wanglab/protein_catalogue
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--- license: apache-2.0 language: - en tags: - protein - gene-ontology - function-prediction - biology - bioinformatics - reasoning datasets: - wanglab/bioreason-pro-sft-reasoning-data --- <h1 align="center"> 🧬 BioReason-Pro Protein Catalogue<br>Functional Annotations for 223K+ Proteins </h1> <p align="center"> <a href="https://www.biorxiv.org/content/10.64898/2026.03.19.712954v1" target="_blank"><img src="https://img.shields.io/badge/bioRxiv-2026.03.19.712954-FF6B6B?style=for-the-badge&logo=arxiv&logoColor=white" alt="bioRxiv"></a> <a href="https://github.com/bowang-lab/BioReason-Pro"><img src="https://img.shields.io/badge/GitHub-Code-4A90E2?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"></a> <a href="https://bioreason.net"><img src="https://img.shields.io/badge/Website-Online-00B89E?style=for-the-badge&logo=internet-explorer&logoColor=white" alt="Website"></a> <a href="https://huggingface.co/collections/wanglab/bioreason-pro"><img src="https://img.shields.io/badge/HuggingFace-Models & Data-FFBF00?style=for-the-badge&logo=huggingface&logoColor=white" alt="HuggingFace"></a> </p> <br> ## Overview Pre-computed functional annotations for over 223,000 proteins generated by [BioReason-Pro SFT](https://huggingface.co/wanglab/bioreason-pro-sft). Each entry contains a structured reasoning trace, functional summary, UniProt summary, InterPro domain annotations, and Gene Ontology (GO) term predictions across molecular function, biological process, and cellular component. Browse the catalogue interactively at [bioreason.net](https://bioreason.net). ## Dataset Structure | Column | Type | Description | |--------|------|-------------| | `protein_id` | string | Protein identifier (e.g. UniProt accession) | | `protein` | string | Amino acid sequence | | `organism` | string | Source organism | | `model` | string | Model used to generate the annotation (e.g. `SFT`) | | `generation` | string | Full model output including `<think>` reasoning trace and structured functional annotations | ### Generation Format The `generation` column contains the raw model output in the following structure: ``` <think> [Detailed biological reasoning trace] </think> - Functional Summary: [Concise functional description] - UniProt Summary: [Brief UniProt-style summary] - InterPro: - IPR000000: Domain name (type) [start-end] - Molecular Function: - GO:0000000 function name - Biological Process: - GO:0000000 process name - Cellular Component: - GO:0000000 component name ``` ## Usage ```python from datasets import load_dataset ds = load_dataset("wanglab/protein_catalogue", split="train") print(ds[0]) ``` ## See Also - [BioReason-Pro SFT](https://huggingface.co/wanglab/bioreason-pro-sft) — supervised fine-tuned model - [BioReason-Pro RL](https://huggingface.co/wanglab/bioreason-pro-rl) — reinforcement learning optimized checkpoint - [GO-GPT](https://huggingface.co/wanglab/gogpt) — autoregressive GO term predictor - [SFT Reasoning Data](https://huggingface.co/datasets/wanglab/bioreason-pro-sft-reasoning-data) — training data ## Citation If you find this work useful, please cite our papers: ```bibtex @article {Fallahpour2026.03.19.712954, author = {Fallahpour, Adibvafa and Seyed-Ahmadi, Arman and Idehpour, Parsa and Ibrahim, Omar and Gupta, Purav and Naimer, Jack and Zhu, Kevin and Shah, Arnav and Ma, Shihao and Adduri, Abhinav and G{\"u}loglu, Talu and Liu, Nuo and Cui, Haotian and Jain, Arihant and de Castro, Max and Fallahpour, Amirfaham and Cembellin-Prieto, Antonio and Stiles, John S. and Nem{\v c}ko, Filip and Nevue, Alexander A. and Moon, Hyungseok C. and Sosnick, Lucas and Markham, Olivia and Duan, Haonan and Lee, Michelle Y. Y. and Salvador, Andrea F. M. and Maddison, Chris J. and Thaiss, Christoph A. and Ricci-Tam, Chiara and Plosky, Brian S. and Burke, Dave P. and Hsu, Patrick D. and Goodarzi, Hani and Wang, Bo}, title = {BioReason-Pro: Advancing Protein Function Prediction with Multimodal Biological Reasoning}, elocation-id = {2026.03.19.712954}, year = {2026}, doi = {10.64898/2026.03.19.712954}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/early/2026/03/20/2026.03.19.712954}, eprint = {https://www.biorxiv.org/content/early/2026/03/20/2026.03.19.712954.full.pdf}, journal = {bioRxiv} } @misc{fallahpour2025bioreasonincentivizingmultimodalbiological, title={BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model}, author={Adibvafa Fallahpour and Andrew Magnuson and Purav Gupta and Shihao Ma and Jack Naimer and Arnav Shah and Haonan Duan and Omar Ibrahim and Hani Goodarzi and Chris J. Maddison and Bo Wang}, year={2025}, eprint={2505.23579}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2505.23579}, } ```
许可证：Apache-2.0 语言： - 英语 标签： - 蛋白质 - 基因本体（Gene Ontology） - 功能预测 - 生物学 - 生物信息学 - 推理 关联数据集： - wanglab/bioreason-pro-sft-reasoning-data <h1 align="center">🧬 BioReason-Pro 蛋白质目录<br>223,000+ 蛋白质功能注释集</h1> <p align="center"> <a href="https://www.biorxiv.org/content/10.64898/2026.03.19.712954v1" target="_blank"><img src="https://img.shields.io/badge/bioRxiv-2026.03.19.712954-FF6B6B?style=for-the-badge&logo=arxiv&logoColor=white" alt="bioRxiv 预印本"></a> <a href="https://github.com/bowang-lab/BioReason-Pro"><img src="https://img.shields.io/badge/GitHub-代码库-4A90E2?style=for-the-badge&logo=github&logoColor=white" alt="GitHub 代码库"></a> <a href="https://bioreason.net"><img src="https://img.shields.io/badge/网站-上线-00B89E?style=for-the-badge&logo=internet-explorer&logoColor=white" alt="官方网站"></a> <a href="https://huggingface.co/collections/wanglab/bioreason-pro"><img src="https://img.shields.io/badge/HuggingFace-模型与数据集合集-FFBF00?style=for-the-badge&logo=huggingface&logoColor=white" alt="HuggingFace 模型与数据集合集"></a> </p> <br> ## 数据集概览 本数据集包含由[BioReason-Pro SFT](https://huggingface.co/wanglab/bioreason-pro-sft)生成的超过223,000种蛋白质的预计算功能注释。每条数据均包含结构化推理轨迹、功能总结、UniProt（通用蛋白质数据库）总结、InterPro（蛋白质结构域注释数据库）注释，以及覆盖分子功能、生物过程和细胞组分的基因本体（Gene Ontology, GO）术语预测结果。 您可通过[bioreason.net](https://bioreason.net)交互式浏览该目录。 ## 数据集结构 | 列名 | 数据类型 | 描述 | |--------|------|-------------| | `protein_id` | 字符串 | 蛋白质标识符（例如UniProt登录号） | | `protein` | 字符串 | 氨基酸序列 | | `organism` | 字符串 | 来源生物 | | `model` | 字符串 | 用于生成注释的模型（例如`SFT`） | | `generation` | 字符串 | 完整的模型输出，包含`<think>`推理轨迹与结构化功能注释 | ### 生成格式 `generation`列包含原始模型输出，其结构如下： <think> [详细的生物学推理轨迹] </think> - 功能总结：[简洁的功能描述] - UniProt 总结：[简要的UniProt风格总结] - InterPro： - IPR000000：结构域名称（类型）[起始-结束位置] - 分子功能： - GO:0000000 功能名称 - 生物过程： - GO:0000000 过程名称 - 细胞组分： - GO:0000000 组分名称 ## 使用方法 python from datasets import load_dataset ds = load_dataset("wanglab/protein_catalogue", split="train") print(ds[0]) 上述代码为使用datasets库加载该数据集训练分割并打印第一条数据的示例。 ## 相关项目 - [BioReason-Pro SFT](https://huggingface.co/wanglab/bioreason-pro-sft) — 监督微调模型 - [BioReason-Pro RL](https://huggingface.co/wanglab/bioreason-pro-rl) — 强化学习优化的模型 checkpoint - [GO-GPT](https://huggingface.co/wanglab/gogpt) — 自回归GO术语预测器 - [SFT Reasoning Data](https://huggingface.co/datasets/wanglab/bioreason-pro-sft-reasoning-data) — 训练数据集 ## 引用格式 若您认为本工作对您有帮助，请引用以下论文： bibtex @article {Fallahpour2026.03.19.712954, author = {Fallahpour, Adibvafa and Seyed-Ahmadi, Arman and Idehpour, Parsa and Ibrahim, Omar and Gupta, Purav and Naimer, Jack and Zhu, Kevin and Shah, Arnav and Ma, Shihao and Adduri, Abhinav and G{"u}loglu, Talu and Liu, Nuo and Cui, Haotian and Jain, Arihant and de Castro, Max and Fallahpour, Amirfaham and Cembellin-Prieto, Antonio and Stiles, John S. and Nem{v c}ko, Filip and Nevue, Alexander A. and Moon, Hyungseok C. and Sosnick, Lucas and Markham, Olivia and Duan, Haonan and Lee, Michelle Y. Y. and Salvador, Andrea F. M. and Maddison, Chris J. and Thaiss, Christoph A. and Ricci-Tam, Chiara and Plosky, Brian S. and Burke, Dave P. and Hsu, Patrick D. and Goodarzi, Hani and Wang, Bo}, title = {BioReason-Pro: Advancing Protein Function Prediction with Multimodal Biological Reasoning}, elocation-id = {2026.03.19.712954}, year = {2026}, doi = {10.64898/2026.03.19.712954}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/early/2026/03/20/2026.03.19.712954}, eprint = {https://www.biorxiv.org/content/early/2026/03/20/2026.03.19.712954.full.pdf}, journal = {bioRxiv} } @misc{fallahpour2025bioreasonincentivizingmultimodalbiological, title={BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model}, author={Adibvafa Fallahpour and Andrew Magnuson and Purav Gupta and Shihao Ma and Jack Naimer and Arnav Shah and Haonan Duan and Omar Ibrahim and Hani Goodarzi and Chris J. Maddison and Bo Wang}, year={2025}, eprint={2505.23579}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2505.23579}, }
