Sean Higgins
Department of Computer Science
University of Colorado, Colorado Springs
Email: shiggins@uccs.edu
Victor Akpokiro
Department of Computer Science
University of Colorado, Colorado Springs
Email: vakpokir@uccs.edu
Oluwatosin Oluwadare, PhD
Department of Computer Science
University of Colorado, Colorado Springs
Email: ooluwada@uccs.edu
Access TADMaster: http://tadmaster.io
TADMaster can be run in a Docker-containerized environment locally on users computer. Before cloning this repository and attempting to build, the Docker engine, If you are new to docker here is a quick docker tutorial for beginners.
To install and build TADMaster follow these steps.
- Clone this repository locally using the command
git clone https://github.com/OluwadareLab/TADMaster.git && cd TADMaster
. - Pull the TADMaster docker image from docker hub using the command
docker pull oluwadarelab/tadmaster:latest
. This may take a few minutes. Once finished, check that the image was sucessfully pulled usingdocker image ls
. - Run the TADMaster container and mount the present working directory to the container using
docker run -v ${PWD}:${PWD} -p 8050:8050 -it oluwadarelab/tadmaster
. cd
to your home directory where TADMaster is downloaded.
👍 Congratulations! You can now run TADMaster and TADMaster Plus locally with no restriction.
TADMaster is written in Python3, Dash and includes many R, python c++ libraries necessary to run the various tools included and perform analysis visualization. All dependencies are included in the Docker environment. GPU is not loaded into the docker container as it is not needed to run TADMaster.
Now, that you are running a docker container, follow the step by step instructions to execute the cloned TADMaster source codes for job processing and visualization.
- Make changes to the
TADMaster.config
file based on your preferences.- We have provided some default parameter settings as an example.
- Required Inputs are : Specify the input matrix path, chromosome number, resolution and input datatype.
- We have provided some default parameter setting as an example.
- Use
True
orFalse
to turn on or off respectively a Normalization or TADCaller algorithm.- By default we Turned on Normalization:
KR
and TADCallers:Armatus and DI
- By default we Turned on Normalization:
In both scripts:
- Replace
path_directory
in line 1 to the directory where yourTADMaster.config
file is located- We have provided some default settings as an example.
- Change the
home_path
to the directory whereTADMaster
repository files you downloaded is located- We have provided some default settings as an example.
- make a new directory:
mkdir example_job_output
- Change the
job_path
to the path directory where you want the job processing outputs to be saved- We assigned
job_path
directory toexample_job_output
in both scripts.
- We assigned
$ chmod +x TADMasterPlus.sh
$./TADMasterPlus.sh
- Running this script with this settings should take about 2 minutes.
- Once Completed, TADMasterPlus will generate all outputs in the output path,
job_path
directory, that the user identified. - TADMAster Plus also generates a
Read.me
file that describes the output file structure and organization.
Refer to the Visualization Section below for this.
- Extract the TADMaster output file structure we provided.
$ tar -xvf job_tadmaster.tar.gz
Next, copy the .bed
files you want to perform analysis on into the directory path specified below based on the pathway you followed above.
We have provided two example bed files you can use as a sample in the Data Folder
- Path to copy the bed files into:
job_tadmaster/output/NoMatrixProvided/
. where
job_tadmaster
by default is the name of the passive output directory extracted in Step A into the TADMaster directory. Passive because it wasn't specified by you.NoMatrixProvided
signifies that no matrix was provided for the bed files
Note: See our wiki for information about BED file formats accepted by TADMaster
Refer to the Visualization Section below for this.
- When you have completed a TADMaster or TADMaster job submission, the next step is Visualization.
- At this step, your job should be in the output path,
job_path
directory, which would contain the BED Files to perform analysis and visualization on.- BED files will be located in
job_path/output/<name_of_identifier_used>/
- BED files will be located in
- To visualize the analysis from the bed files:
- Required Inputs are : Specify the
resolution
,chromosome number
, andjob_path
of the job- We have provided some default input assignment as an example for TADMaster Plus Job visualization.
$ python tadmaster_visualize.py
-
Once, you have executed the python script, You should get the message that
Dash is running on http://0.0.0.0:8050/
(ignore warning messages provided). 👍 Congratulations! This means that you can visualize locally. -
Open your browser and copy the URL shown. You would be able to access the visualization of the output jobs.
-
Please note that for some browsers, like safari, localhost URL could be different. If URL:http://0.0.0.0:8050/ doesn't return a visualization, please use URL: http://127.0.0.1:8050
Important Note for Users: Minor compatibility issues between certain operating systems and browsers have been observed with the local TADMaster visualization script. These issues arise from improper loading of data chunks and can be resolved by closing the browser (or tab) and reloading local host.
- job_861482c1-927a-487c-a18a-a2be43fe0478.zip: A previously submitted job
- Data: Sample input data accepted
- TADCaller: TAD algorithm source codes used by TADMaster
- Analysis: Pre- and Post- processing scripts used by TADMaster
- normalization: Libraries and Scripts to support data normalization for the web server
Please see the wiki for an extensive documentation of how to use TADMaster functions
Higgins, S., Akpokiro, V., Westcott, A., & Oluwadare, O. (2022). TADMaster: a comprehensive web-based tool for the analysis of topologically associated domains. BMC bioinformatics, 23(1), 1-10.