Deploy

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: check-yaml
         stages: [commit]
@@ -29,7 +29,7 @@ repos:
       - id: cargo-check
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: "v0.4.2"
+    rev: "v0.6.9"
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix, --unsafe-fixes]
@@ -81,7 +81,7 @@ repos:
 
   # jupyter notebook linting
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.8.5
+    rev: 1.8.7
     hooks:
       - id: nbqa-black
         args: ["--line-length=99"]

Cargo.toml

@@ -2,12 +2,23 @@
 name = "deepchopper"
 version = "0.1.0"
 edition = "2021"
+authors = ["Yangyang Li <[email protected]>"]
+description = "A Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing"
+documentation = "https://docs.rs/bitflags"
+homepage = "https://serde.rs"
+repository = "https://github.com/rust-lang/cargo"
+license = "Apache-2.0"
+readme = "README.md"
 
 [lib]
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 name = "deepchopper"
 crate-type = ["cdylib", "rlib"]
 
+[[bin]]
+path = "src/bin/predict.rs"
+name = "deepchopper-chop"
+
 [profile.dev]
 debug = 1
 

README.md

@@ -1,15 +1,21 @@
-# <img src="./documentation/logo.webp" alt="logo" height=100> **DeepChopper** [![social](https://img.shields.io/github/stars/ylab-hi/DeepChopper?style=social)](https://github.com/ylab-hi/DeepChopper/stargazers)
+# <img src="./documentation/logo.webp" alt="logo" height="100"/> **DeepChopper** [![social](https://img.shields.io/github/stars/ylab-hi/DeepChopper?style=social)](https://github.com/ylab-hi/DeepChopper/stargazers)
 
 <!--toc:start-->
 
-- [Feature](#feature)
-- [🤜 Contribution](#%F0%9F%A4%9C-contribution)
+- [ **DeepChopper** ](#-deepchopper-)
+  - [Install](#install)
+  - [Usage](#usage)
+    - [Command-Line Interface](#command-line-interface)
+    - [Library](#library)
+  - [Cite](#cite)
+  - [🤜 Contribution](#-contribution)
+    - [Build Environment](#build-environment)
+    - [Install Dependencies](#install-dependencies)
 
 <!--toc:end-->
 
-Language models identify chimeric artificial reads in NanoPore direct-RNA sequencing data.
-DeepChopper leverages language model to accurately detect and chop these aritificial sequences which may cause chimeric reads, ensuring higher quality and more reliable sequencing results.
-By integrating seamlessly with existing workflows, DeepChopper provides a robust solution for researchers and bioinformaticians working with NanoPorea direct-RNA sequencing data.
+DeepChopper leverages language model to accurately detect and chop artificial sequences which may cause chimeric reads, ensuring higher quality and more reliable sequencing results.
+By integrating seamlessly with existing workflows, DeepChopper provides a robust solution for researchers and bioinformatics working with NanoPore direct-RNA sequencing data.
 
 ## Install
 
@@ -33,37 +39,71 @@ DeepChopper can be installed using pip, the Python package installer. Follow the
 4. Verify the installation:
 
    ```bash
-   deepchopper --version
+   deepchopper --help
    ```
 
+5. DeepChopper include a Rust command line tool for faster performance.
+
+```bash
+cargo install deepchopper
+```
+
 For GPU support, ensure you have CUDA installed on your system, then install the GPU version:
 
 ```bash
 pip install deepchopper[gpu]
 ```
 
-Note: If you encounter any issues,
-please check our GitHub repository for troubleshooting guides or to report a problem.
+Note: If you encounter any issues, please check our GitHub repository for troubleshooting guides or to report a problem.
 
 ## Usage
 
+We provide a [complete guide](./documentation/tutorial.md) on how to use DeepChopper for NanoPore direct-RNA sequencing data.
+Below is a brief overview of the command-line interface and library usage.
+
+### Command-Line Interface
+
+DeepChopper provides a command-line interface (CLI) for easy access to its features. In total, there are three commands: `encode`, `predict`, and `chop`.
+DeepChopper can be used to encode, predict, and chop chimeric reads in direct-RNA sequencing data.
+
+Firstly, we need to encode the input data using the `encode` command, which will generate a `.parquet` file.
+
+```bash
+deepchopper endcode <input.fq>
+```
+
+Next, we can use the `predict` command to predict chimeric reads in the encoded data.
+
 ```bash
-deepchopper endcode --input <input>
+deepchopper predict <input.parquet> --ouput-path predictions
 ```
 
+Finally, we can use the `chop` command to chop the chimeric reads in the input data.
+
 ```bash
-deepchopper predict --input <input> --output <output>
+deepchopper chop <predictions> raw.fq
 ```
 
+Besides, DeepChopper provides a web-based user interface for users to interact with the tool.
+However, the web-based application can only take one FASTQ record at a time.
+
 ```bash
-deepchopper chop --input <input> --output <output>
+deepchopper web
+```
+
+### Library
+
+```python
+import deepchopper
+
+model = deepchopper.DeepChopper.from_pretrained("yangliz5/deepchopper")
 ```
 
 ## Cite
 
 ## 🤜 Contribution
 
-**Build Environment**
+### Build Environment
 
 ```bash
 git clone https://github.com/ylab-hi/DeepChopper.git
@@ -72,7 +112,7 @@ conda env create -n environment.yaml
 conda activate deepchopper
 ```
 
-**Install Dependencies**
+### Install Dependencies
 
 ```bash
 pip install pipx

deepchopper/__init__.py

@@ -1,6 +1,7 @@
 """DeepChopper package."""
 
-from . import data, eval, models, train, utils
+from . import data, eval, models, train, ui, utils
 from .deepchopper import *  # noqa: F403
+from .models import DeepChopper
 
-__all__ = ["models", "utils", "data", "train", "eval"]
+__all__ = ["models", "utils", "data", "train", "eval", "DeepChopper", "ui"]