Python interface to running command-line and web-based MHC binding predictors
Project description
`|Build Status| <https://travis-ci.org/hammerlab/mhctools>`_ `|Coverage
Status| <https://coveralls.io/r/hammerlab/mhctools?branch=master>`_
`|DOI| <https://zenodo.org/badge/latestdoi/18834/hammerlab/mhctools>`_
mhctools
========
Python interface to running command-line and web-based MHC binding
predictors.
Example
-------
\`\`\`python from mhctools import NetMHCpan # Run NetMHCpan for alleles
HLA-A*01:01 and HLA-A*02:01 predictor = NetMHCpan(alleles=["A\*02:01",
"hla-a0101"])
scan the short proteins 1L2Y and 1L3Y for epitopes
==================================================
protein\_sequences = { "1L2Y": "NLYIQWLKDGGPSSGRPPPS", "1L3Y":
"ECDTINCERYNGQVCGGPGRGLCFCGKCRCHPGFEGSACQA" }
binding\_predictions =
predictor.predict\_subsequences(protein\_sequences,
peptide\_lengths=[9])
flatten binding predictions into a Pandas DataFrame
===================================================
df = binding\_predictions.to\_dataframe()
epitope collection is sorted by percentile rank
===============================================
of binding predictions
======================
for binding\_prediction in binding\_predictions: if
binding\_prediction.affinity < 100: print("Strong binder: %s" %
(binding\_prediction,)) \`\`\` ## API
The following MHC binding predictors are available in ``mhctools``: \*
``MHCflurry``: open source predictor installed by default with
``mhctools``, requires the user run ``mhcflurry-downloads fetch`` first
to download MHCflurry models \* ``NetMHC3``: requires locally installed
version of `NetMHC 3.x <http://www.cbs.dtu.dk/services/NetMHC-3.4/>`_ \*
``NetMHC4``: requires locally installed version of `NetMHC
4.x <http://www.cbs.dtu.dk/services/NetMHC/>`_ \* ``NetMHC``: a wrapper
function to automatically use ``NetMHC3`` or ``NetMHC4`` depending on
what's installed. \* ``NetMHCpan``: requires locally installed version
of `NetMHCpan <http://www.cbs.dtu.dk/services/NetMHCpan/>`_ \*
``NetMHCIIpan``: requires locally installed version of
`NetMHCIIpan <http://www.cbs.dtu.dk/services/NetMHCIIpan/>`_ \*
``NetMHCcons``: requires locally installed version of
`NetMHCcons <http://www.cbs.dtu.dk/services/NetMHCcons/>`_ \*
``IedbMhcClass1``: Uses IEDB's REST API for class I binding predictions.
\* ``IedbMhcClass2``: Uses IEDB's REST API for class II binding
predictions. \* ``RandomBindingPredictor``: Creates binding predictions
with random IC50 and percentile rank values.
Every binding predictor is constructed with an ``alleles`` argument
specifying the HLA type for which to make predictions. Predictions are
generated by calling the ``predict`` method with a dictionary mapping
sequence IDs or names to amino acid sequences.
Additionally there is a module for running the
`NetChop <http://www.cbs.dtu.dk/services/NetChop>`_ proteosomal cleavage
predictor: \* ``NetChop``: requires locally installed version of
`NetChop-3.1 <http://www.cbs.dtu.dk/services/NetChop/>`_
.. |Build
Status| image:: https://travis-ci.org/hammerlab/mhctools.svg?branch=master
.. |Coverage
Status| image:: https://coveralls.io/repos/hammerlab/mhctools/badge.svg?branch=master
.. |DOI| image:: https://zenodo.org/badge/18834/hammerlab/mhctools.svg
Status| <https://coveralls.io/r/hammerlab/mhctools?branch=master>`_
`|DOI| <https://zenodo.org/badge/latestdoi/18834/hammerlab/mhctools>`_
mhctools
========
Python interface to running command-line and web-based MHC binding
predictors.
Example
-------
\`\`\`python from mhctools import NetMHCpan # Run NetMHCpan for alleles
HLA-A*01:01 and HLA-A*02:01 predictor = NetMHCpan(alleles=["A\*02:01",
"hla-a0101"])
scan the short proteins 1L2Y and 1L3Y for epitopes
==================================================
protein\_sequences = { "1L2Y": "NLYIQWLKDGGPSSGRPPPS", "1L3Y":
"ECDTINCERYNGQVCGGPGRGLCFCGKCRCHPGFEGSACQA" }
binding\_predictions =
predictor.predict\_subsequences(protein\_sequences,
peptide\_lengths=[9])
flatten binding predictions into a Pandas DataFrame
===================================================
df = binding\_predictions.to\_dataframe()
epitope collection is sorted by percentile rank
===============================================
of binding predictions
======================
for binding\_prediction in binding\_predictions: if
binding\_prediction.affinity < 100: print("Strong binder: %s" %
(binding\_prediction,)) \`\`\` ## API
The following MHC binding predictors are available in ``mhctools``: \*
``MHCflurry``: open source predictor installed by default with
``mhctools``, requires the user run ``mhcflurry-downloads fetch`` first
to download MHCflurry models \* ``NetMHC3``: requires locally installed
version of `NetMHC 3.x <http://www.cbs.dtu.dk/services/NetMHC-3.4/>`_ \*
``NetMHC4``: requires locally installed version of `NetMHC
4.x <http://www.cbs.dtu.dk/services/NetMHC/>`_ \* ``NetMHC``: a wrapper
function to automatically use ``NetMHC3`` or ``NetMHC4`` depending on
what's installed. \* ``NetMHCpan``: requires locally installed version
of `NetMHCpan <http://www.cbs.dtu.dk/services/NetMHCpan/>`_ \*
``NetMHCIIpan``: requires locally installed version of
`NetMHCIIpan <http://www.cbs.dtu.dk/services/NetMHCIIpan/>`_ \*
``NetMHCcons``: requires locally installed version of
`NetMHCcons <http://www.cbs.dtu.dk/services/NetMHCcons/>`_ \*
``IedbMhcClass1``: Uses IEDB's REST API for class I binding predictions.
\* ``IedbMhcClass2``: Uses IEDB's REST API for class II binding
predictions. \* ``RandomBindingPredictor``: Creates binding predictions
with random IC50 and percentile rank values.
Every binding predictor is constructed with an ``alleles`` argument
specifying the HLA type for which to make predictions. Predictions are
generated by calling the ``predict`` method with a dictionary mapping
sequence IDs or names to amino acid sequences.
Additionally there is a module for running the
`NetChop <http://www.cbs.dtu.dk/services/NetChop>`_ proteosomal cleavage
predictor: \* ``NetChop``: requires locally installed version of
`NetChop-3.1 <http://www.cbs.dtu.dk/services/NetChop/>`_
.. |Build
Status| image:: https://travis-ci.org/hammerlab/mhctools.svg?branch=master
.. |Coverage
Status| image:: https://coveralls.io/repos/hammerlab/mhctools/badge.svg?branch=master
.. |DOI| image:: https://zenodo.org/badge/18834/hammerlab/mhctools.svg
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