QC-report-for-NGS-data-using-FLASK

My First Flask Application

Overview

Welcome to my first Flask web application! This project is designed to help users generate quality reports for Next-Generation Sequencing (NGS) data. Using this application, you can process raw NGS data files and generate comprehensive quality reports using FastQC and MultiQC tools. The application provides a user-friendly interface for uploading files, performing quality checks, and viewing results.

Application interface with workflow of NGS Data Quality Report Generator

1. Home Page

Description: Home page where the user is provided with the provision to select three options: single-end, paired-end, or demo-data. If the user selects demo-data, FastQC reports will start generating.

2. Upload Files Page

Upload Files Page Description: If the user selects an option other than demo-data, a provision for uploading files will be given. After uploading, FastQC reports will be generated.

3. FastQC Reports Page

FastQC Reports Page Description: The list of FastQC HTML reports will be displayed, with the option to choose the MultiQC step.

4. MultiQC Reports Page

MultiQC Reports Page Description: If the user chooses MultiQC, those reports will be generated along with a provision for the user to download all the reports.

5. Download Reports Page

Download Reports Page - Step 1 Download Reports Page - Step 2 Description: If the user doesn’t choose the MultiQC step, they will be redirected to a page where they can download all the reports.

Features

File Upload: Upload raw NGS data files directly through the web interface.
Quality Assessment: Run FastQC on the uploaded files to assess quality.
Report Generation: Generate FastQC reports and optionally run MultiQC to compile and summarize the results.
Report Viewing: View generated reports directly through the application.
Download Reports: Download all generated reports and files in a single zip archive.
Demo Data: Use pre-loaded demo data for quick testing.

Technologies Used

Flask: A lightweight web framework for Python.
FastQC: A quality control tool for high-throughput sequence data.
MultiQC: A tool for aggregating results from multiple quality control tools.
HTML/CSS: For creating the user interface.
Python: For the backend logic and handling data processing.

Installation

To run this application locally, follow these steps:

Clone the Repository:

 git clone https://github.com/ajayashettigar/QC-report-for-NGS-data.git
 cd QC-report-for-NGS-data

Set Up a Virtual Environment:

 python -m virtualenv venv
 source env/bin/activate  # On Windows use `venv\Scripts\activate`

Install Required Packages:

 pip install flask
 pip install subprocess
 pip install shutil
 pip install zipfile

Run the Application:
```
 python app.py
```
The application will start on http://localhost:5003.

Usage

Access the Application: Open a web browser and go to http://172.18.110.135:5003.
Upload Files: Select “single-end” or “paired-end” to upload your files. If you do not have files, then select “use demo data” to use default demo files.
Generate Reports: After uploading, the application will process the files and generate FastQC reports. I have also included the provision for performing MultiQC if the user choose to.
View and Download Reports: You can view and download all reports and files in a zip archive.

Collaborations!

Contributions are welcome! If you have suggestions or improvements, feel free to open an issue or submit a pull request.

Acknowledgements

This project uses the following tools and libraries:

FastQC: A quality control tool for high-throughput sequence data. cite

MultiQC: Aggregates results from multiple quality control tools into a single report. cite

Flask: A lightweight WSGI web application framework. cite

Contact

For any questions or feedback, please contact Ajay A Shettigar.