--- tags: - BiooBang - Deep Learning - Language Model - Bioinformatics - Synthetic Biology - HEK293T - CDS generation license: cc-by-nc-4.0 --- **BiooBang** is an advanced biological language model designed to integrate protein amino acid sequences and mRNA coding sequences (CDS) within a unified framework. Built on a Transformer-based prefix decoder architecture, BiooBang leverages the principles of natural language processing to treat protein and CDS sequences as biological “languages”, enabling self-supervised learning for comprehensive training. ## Note This model was fine-tuned specifically for codon optimization in the HEK293T cell line. ## Use Case The source code corresponding to this model is publicly available on GitHub at: https://github.com/lonelycrab888/BiooBang After installing BiooBang, you can use: ```python # ========== generate CDS import torch from model.tokenization_UniBioseq import UBSLMTokenizer from model.modeling_UniBioseq import UniBioseqForCausalLM from model.UBL_utils import CodonLogitsProcessor from transformers.generation.logits_process import LogitsProcessorList tokenizer = UBSLMTokenizer.from_pretrained("lonelycrab88/BiooBang-1.0-HEK293T") model = UniBioseqForCausalLM.from_pretrained("lonelycrab88/BiooBang-1.0-HEK293T", device_map='auto') protein_prompt = "MASSDKQTSPKPPPSPSPLRNSKFCQSNMRILIS" input_ids = torch.tensor([tokenizer.encode(input_protein)+[36]]).to(model.device) max_length = 4*len(input_protein)+6 logits_processor = LogitsProcessorList() logits_processor.append(CodonLogitsProcessor(input_protein, tokenizer, len(input_protein))) result = model.generate(input_ids, max_length = max_length, num_beams = 10, logits_processor=logits_processor, low_memory=True, num_return_sequences=1) result_CDS_tok = tokenizer.decode(result[0][len(input_protein)+3:].tolist()).replace(" ","").upper() ``` ## Citing this Work Please cite our paper: ```bibtex @article {Zhao2024.10.24.620004, author = {Zhao, Heng-Rui and Cheng, Meng-Ting and Zhu, Jinhua and Wang, Hao and Yang, Xiang-Rui and Wang, Bo and Sun, Yuan-Xin and Fang, Ming-Hao and Chen, Enhong and Li, Houqiang and Han, Shu-Jing and Chen, Yuxing and Zhou, Cong-Zhao}, title = {Integration of protein and coding sequences enables mutual augmentation of the language model}, elocation-id = {2024.10.24.620004}, year = {2024}, doi = {10.1101/2024.10.24.620004}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/early/2024/10/29/2024.10.24.620004}, eprint = {https://www.biorxiv.org/content/early/2024/10/29/2024.10.24.620004.full.pdf}, journal = {bioRxiv} } ``` ## Contacts If you’re interested in other cell lines and open to collaboration, please don’t hesitate to contact us!