Skip to content

Latest commit

 

History

History
80 lines (51 loc) · 2.57 KB

README.md

File metadata and controls

80 lines (51 loc) · 2.57 KB
Raw

Web-server for TOPCONS2

Description:

This is the web-server implementation of the TOPCONS2 workflow.

The web-server is developed with Django 2.2.7+ and Python 3.6+

This software is open source and licensed under the MIT license

TOPCONS2 is an updated version of the widely used TOPCONS for predicting
membrane protein topologies using consensus prediction.
It is faster yet more accurate than the old TOPCONS according to our solid
benchmarking. Moreover, it predicts not only the trans-membrane helices,
but also the location of signal peptide


This implementation employs two queuing schemes for small jobs and large
jobs respectively. For single-sequence jobs submitted via web-page, they
will be run directly (and usually immediately after submission) at the
front-end server. For multiple-sequence jobs or jobs submitted via the API
(a Python script for the command-line use of the API is included in the
package), they will be forwarded to the remote servers via the WSDL (Web
Service Definition Language) service. Consequently, the web-server can
handle jobs of all proteins from a proteome. 

This implementation is suitable as as a base platform for bioinformatic
prediction tools that need to be run for one or many sequences but the
computational time for each sequence is short.

Author

Nanjiang Shu

System developer at NBIS

Email: [email protected]

Reference

Tsirigos, K.D., Peters, C., Shu, N.*, Kall, L., Elofsson, A., 2015. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic Acids Res. 43, W401-W407 (*Co-first authors)

Installation

  1. Install dependencies for the web server

    • Apache
    • mod_wsgi

  2. Install the virtual environments by running the following command. Please make sure that the executable virtualenv for Python3 and the same version for which mod_wsgi is installed.

    $ bash setup_virtualenv.sh

  3. Create the django database db.sqlite3

  4. Run

    $ bash init.sh

    to initialize the working folder

  5. In the folder proj, create a softlink of the setting script.

    For development version

     $ ln -s dev_settings.py settings.py

    For release version

     $ ln -s pro_settings.py settings.py

    Note: for the release version, you need to create a file with secret key and stored at /etc/django_pro_secret_key.txt

  6. On the computational node. run

    $ virtualenv env --system-site-packages

    to make sure that python can use all other system-wide installed packages