TIS codon site and convert it to 6-mers:

python

def generate_kmer(sequence: str, k: int, overlap: int = 1):
    """Generate k-mer encoding from DNA sequence."""
    return " ".join([sequence[j:j+k] for j in range(0, len(sequence) - k + 1, overlap)])

# Example TIS-centered sequence (60 bp window)
sequence = "AGAACCAGCCGGAGACCTCCTGCTCGTACATGAAAGGCTCGAGCAGCCGGGCGAGGGCGG"
seq_kmer = generate_kmer(sequence, k=6)

Run Model

python

# Tokenize input
inputs = tokenizer(
  seq_kmer,
  return_tensors="pt",
  max_length=tokenizer.model_max_length,
  padding="max_length",
  truncation=True
)

# Run inference
with torch.no_grad():
  outputs = model(**inputs)
  logits = outputs.logits
  predicted_class = torch.argmax(logits, dim=-1).item()