Bio Dataset Manager: easily encode biological sequences into tensors

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Authors:

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- Fabio Bove | fabio.bove.dr@gmail.com
- Eugenio Bertolini |

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What is it?

Bio Data Manager is a Python project designed for managing and processing bio-sequence data, including DNA, proteins, and SMILES strings. This tool facilitates the encoding of these sequences into tensors, which can then be used for AI computations and complex model implementations.

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Project Structure

• bio_data_manager/: Contains core modules for bioinformatics sequence processing and management.
• bio_sequences/: Handles various operations related to biological sequences such as DNA and protein.

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Installation

1. Install it as a library
   • Using CPU:

     pip install bio-dataset-manager

   • Using CUDA:

     pip install bio-dataset-manager[cuda] -f https://download.pytorch.org/whl/torch_stable.html

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Usage

Examples of the code can be found in the examples folder.

1. import the modules

import torch
from bio_dataset_manager.bio_dataloader import BioDataloader
from bio_dataset_manager.bio_dataset import BioDataset
from bio_sequences.dna_sequence import DnaSequence

2. create the dataset and dataloader

dataset = BioDataset(
        dataset_folder="path/to/dataset",
        sequences_limit=10,
        randomize_choice=True,
        pad_same_len=False,
        window_size=1,
        sequence_info=DnaSequence(),
        sequences=None,
    )

dataloader = BioDataloader(
        dataset=dataset,
        batch_size=5,
        shuffle=True,
        collate_fn=dataset.collate_fn,
        split_ratio=0.5,
        use_gpu=True if torch.cuda.is_available() else False
    )

3. training loop example

epochs = 5
for epoch in range(epochs):
    with tqdm(total=len(dataloader.training_dataloader), desc=f"Epoch {epoch + 1}/{epochs}", unit="batch") as pbar:
        for batch in dataloader.training_dataloader:
            y_real, lengths = dataloader.process_batch(batch)
            time.sleep(0.1)
            pbar.update(1)
            pbar.set_postfix(
                loss_gen=f"0.0",
                loss_dis=f"0.0"
            )
    pbar.refresh()

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Contributing

Feel free to submit issues or pull requests if you'd like to contribute to this project.

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License