Biosaic

Overview

Biosaic(Bio-Mosaic) is a tokenizer library built for Enigma2. It contains: Tokenizer, Embedder for DNA & Amino Acid Protein Sequences. Has a VQ-VAE & Evoformer architecture based encoders that could convert sequences into embeddings and vice-versa for model training use-case.

Features

• Tokenization: converts the sequences into K-Mers. (for DNA only)
• Encoding: converts sequences into embeddings for classification, training purposes.
• Easy use: it's very basic and easy to use library.
• SoTA encoder: Evoformer & VQ-VAE model are inspired from the AlphaFold-2

Prerequisites

System Requirements

• Operating System: Linux, macOS, or Windows with support for GCC or Clang.
• Python: Version 3.7 or higher.

Dependencies

• Python Modules:
  • pickle: for loading and saving model files.
  • os: for file and path handling.
  • urllib: for loading the vocabs from repo.
  • tempfile: for loading the vocabs from repo.
  • torch: for using the encoders.

Installation

1. From PyPI:

	pip install biosaic

2. Clone the Repo:

	git clone https://github.com/shivendrra/biosaic.git
	cd biosaic

Usage

For now, KMer tokenizer for DNA sequences works properly with no issue so far. VQ-VAE model for DNA sequence tokenization is currently been trained, will take some time to test and deploy. Evoformer is next in queue for training.

Fetch Encodings/Models

import biosaic
from biosaic import get_encodings, get_models

print("available models: ", get_models)
print("available encodings: ", get_encodings)

Output

available models:  ['dna-perchar', 'enigma1', 'EnBERT', 'enigma2']
available encodings:  ['base_1k', 'base_2k', 'base_3k', 'base_4k', 'base_5k']

Create an instance of the tokenizer with a specified k-mer size, & split them into tokens, encode & decode them fastly:

import biosaic
from biosaic import tokenizer

token = tokenizer(encoding=get_encodings[2])
print(token.vocab_size)

sequence = "TCTTACATAGAAAGGAGCGGTATTTGGTATGAATTTATTTGCAACTGACTG"
encoded = token.encode(sequence)
decoded = token.decode(encoded)
tokenized = token.tokenize(sequence)

print(tokenized)
print(encoded[:100])
print(decoded[:300])
print(decoded == sequence)

Output

84

['TCT', 'CTT', 'TTA', 'TAC', 'ACA', 'CAT', 'ATA', 'TAG', 'AGA', 'GAA', 'AAA', 'AAG', 'AGG', 'GGA', 'GAG', 'AGC', 'GCG', 'CGG', 'GGT', 'GTA', 'TAT', 'ATT', 'TTT', 'TTG', 'TGG', 'GGT', 'GTA', 'TAT', 'ATG', 'TGA', 'GAA', 'AAT', 'ATT', 'TTT', 'TTA', 'TAT', 'ATT', 'TTT', 'TTG', 'TGC', 'GCA', 'CAA', 'AAC', 'ACT', 'CTG', 'TGA', 'GAC', 'ACT', 'CTG', 'TG', 'G']

[75, 51, 80, 69, 24, 39, 32, 70, 28, 52, 20, 22, 30, 60, 54, 29, 58, 46, 63, 64, 71, 35, 83, 82, 78, 63, 64, 71, 34, 76, 52, 23, 35, 83, 80, 71, 35, 83, 82, 77, 56, 36, 21, 27, 50, 76, 53, 27, 50, 18, 2]

TCTTACATAGAAAGGAGCGGTATTTGGTATGAATTTATTTGCAACTGACTG

True

Contributing

1. Fork the repository.

2. Create a feature branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add feature"

4. Push to the branch:

   git push origin feature-name

5. Create a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.