README.md

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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:
- 1M<n<10M
task_categories:
- text-generation
- question-answering
---

![image.png](./OPI_logo.png)

# Dataset Overview

**Dataset size: Thera are <u>1.64M samples</u>, including <u>training (1,615,661)</u> and <u>testing (26,607)</u> 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

<!-- ## Dataset Description -->

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## 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.

<table border="1" style="text-align:center; border-collapse:collapse;">
  <tr>
    <th style="text-align:center;">Task Type</th>
    <th style="text-align:center;">Type Abbr.</th>
    <th style="text-align:center;">Task Name</th>
    <th style="text-align:center;">Task Abbr.</th>
    <th style="text-align:center;">Training set size</th>
    <th style="text-align:center;">Testing set size</th>
  </tr>
  <tr>
    <td rowspan="3">Sequence Understanding</td>
    <td rowspan="3">SU</td>
    <td>EC Number Prediction</td>
    <td>EC_number</td>
    <td style="text-align:center;">74,487</td>
    <td style="text-align:center;">392 (NEW-392), 149 (Price-149)</td>
  </tr>
  <tr>
    <td>Fold Type Prediction</td>
    <td>Fold_type</td>
    <td style="text-align:center;">12,312</td>
    <td style="text-align:center;">718 (Fold), 1254 (Superfamily), 1272 (Family)</td>
  </tr>
  <tr>
    <td>Subcellular Localization Prediction</td>
    <td>Subcellular_localization</td>
    <td style="text-align:center;">11,230</td>
    <td style="text-align:center;">2,772</td>
  </tr>
  <tr>
    <td rowspan="3">Annotation Prediction</td>
    <td rowspan="3">AP</td>
    <td>Function Keywords Prediction</td>
    <td>Keywords</td>
    <td style="text-align:center;">451,618</td>
    <td style="text-align:center;">184 (CASPSimilarSeq), 1,112 (IDFilterSeq), 4562 (UniprotSeq)</td>
  </tr>
  <tr>
    <td>Gene Ontology(GO) Terms Prediction</td>
    <td>GO</td>
    <td style="text-align:center;">451,618</td>
    <td style="text-align:center;">184 (CASPSimilarSeq), 1,112 (IDFilterSeq), 4562 (UniprotSeq)</td>
  </tr>
  <tr>
    <td>Function Description Prediction</td>
    <td>Function</td>
    <td style="text-align:center;">451,618</td>
    <td style="text-align:center;">184 (CASPSimilarSeq), 1,112 (IDFilterSeq), 4562 (UniprotSeq)</td>
  </tr>
  <tr>
    <td rowspan="3">Knowledge Mining</td>
    <td rowspan="3">KM</td>
    <td>Tissue Location Prediction from Gene Symbol</td>
    <td>gSymbol2Tissue</td>
    <td style="text-align:center;">8,723</td>
    <td style="text-align:center;">2,181</td>
  </tr>
  <tr>
    <td>Cancer Prediction from Gene Symbol</td>
    <td>gSymbol2Cancer</td>
    <td style="text-align:center;">590</td>
    <td style="text-align:center;">148</td>
  </tr>
  <tr>
    <td>Cancer Prediction from Gene Name</td>
    <td>gName2Cancer</td>
    <td style="text-align:center;">590</td>
    <td style="text-align:center;">148</td>
  </tr>
</table>

## 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.