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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


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This version
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0.99

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