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Introduction and setup
=======================
MHCflurry is a Python package for peptide/MHC I binding affinity prediction. It
provides competitive accuracy with a fast, documented, open source
implementation.
You can download pre-trained MHCflurry models fit to affinity measurements
deposited in IEDB. See the "downloads_generation/models_class1" directory in the
repository for the workflow used to train these predictors. Users with their own
data can also fit their own MHCflurry models.
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Currently only allele-specific prediction is implemented, in which separate models
are trained for each allele. The released models therefore support a fixed set of common
class I alleles for which sufficient published training data is available.
MHCflurry supports Python versions 2.7 and 3.4+. It uses the Keras neural
network library via either the Tensorflow or Theano backends. GPUs may
optionally be used for a generally modest speed improvement.
If you find MHCflurry useful in your research please cite:
O'Donnell, T. et al., 2017. MHCflurry: open-source class I MHC
binding affinity prediction. bioRxiv. Available at:
http://www.biorxiv.org/content/early/2017/08/09/174243.
Installation (pip)
-------------------
Install the package:
::
pip install mhcflurry
Then download our datasets and trained models:
::
mhcflurry-downloads fetch
From a checkout you can run the unit tests with:
::
pip install nose
nosetests .
Using conda
-------------
You can alternatively get up and running with a `conda <https://conda.io/docs/>`__
environment as follows. Some users have reported that this can avoid problems installing
tensorflow.
::
conda create -q -n mhcflurry-env python=3.6 'tensorflow>=1.1.2'
source activate mhcflurry-env
Then continue as above:
::
pip install mhcflurry
mhcflurry-downloads fetch