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entexBERT

Repository containing code for training entexBERT, a fine-tuned DNABERT model with a classifier for the prediction of allele-specific behavior.

The model adds a fully-connected neural net layer on top of the token/sequence embedding of DNABERT to predict whether the single-nucleotide polymorphism located at the center of the window is sensitive to allele-specific effects. The pre-trained weights from DNABERT are used for entexBERT.

Requirements

This project builds off of DNABERT, which should be installed according to the instructions found at the linked repository. Additionally, we use pytorch==1.10.2 and cudatoolkit==11.3.1.

Usage

Save the .py files from this repository in the same directory as the DNABERT training and finetuning scripts. Sample bash scripts for training/fine-tuning, testing, and visualizing model outputs are provided below.

Data

Each of train.txt, test.txt, and dev.txt should contain rows where each row consists of a sequence in kmer format followed by a (0/1) label.

Refer to the sample data directory /model/ in this repository for the format required by the model. The DNABERT repository also provides some sample data

The sample provided in this repository as well as data used for the paper is generated from the EN-TEx data set.

Model

Pre-trained DNABERT models can be found here. Then, fine-tune according to instructions in this section.

Fine-tuning

KMER: 3, 4, 5 or 6

MODEL_PATH: where the pre-trained DNABERT model is located

DATA_PATH: where the train, test, and dev data sets are stored

Set other hyperparameters according to your use case.

python3 entexbert_ft.py \
    --model_type ${model} \
    --tokenizer_name=dna$KMER \
    --model_name_or_path \$MODEL_PATH \
    --task_name dnaprom \
    --do_train \
    --do_eval \
    --do_predict \
    --data_dir \$DATA_PATH \
    --predict_dir \$DATA_PATH \
    --max_seq_length ${seq_len} \
    --per_gpu_eval_batch_size=${batch}   \
    --per_gpu_train_batch_size=${batch}   \
    --learning_rate ${lr} \
    --num_train_epochs ${ep} \
    --output_dir \$OUTPUT_PATH \
    --evaluate_during_training \
    --logging_steps 5000 \
    --save_steps 20000 \
    --warmup_percent 0.1 \
    --hidden_dropout_prob 0.1 \
    --overwrite_output \
    --weight_decay 0.01 \
    --n_process 8 \
    --pred_layer ${layer} \
    --seed ${seed}

Testing

PREDICTION_PATH specifies where you would like to store predictions.

python3 entexbert_ft.py \
    --model_type ${model} \
    --tokenizer_name=dna$KMER \
    --model_name_or_path $MODEL_PATH \
    --task_name dnaprom \
    --do_predict \
    --data_dir $DATA_PATH  \
    --max_seq_length ${seq_len} \
    --per_gpu_pred_batch_size=${batch}   \
    --output_dir $MODEL_PATH \
    --predict_dir $PREDICTION_PATH \
    --n_process 8

Visualization

python3 entexbert_ft.py \
    --model_type ${model} \
    --tokenizer_name=dna$KMER \
    --model_name_or_path $MODEL_PATH \
    --task_name dnaprom \
    --do_visualize \
    --visualize_data_dir $DATA_PATH \
    --visualize_models $KMER \
    --data_dir $DATA_PATH  \
    --max_seq_length ${seq_len} \
    --per_gpu_pred_batch_size=${batch}   \
    --output_dir $MODEL_PATH \
    --predict_dir $PREDICTION_PATH \
    --n_process 8