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. Newer version has been moved to: DEVS/Biosaic, contribute to this repo for further 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
- Operating System: Linux, macOS, or Windows with support for GCC or Clang.
- Python: Version 3.7 or higher.
- 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.
pip install biosaic
git clone https://github.com/shivendrra/biosaic.git
cd biosaicFor 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.
import biosaic from biosaic import get_encodings, get_models print("available models: ", get_models) print("available encodings: ", get_encodings)
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)
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']
[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]
TCTTACATAGAAAGGAGCGGTATTTGGTATGAATTTATTTGCAACTGACTG
TrueThis project is licensed under the MIT License. See the LICENSE file for details.