MetaLocGramN is a method for subcellular localization prediction of Gram-negative proteins.
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MetaLocGramN.egg-info
.gitignore
.travis.yml
LICENSE.txt
MANIFEST.in
MetaLocGramN.py
PKG-INFO
README.md
requirements.txt
run-tests.sh
setup.cfg
setup.py
test.py

README.md

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.

Build Status

Build Status

Requirements

  • suds = 0.4

Installation

Install it with pip (or easy_install)::

pip install MetaLocGramN

The cmd will get https://pypi.python.org/pypi/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