PerfectSense is a software that performs data fusion on sensor data in order to obtain accurate estimates from equipment with uncertainty.
MethylSight is an support vector machine (SVM)-based lysine methylation prediction predictor. Our predictor was trained using physicochemical features extracted with ProtDCal, bypassing the need for computationally expensive sequence alignment.
MethylSight extracts lysine-centered windows of 71 residues and computes 28 features, before classfying them with a trained SVM model.
A pre-print of the paper associated with the description of the software is available here.
- Francois Charih - [email protected] - Carleton University
- Kyle K. Biggar - [email protected] - Carleton University
- James R. Green - [email protected] - Carleton University
This repository comprises three things:
- The web server implementation (in
src/) - The React.js-based web client (in
client/) - The standalone predictor (in
predictor/MethylSightV1)
To run MethylSight, you will need to install Docker on your platform.
Then, you will need to create a container and download some software, i.e. weka and ProtDCal which will be installed for you if you run the commands below.
$ git clone https://github.com/flexplicateur/MethylSight.git && cd MethylSight
$ wget https://github.com/fcharih/MethylSight/releases/download/deps/methylsightv1_software.tar.gz \
&& tar -zxvf methylsightv1_software.tar.gz -C predictor/MethylSightV1 \
&& rm methylsightv1_software.tar.gz
$ docker-compose build
$ docker-compose up -d
MethylSight will then be listening on port 5000 and waiting for requests. You can then
submit requests to the port 5000 using curl
$ curl -X POST -H "Content-Type: application/json" \
-d '{ "tag": ">protein_name", "sequence": "MYPRTEINHASAKHERE" }' \
http://localhost:5000
This project is licensed under the MIT License - see the LICENSE.md file for details