TB-DROP

Introduction

• Aims the goal to end the global TB epidemic by 2030, which has been adopted by all Member States of the United Nations (UN) and the World Health Organization (WHO).
• Is an end-to-end, user-friendly, deep learning-based drug resistance prediction platform of Mycobacterium tuberculosis utilizing whole genome mutations.

Installation

TB-DROP integrates with numbers of softwares commonly used in bioinformatics research, which needs to be run on linux. To simplify the installation of TB-DROP and its dependencies, we packaged TB-DROP into a Docker image, so that users needn't resolve the complex configuration of various softwares and TB-DROP could be used on any operating system that Docker supports.

Docker Installation

Therefore, users must install Docker first. The installation of Docker could refer to the guideline in the official website of Docker (https://docs.docker.com/engine/install/). Some key points are listed below:

• Make sure that your computer's CPU supports virtualisation and WSL2 is installed. In regard to install WSL2, please refer to the following tutorial (https://aka.ms/wsl2kernel):
• You can install your preferred linux system on WSL2 in the Microsoft Store after completing the installation of WSL2, and Ubuntu 20.04 is highly recommonded, because TB-DROP is developed based on Ubuntu 20.04.

Download

Please create a folder Docker and downlocad all files required for TB-DROP ( Dockerfile, pipeline.tar.gz and prepare_env.sh ) to this directory from our latest release. Note Well: All files should be downloaded from the Releases page of github rather than the homepage of this repository because the files displayed at the homepage are only part of the whole files. The reason why we don't upload all files to the homepage is some files are too large to upload to the homepage.

Create Image

Launch command line mode and go into the folder Docker, then run:

docker build -t name_of_image .

Run docker images, find and remember the image ID of the image created before.

• No capital letters in the name.

Create Container & Run TB-DROP

To store the container, please create a folder other than the directory where the image is stored, and then move pipeline.tar.gz and prepare_env.sh to this folder. Then run:

docker run -p host_port:8080 -v path_to_store_container:/root/pipeline -itd image_ID /bin/bash -c "cd /root/pipeline;bash prepare_env.sh;touch finish;/bin/bash"

Creating a container for the first time takes a relatively long time because TB-DROP needs to decompress the file 'pipeline.tar.gz' and prepare the environment.

• Parameters 'host_port' after -p can be freely specified. However, please remember the ports you specified, it's required when accessing the TB-DROP through localhost:host_port.
• The path_to_store_container after -v must be absolute path.
• When finish appears, installation has been completed, and then Docker will return the ID of the container, which is used to perform a series of operations, such as starting and deleting containers.

Start TB-DROP

Visit localhost:host port in the browser, then you will see TB-DROP.

Restart

All data was saved in the container, so that you must restart the container created before rather than create a new container when you want to use TB-DROP again. The steps of restarting TB-DROP were listed below:

1. Start Docker and launch the container created before in the container tab.
2. Click the CLI botton to open a terminal.
3. Run the following commands:

   service mysql start

   nohup python3 /root/pipeline/tb-visualization/03.server/flask/run.py &> /dev/null &

4. Close the terminal and visit localhost:host port in the browser.

Usage of TB-DROP

Uploading:

Fill in the sample ID and select the corresponding fastq files of the sample.Click Submit.

Analyzing:

Select the samples you want to analyze and Click Start Analysis.The status of sample will turn to finished when analysis is done. Click the sample ID then you will be redirected to the report page.