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:
git clone https://github.com/Moha-cm/AutoRNASeq.git
cd AutoRNASeq/app_file
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
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
After installing all dependencies, you can start the Flask application by running:
python ./app.py
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.