Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/opendilab/di-bioseq
Decision Intelligence platform for Biological Sequence Searching
https://github.com/opendilab/di-bioseq
biological-data-analysis biological-sequence-analysis reinforcement-learning
Last synced: about 2 months ago
JSON representation
Decision Intelligence platform for Biological Sequence Searching
- Host: GitHub
- URL: https://github.com/opendilab/di-bioseq
- Owner: opendilab
- License: apache-2.0
- Created: 2022-03-31T12:51:26.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-10T06:37:35.000Z (about 2 years ago)
- Last Synced: 2023-03-05T11:18:50.287Z (almost 2 years ago)
- Topics: biological-data-analysis, biological-sequence-analysis, reinforcement-learning
- Language: Python
- Homepage:
- Size: 95.4 MB
- Stars: 95
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DI-bioseq
[](https://twitter.com/opendilab)
[](https://github.com/opendilab/DI-bioseq/stargazers)
[](https://github.com/opendilab/DI-bioseq/network)
[](https://github.com/opendilab/DI-bioseq/blob/master/LICENSE)
## Introduction
**DI-bioseq** is an open-source Decision Intelligence platform for biological sequence prediction and searching.
DI-bioseq provides a Reinforcement Learning pipeline for **biological sequence searching**, including DNA, RNA, and amino acid sequences.
DI-bioseq uses [**DI-engine**](https://github.com/opendilab/DI-engine), an RL platform to build searching methods.
DI-bioseq is an application platform under [**OpenDILab**](http://opendilab.org/).
DI-bioseq was developed referencing [FLEXS](https://github.com/samsinai/FLEXS) on biological environments and modules.
## Outline
- [Introduction](#introduction)
- [Installation](#installation)
- [Features](#features)
- [Quick Start](#quick-start)
- [Contributing](#contributing)
- [License](#license)
- [Citation](#citation)
## Introduction
Biological sequence searching problems aims to find a sequence with a fixed length of these elements that has the best characteristics that humans desired, represented by a score. The search usually starts from a series of known sequences whose scores are measured via biological experiments.
Considering the high cost and time consumption of real biological experiments required for verifying the results, the searching procedure is expected to be high-efficient, with fewer scores referred to from biological experiments, rather than proposing lots of candidates and iteration many rounds to find the best one.
Biological sequence searching pipelines often consists of the following modules:
- A biological ground-truth data/simulator that can get the fitness score of a proposed sequence, always called **Landscape**
- A prediction **Model** that attempts to fit the ground-truth landscape from known score
- An **Encoder** to convert a sequence into numbers
- An **Engine** which is the core method to find new sequence proposals from known ones and scores
## Installation
**DI-bioseq** together with **DI-engine** can be easily installed from source via `pip`.
```bash
git clone https://github.com/opendilab/DI-bioseq.git
cd di-bioseq
pip install -e . --user
```
## Features
**DI-bioseq** currently supports the following searching modules.
| Landscape | Engine | Model | Encoder |
| ---- | ---- | ---- | ---- |
| GB1 | Random | Linear Regression | Onehot |
| TF-Binding | Off-policy PPO(onehot only) | Random Forest Regression | Georgiev(protein only) |
| | AdaLead | | |
## Quick Start
**DI-bioseq** provides a standard searching pipeline and entry that can be configured by arguments.
```bash
usage: main.py [-h] [--landscape LANDSCAPE] [--engine ENGINE] [--model MODEL]
[--encoder ENCODER] [--score SCORE] [--predict PREDICT]
[--round ROUND] [--logdir LOGDIR]
optional arguments:
-h, --help show this help message and exit
--landscape LANDSCAPE
landscape type
--engine ENGINE searching engine type
--model MODEL prediction model type
--encoder ENCODER encoder type
--score SCORE max score in landscape each round
--predict PREDICT max prediction in model each round
--round ROUND searching rounds
--logdir LOGDIR result logging folder path
```
## Join and Contribute
We appreciate all contributions to improve DI-bioseq, both algorithms and system designs. Welcome to OpenDILab community! Scan the QR code and add us on Wechat:
Or you can contact us with [slack](https://opendilab.slack.com/join/shared_invite/zt-v9tmv4fp-nUBAQEH1_Kuyu_q4plBssQ#/shared-invite/email) or email ([email protected]).
## License
DI-bioseq released under the Apache 2.0 license.
## Citation
@misc{bioseq,
title={{DI-bioseq: OpenDILab} Decision Intelligence platform for biological sequence prediction and searching},
author={DI-bioseq Contributors},
publisher = {GitHub},
howpublished = {\url{`https://github.com/opendilab/DI-bioseq`}},
year={2021},
}