https://github.com/moha-cm/autornaseq

Simplified RNA-Seq Insights with automation
https://github.com/moha-cm/autornaseq

conda flask-application mamba python r snakemake-pipeline snakemake-workflow

Last synced: 21 days ago
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Simplified RNA-Seq Insights with automation

• Host: GitHub
• URL: https://github.com/moha-cm/autornaseq
• Owner: Moha-cm
• License: mit
• Created: 2024-08-24T07:56:19.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-21T02:12:15.000Z (28 days ago)
• Last Synced: 2024-10-21T05:31:09.393Z (28 days ago)
• Topics: conda, flask-application, mamba, python, r, snakemake-pipeline, snakemake-workflow
• Language: HTML
• Homepage:
• Size: 4.61 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# AutoRNASeq

## Simplified RNA-Seq Insights with Automation

AutoRNASeq is an application that automates RNA-Seq data analysis. Users can upload raw counts and metadata files to generate insightful plots and analyze their data. The backend leverages R scripts and Snakemake for processing.

Installation

Follow these steps to set up and run the application:

### 1. Clone the Repository

`git clone https://github.com/Moha-cm/AutoRNASeq.git`

`cd AutoRNASeq/app_file`

### 2. Install Python Dependencies

Ensure you have Python installed (preferably in a virtual environment). Then, install the required Python packages using the requirements.txt file:

`pip install -r requirements.txt`

### 3. Set Up Mamba and Install Snakemake

To efficiently manage Snakemake and other bioinformatics tools, you'll need Mamba, a faster alternative to Conda. You can install Mamba using Miniforge:

Install Miniforge:

Visit the Miniforge GitHub page and download the appropriate installer for your operating system.

Set Up Mamba:

Once Miniforge is installed, set up Mamba by running the following command:

`conda install mamba -n base -c conda-forge`

Install Snakemake:

Now, install Snakemake using Mamba:

`mamba install -c conda-forge snakemake`

### 4. Running the Application

After installing all dependencies, you can start the Flask application by running:

`python ./app.py`

### 5. Usage

Upload Data: Use the UI to upload your raw counts and metadata files with the samples columns like files in  the sample data folder.

Generate Plots: The application will trigger Snakemake workflows and R scripts to process the data and generate plots based on your input.

Explore Results: Visualize the generated plots and download files directly from the interface.