--- extra_gated_heading: Acknowledge license to accept the repository extra_gated_prompt: > The Beijing Academy of Artificial Intelligence (hereinafter referred to as "we" or "BAAI") provides you with an open-source dataset (hereinafter referred to as "dataset") through the OPI HuggingFace repository (https://huggingface.co/datasets/BAAI/OPI). You can download the dataset you need and use it for purposes such as learning and research while abiding by the usage rules of each original dataset. Before you acquire the open-source dataset (including but not limited to accessing, downloading, copying, distributing, using, or any other handling of the dataset), you should read and understand this "OPI Open-Source Dataset Usage Notice and Disclaimer" (hereinafter referred to as "this statement"). Once you acquire the open-source dataset, regardless of your method of acquisition, your actions will be regarded as acknowledgment of the full content of this statement. 1. 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We may update, adjust the range of the open-source dataset we provide, or suspend, pause, or terminate the open-source dataset service due to business development, third-party cooperation, changes in laws and regulations, and other reasons. extra_gated_fields: Name: text Affiliation: text Country: text I agree to accept the license: checkbox extra_gated_button_content: Acknowledge license license: cc-by-nc-4.0 language: - en tags: - biology - protein - instruction tuning - AI4Science - Life Science - LLM pretty_name: Open Protein Instructions(OPI) size_categories: - 1M1.64M samples, including training (1,615,661) and testing (26,607) sets, in OPI dataset, covering 9 protein-related tasks.** We are excited to announce the release of the **Open Protein Instructions (OPI)** dataset, a curated collection of instructions covering 9 tasks for adapting LLMs to protein biology. The dataset is designed to advance LLM-driven research in the field of protein biology. We welcome contributions and enhancements to this dataset from the community. OPI is the initial part of Open Biology Instructions(OBI) project, together with the subsequent Open Molecule Instructions(OMI), Open DNA Instructions(ODI), Open RNA Instructions(ORI) and Open Single-cell Instructions (OSCI). OBI is a project which aims to fully leverage the potential ability of Large Language Models(LLMs), especially the scientific LLMs like Galactica, to facilitate research in AI for Life Science community. While OBI is still in an early stage, we hope to provide a starting point for the community to bridge LLMs and biological domain knowledge. ## Dataset Update The previous version of OPI dataset is based on the **release 2022_01** of UniProtKB/Swiss-Prot protein knowledgebase. At current, OPI is updated to contain the latest **release 2023_05**, which can be accessed via the dataset file [OPI_updated_160k.json](./OPI_DATA/OPI_updated_160k.json). Reference: - https://ftp.uniprot.org/pub/databases/uniprot/previous_releases/release-2022_01/knowledgebase/UniProtKB_SwissProt-relstat.html - https://ftp.uniprot.org/pub/databases/uniprot/previous_releases/release-2023_05/knowledgebase/UniProtKB_SwissProt-relstat.html ## OPI Dataset Construction Pipeline The OPI dataset is curated on our own by extracting key information from [Swiss-Prot](https://www.uniprot.org/uniprotkb?facets=reviewed%3Atrue&query=%2A) database. The following figure shows the general construction process. ![image.png](./OPI_data.png) ## OPI Dataset Folder Structure The OPI dataset is organized into the three subfolders—AP, KM, and SU—by in the [OPI_DATA](https://huggingface.co/datasets/BAAI/OPI/tree/main/OPI_DATA) directory within this repository, where you can find a subset for each specific task as well as the full dataset file: [OPI_full_1.61M_train.json](https://huggingface.co/datasets/BAAI/OPI/blob/main/OPI_DATA/OPI_full_1.61M_train.json). ``` ./OPI_DATA/ └── SU │ ├── EC_number │ │ ├── test │ │ │ ├── CLEAN_EC_number_new_test.jsonl │ │ │ └── CLEAN_EC_number_price_test.jsonl │ │ └── train │ │ ├── CLEAN_EC_number_train.json │ ├── Fold_type │ │ ├── test │ │ │ └── fold_type_test.jsonl │ │ └── train │ │ └── fold_type_train.json │ └── Subcellular_localization │ ├── test │ │ ├── subcell_loc_test.jsonl │ └── train └── subcell_loc_train.json ├── AP │ └── Keywords │ │ ├── test │ │ │ ├── CASPSimilarSeq_keywords_test.jsonl │ │ │ ├── IDFilterSeq_keywords_test.jsonl │ │ │ └── UniProtSeq_keywords_test.jsonl │ │ └── train │ │ ├── keywords_train.json │ ├── GO │ │ ├── test │ │ │ ├── CASPSimilarSeq_go_terms_test.jsonl │ │ │ ├── IDFilterSeq_go_terms_test.jsonl │ │ │ └── UniProtSeq_go_terms_test.jsonl │ │ └── train │ │ ├── go_terms_train.json │ ├── Function │ ├── test │ │ ├── CASPSimilarSeq_function_test.jsonl │ │ ├── IDFilterSeq_function_test.jsonl │ │ └── UniProtSeq_function_test.jsonl │ └── train │ ├── function_train.json ├── KM └── gSymbol2Tissue │ ├── test │ │ └── gene_symbol_to_tissue_test.jsonl │ └── train │ └── gene_symbol_to_tissue_train.json ├── gSymbol2Cancer │ ├── test │ │ └── gene_symbol_to_cancer_test.jsonl │ └── train │ └── gene_symbol_to_cancer_train.json ├── gName2Cancer ├── test │ └── gene_name_to_cancer_test.jsonl └── train └── gene_name_to_cancer_train.json ``` ## Dataset Examples **An example of OPI training data:** ``` instruction: What is the EC classification of the input protein sequence based on its biological function? input: MGLVSSKKPDKEKPIKEKDKGQWSPLKVSAQDKDAPPLPPLVVFNHLTPPPPDEHLDEDKHFVVALYDYTAMNDRDLQMLKGEKLQVLKGTGDWWLARS LVTGREGYVPSNFVARVESLEMERWFFRSQGRKEAERQLLAPINKAGSFLIRESETNKGAFSLSVKDVTTQGELIKHYKIRCLDEGGYYISPRITFPSL QALVQHYSKKGDGLCQRLTLPCVRPAPQNPWAQDEWEIPRQSLRLVRKLGSGQFGEVWMGYYKNNMKVAIKTLKEGTMSPEAFLGEANVMKALQHERLV RLYAVVTKEPIYIVTEYMARGCLLDFLKTDEGSRLSLPRLIDMSAQIAEGMAYIERMNSIHRDLRAANILVSEALCCKIADFGLARIIDSEYTAQEGAK FPIKWTAPEAIHFGVFTIKADVWSFGVLLMEVVTYGRVPYPGMSNPEVIRNLERGYRMPRPDTCPPELYRGVIAECWRSRPEERPTFEFLQSVLEDFYT ATERQYELQP output: 2.7.10.2 ``` **An example of OPI testing data:** ``` {"id": "seed_task_0", "name": "EC number of price dataset from CLEAN", "instruction": "Return the EC number of the protein sequence.", "instances": [{"input": "MAIPPYPDFRSAAFLRQHLRATMAFYDPVATDASGGQFHFFLDDGTVYNTHTRHLVSATRFVVTHAMLYRTTGEARYQVGMRHALEFLRTAFLDPATGGY AWLIDWQDGRATVQDTTRHCYGMAFVMLAYARAYEAGVPEARVWLAEAFDTAEQHFWQPAAGLYADEASPDWQLTSYRGQNANMHACEAMISAFRATGERR YIERAEQLAQGICQRQAALSDRTHAPAAEGWVWEHFHADWSVDWDYNRHDRSNIFRPWGYQVGHQTEWAKLLLQLDALLPADWHLPCAQRLFDTAVERGWD AEHGGLYYGMAPDGSICDDGKYHWVQAESMAAAAVLAVRTGDARYWQWYDRIWAYCWAHFVDHEHGAWFRILHRDNRNTTREKSNAGKVDYHNMGACYDVL LWALDAPGFSKESRSAALGRP", "output": "5.3.1.7"}], "is_classification": false} ``` ## OPEval: Nine evaluation tasks using the OPI dataset To assess the effectiveness of instruction tuning with the OPI dataset, we developed OPEval, which comprises three categories of evaluation tasks. Each category includes three specific tasks. The table below outlines the task types, names, and the corresponding sizes of the training and testing sets.
Task Type Type Abbr. Task Name Task Abbr. Training set size Testing set size
Sequence Understanding SU EC Number Prediction EC_number 74,487 392 (NEW-392), 149 (Price-149)
Fold Type Prediction Fold_type 12,312 718 (Fold), 1254 (Superfamily), 1272 (Family)
Subcellular Localization Prediction Subcellular_localization 11,230 2,772
Annotation Prediction AP Function Keywords Prediction Keywords 451,618 184 (CASPSimilarSeq), 1,112 (IDFilterSeq), 4562 (UniprotSeq)
Gene Ontology(GO) Terms Prediction GO 451,618 184 (CASPSimilarSeq), 1,112 (IDFilterSeq), 4562 (UniprotSeq)
Function Description Prediction Function 451,618 184 (CASPSimilarSeq), 1,112 (IDFilterSeq), 4562 (UniprotSeq)
Knowledge Mining KM Tissue Location Prediction from Gene Symbol gSymbol2Tissue 8,723 2,181
Cancer Prediction from Gene Symbol gSymbol2Cancer 590 148
Cancer Prediction from Gene Name gName2Cancer 590 148
## License The dataset is licensed under a Creative Commons Attribution Non Commercial 4.0 License. The use of this dataset should also abide by the original [License & Disclaimer](https://www.uniprot.org/help/license) and [Privacy Notice](https://www.uniprot.org/help/privacy) of UniProt.