Skip to main content

MetaLocGramN: a method for subcellular localization prediction of Gram-negative proteins.

Project description

Welcome to MetaLocGramN
---------------

The MetaLocGramN is a method for subcellular localization prediction of Gram-negative proteins.
Read more: http://iimcb.genesilico.pl/MetaLocGramN/home

How does MetaLocGramN work?
============

The MetaLocGramN is a gateway to a number of primary prediction methods (various types: signal peptide, beta-barrel, transmembrane helices and subcellular localization predictors).

The MetaLocGramN integrates the primary methods and based on their outputs provides overall consensus prediction.

Requirements
============

* suds = 0.4

Installation
============

Install it with pip (or easy_install)::

pip install MetaLocGramN

How to start?
============

If you are really lazy try:

$ ipython

In [1]: from MetaLocGramN import *
In [2]: run_example()
# job_id: 1X820N
# status: queue
# status: primary prediction::in progress
# status: primary prediction::in progress
# status: primary prediction::done
# status: consenus::done
# status: done
extracellular,47.541,0.0,0.0,0.0,52.459,
primary methods: CELLO,cytoplasmic,0.6138,0.036,0.1346,0.0612,0.1546,PSLpred,extracellular,0.2,0.531,PSORTb3,unknown,0.2,0.2,0.2,0.2,0.2,SosuiGramN,cytoplasmic
In [3]: run_example?
# to get help!
In [4]: run_example??
# to get even bigger help!

if you want to find out more, see test.py inside the pkg.

import MetaLocGramN
import time

if __name__ == "__main__":
mlgn = MetaLocGramN.MLGN()

seq = """>fasta
MKLSINKNTLESAVILCNAYVEKKDSSTITSHLFFHADEDKLLIKASDYEIGI
NYKIKKIRVESSGFATANAKSIADVIKSLNNEEVVLETIDNFLFVRQKNTKYK
"""
mlgn.predict(seq)
print '# job_id:', mlgn.get_job_id()
status = ''
while True:
status = mlgn.get_status()
print '# status:', status
if status == 'done':
break
time.sleep(5)
print mlgn.get_result()

You should get something like:

python test.py
# job_id: K6Q10Q
# status: queue
# status: queue
# status: primary prediction::in progress
# status: primary prediction::in progress
# status: primary prediction::done
# status: done
extracellular,47.541,0.0,0.0,0.0,52.459,
primary methods: CELLO,cytoplasmic,0.6138,0.036,0.1346,0.0612,0.1546,PSLpred,extracellular,0.2,0.531,PSORTb3,unknown,0.2,0.2,0.2,0.2,0.2,SosuiGramN,cytoplasmic

Authors
==================================================

Marcin Magnus,
Marcin Pawlowski,
Janusz M. Bujnicki

http://iimcb.genesilico.pl/


Happy predictions!
==================================================

Marcin Magnus magnus@genesilico.pl

Project details


Release history Release notifications | RSS feed

This version

0.99

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for MetaLocGramN, version 0.99
Filename, size File type Python version Upload date Hashes
Filename, size MetaLocGramN-0.99.tar.gz (14.8 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page