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diff --git a/docs/Makefile b/docs/Makefile
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--- a/docs/Makefile
+++ b/docs/Makefile
@@ -63,12 +63,13 @@ generate:
# Added by Tim:
.PHONY: readme
readme: text
- pandoc -B package_readme/readme_header.md \
+ #cp _build/text/package_readme/readme.template.txt package_readme/readme.generated.rst
+ pandoc -B package_readme/readme_header.rst \
-f rst \
- -t gfm \
+ -t rst \
--base-header-level 2 \
_build/text/package_readme/readme.template.txt \
- -o package_readme/readme.generated.md
+ -o package_readme/readme.generated.rst
.PHONY: clean
clean:
diff --git a/docs/package_readme/readme.generated.rst b/docs/package_readme/readme.generated.rst
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@@ -0,0 +1,326 @@
+.. |Build Status| image:: https://travis-ci.org/hammerlab/mhcflurry.svg?branch=master
+ :target: https://travis-ci.org/hammerlab/mhcflurry
+
+.. |Coverage Status| image:: https://coveralls.io/repos/github/hammerlab/mhcflurry/badge.svg?branch=master
+ :target: https://coveralls.io/github/hammerlab/mhcflurry?branch=master
+
+mhcflurry
+=========
+
+Open source neural network models for peptide-MHC binding affinity prediction
+
+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.
+
+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 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
+
+Command-line usage
+------------------
+
+Downloading models
+==================
+
+Most users will use pre-trained MHCflurry models that we release. These
+models are distributed separately from the source code and may be
+downloaded with the following command:
+
+We also release other "downloads," such as curated training data and
+some experimental models. To see what you have downloaded, run:
+
+mhcflurry-predict
+=================
+
+The "mhcflurry-predict" command generates predictions from the
+command-line. It defaults to using the pre-trained models you downloaded
+above but this can be customized with the "--models" argument. See
+"mhcflurry-predict -h" for details.
+
+ $ mhcflurry-predict --alleles HLA-A0201 HLA-A0301 --peptides
+ SIINFEKL SIINFEKD SIINFEKQ
+ allele,peptide,mhcflurry_prediction,mhcflurry_prediction_low,mhcflurry_prediction_high
+ HLA-A0201,SIINFEKL,5326.541919062165,3757.86675352994,7461.37693353508
+ HLA-A0201,SIINFEKD,18763.70298522213,13140.82000240037,23269.82139560844
+ HLA-A0201,SIINFEKQ,18620.10057358322,13096.425874678192,23223.148184869413
+ HLA-A0301,SIINFEKL,24481.726678691946,21035.52779725433,27245.371837497867
+ HLA-A0301,SIINFEKD,24687.529360239587,21582.590014592537,27749.39869616437
+ HLA-A0301,SIINFEKQ,25923.062203902562,23522.5793450799,28079.456657427705
+
+The predictions returned are affinities (KD) in nM. The "prediction_low"
+and "prediction_high" fields give the 5-95 percentile predictions across
+the models in the ensemble. The predictions above were generated with
+MHCflurry 0.9.2.
+
+Your exact predictions may vary slightly from these (up to about 1 nM)
+depending on the Keras backend in use and other numerical details.
+Different versions of MHCflurry can of course give results considerably
+different from these.
+
+You can also specify the input and output as CSV files. Run
+"mhcflurry-predict -h" for details.
+
+Fitting your own models
+=======================
+
+Library usage
+-------------
+
+The MHCflurry Python API exposes additional options and features beyond
+those supported by the commandline tools. This tutorial gives a basic
+overview of the most important functionality. See the API Documentation
+for further details.
+
+The "Class1AffinityPredictor" class is the primary user-facing
+interface.
+
+ >>> import mhcflurry >>> print("MHCflurry version: %s" %
+ (mhcflurry.__version__)) MHCflurry version: 1.0.0 >>> >>> # Load
+ downloaded predictor >>> predictor =
+ mhcflurry.Class1AffinityPredictor.load() >>>
+ print(predictor.supported_alleles) ['BoLA-6*13:01', 'Eqca-1*01:01',
+ 'H-2-Db', 'H-2-Dd', 'H-2-Kb', 'H-2-Kd', 'H-2-Kk', 'H-2-Ld',
+ 'HLA-A*01:01', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03',
+ 'HLA-A*02:05', 'HLA-A*02:06', 'HLA-A*02:07', 'HLA-A*02:11',
+ 'HLA-A*02:12', 'HLA-A*02:16', 'HLA-A*02:17', 'HLA-A*02:19',
+ 'HLA-A*02:50', 'HLA-A*03:01', 'HLA-A*11:01', 'HLA-A*23:01',
+ 'HLA-A*24:01', 'HLA-A*24:02', 'HLA-A*24:03', 'HLA-A*25:01',
+ 'HLA-A*26:01', 'HLA-A*26:02', 'HLA-A*26:03', 'HLA-A*29:02',
+ 'HLA-A*30:01', 'HLA-A*30:02', 'HLA-A*31:01', 'HLA-A*32:01',
+ 'HLA-A*32:07', 'HLA-A*33:01', 'HLA-A*66:01', 'HLA-A*68:01',
+ 'HLA-A*68:02', 'HLA-A*68:23', 'HLA-A*69:01', 'HLA-A*80:01',
+ 'HLA-B*07:01', 'HLA-B*07:02', 'HLA-B*08:01', 'HLA-B*08:02',
+ 'HLA-B*08:03', 'HLA-B*14:02', 'HLA-B*15:01', 'HLA-B*15:02',
+ 'HLA-B*15:03', 'HLA-B*15:09', 'HLA-B*15:17', 'HLA-B*15:42',
+ 'HLA-B*18:01', 'HLA-B*27:01', 'HLA-B*27:03', 'HLA-B*27:04',
+ 'HLA-B*27:05', 'HLA-B*27:06', 'HLA-B*27:20', 'HLA-B*35:01',
+ 'HLA-B*35:03', 'HLA-B*35:08', 'HLA-B*37:01', 'HLA-B*38:01',
+ 'HLA-B*39:01', 'HLA-B*40:01', 'HLA-B*40:02', 'HLA-B*42:01',
+ 'HLA-B*44:01', 'HLA-B*44:02', 'HLA-B*44:03', 'HLA-B*45:01',
+ 'HLA-B*45:06', 'HLA-B*46:01', 'HLA-B*48:01', 'HLA-B*51:01',
+ 'HLA-B*53:01', 'HLA-B*54:01', 'HLA-B*57:01', 'HLA-B*58:01',
+ 'HLA-B*73:01', 'HLA-B*83:01', 'HLA-C*03:03', 'HLA-C*03:04',
+ 'HLA-C*04:01', 'HLA-C*05:01', 'HLA-C*06:02', 'HLA-C*07:01',
+ 'HLA-C*07:02', 'HLA-C*08:02', 'HLA-C*12:03', 'HLA-C*14:02',
+ 'HLA-C*15:02', 'Mamu-A*01:01', 'Mamu-A*02:01', 'Mamu-A*02:0102',
+ 'Mamu-A*07:01', 'Mamu-A*07:0103', 'Mamu-A*11:01', 'Mamu-A*22:01',
+ 'Mamu-A*26:01', 'Mamu-B*01:01', 'Mamu-B*03:01', 'Mamu-B*08:01',
+ 'Mamu-B*10:01', 'Mamu-B*17:01', 'Mamu-B*17:04', 'Mamu-B*39:01',
+ 'Mamu-B*52:01', 'Mamu-B*66:01', 'Mamu-B*83:01', 'Mamu-B*87:01',
+ 'Patr-A*01:01', 'Patr-A*03:01', 'Patr-A*04:01', 'Patr-A*07:01',
+ 'Patr-A*09:01', 'Patr-B*01:01', 'Patr-B*13:01', 'Patr-B*24:01']
+
+ # coding: utf-8
+
+ # In[22]:
+
+ import pandas import numpy import seaborn import logging from
+ matplotlib import pyplot
+
+ import mhcflurry
+
+ # # Download data and models
+
+ # In[2]:
+
+ get_ipython().system('mhcflurry-downloads fetch')
+
+ # # Making predictions with Class1AffinityPredictor
+
+ # In[3]:
+
+ help(mhcflurry.Class1AffinityPredictor)
+
+ # In[4]:
+
+ downloaded_predictor = mhcflurry.Class1AffinityPredictor.load()
+
+ # In[5]:
+
+ downloaded_predictor.predict(allele="HLA-A0201",
+ peptides=["SIINFEKL", "SIINFEQL"])
+
+ # In[6]:
+
+ downloaded_predictor.predict_to_dataframe(allele="HLA-A0201",
+ peptides=["SIINFEKL", "SIINFEQL"])
+
+ # In[7]:
+
+ downloaded_predictor.predict_to_dataframe(alleles=["HLA-A0201",
+ "HLA-B*57:01"], peptides=["SIINFEKL", "SIINFEQL"])
+
+ # In[8]:
+
+ downloaded_predictor.predict_to_dataframe(
+ allele="HLA-A0201", peptides=["SIINFEKL", "SIINFEQL"],
+ include_individual_model_predictions=True)
+
+ # In[9]:
+
+ downloaded_predictor.predict_to_dataframe(
+ allele="HLA-A0201", peptides=["SIINFEKL", "SIINFEQL",
+ "TAAAALANGGGGGGGG"], throw=False) # Without throw=False, you'll
+ get a ValueError for invalid peptides or alleles
+
+ # # Instantiating a Class1AffinityPredictor from a saved model on
+ disk
+
+ # In[10]:
+
+ models_dir = mhcflurry.downloads.get_path("models_class1", "models")
+ models_dir
+
+ # In[11]:
+
+ # This will be the same predictor we instantiated above. We're just
+ being explicit about what models to load. downloaded_predictor =
+ mhcflurry.Class1AffinityPredictor.load(models_dir)
+ downloaded_predictor.predict(["SIINFEKL", "SIQNPEKP", "SYNFPEPI"],
+ allele="HLA-A0301")
+
+ # # Fit a model: first load some data
+
+ # In[12]:
+
+ # This is the data the downloaded models were trained on data_path =
+ mhcflurry.downloads.get_path("data_curated",
+ "curated_training_data.csv.bz2") data_path
+
+ # In[13]:
+
+ data_df = pandas.read_csv(data_path) data_df
+
+ # # Fit a model: Low level Class1NeuralNetwork interface
+
+ # In[14]:
+
+ # We'll use mostly the default hyperparameters here. Could also
+ specify them as kwargs. new_model =
+ mhcflurry.Class1NeuralNetwork(layer_sizes=[16])
+ new_model.hyperparameters
+
+ # In[16]:
+
+ train_data = data_df.loc[
+ (data_df.allele == "HLA-B*57:01") & (data_df.peptide.str.len()
+ >= 8) & (data_df.peptide.str.len() <= 15)
+
+ ] get_ipython().magic('time new_model.fit(train_data.peptide.values,
+ train_data.measurement_value.values)')
+
+ # In[17]:
+
+ new_model.predict(["SYNPEPII"])
+
+ # # Fit a model: high level Class1AffinityPredictor interface
+
+ # In[18]:
+
+ affinity_predictor = mhcflurry.Class1AffinityPredictor()
+
+ # This can be called any number of times, for example on different
+ alleles, to build up the ensembles.
+ affinity_predictor.fit_allele_specific_predictors( n_models=1,
+ architecture_hyperparameters={"layer_sizes": [16], "max_epochs":
+ 10}, peptides=train_data.peptide.values,
+ affinities=train_data.measurement_value.values,
+ allele="HLA-B*57:01", )
+
+ # In[19]:
+
+ affinity_predictor.predict(["SYNPEPII"], allele="HLA-B*57:01")
+
+ # # Save and restore the fit model
+
+ # In[20]:
+
+ get_ipython().system('mkdir /tmp/saved-affinity-predictor')
+ affinity_predictor.save("/tmp/saved-affinity-predictor")
+ get_ipython().system('ls /tmp/saved-affinity-predictor')
+
+ # In[21]:
+
+ affinity_predictor2 =
+ mhcflurry.Class1AffinityPredictor.load("/tmp/saved-affinity-predictor")
+ affinity_predictor2.predict(["SYNPEPII"], allele="HLA-B*57:01")
+
+Supported alleles and peptide lengths
+-------------------------------------
+
+Models released with the current version of MHCflurry (1.0.0) support
+peptides of length 8-15 and the following 124 alleles:
+
+ BoLA-6*13:01, Eqca-1*01:01, H-2-Db, H-2-Dd, H-2-Kb, H-2-Kd, H-2-Kk,
+ H-2-Ld, HLA-A*01:01, HLA-A*02:01, HLA-A*02:02, HLA-A*02:03,
+ HLA-A*02:05, HLA-A*02:06, HLA-A*02:07, HLA-A*02:11, HLA-A*02:12,
+ HLA-A*02:16, HLA-A*02:17, HLA-A*02:19, HLA-A*02:50, HLA-A*03:01,
+ HLA-A*11:01, HLA-A*23:01, HLA-A*24:01, HLA-A*24:02, HLA-A*24:03,
+ HLA-A*25:01, HLA-A*26:01, HLA-A*26:02, HLA-A*26:03, HLA-A*29:02,
+ HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*32:07,
+ HLA-A*33:01, HLA-A*66:01, HLA-A*68:01, HLA-A*68:02, HLA-A*68:23,
+ HLA-A*69:01, HLA-A*80:01, HLA-B*07:01, HLA-B*07:02, HLA-B*08:01,
+ HLA-B*08:02, HLA-B*08:03, HLA-B*14:02, HLA-B*15:01, HLA-B*15:02,
+ HLA-B*15:03, HLA-B*15:09, HLA-B*15:17, HLA-B*15:42, HLA-B*18:01,
+ HLA-B*27:01, HLA-B*27:03, HLA-B*27:04, HLA-B*27:05, HLA-B*27:06,
+ HLA-B*27:20, HLA-B*35:01, HLA-B*35:03, HLA-B*35:08, HLA-B*37:01,
+ HLA-B*38:01, HLA-B*39:01, HLA-B*40:01, HLA-B*40:02, HLA-B*42:01,
+ HLA-B*44:01, HLA-B*44:02, HLA-B*44:03, HLA-B*45:01, HLA-B*45:06,
+ HLA-B*46:01, HLA-B*48:01, HLA-B*51:01, HLA-B*53:01, HLA-B*54:01,
+ HLA-B*57:01, HLA-B*58:01, HLA-B*73:01, HLA-B*83:01, HLA-C*03:03,
+ HLA-C*03:04, HLA-C*04:01, HLA-C*05:01, HLA-C*06:02, HLA-C*07:01,
+ HLA-C*07:02, HLA-C*08:02, HLA-C*12:03, HLA-C*14:02, HLA-C*15:02,
+ Mamu-A*01:01, Mamu-A*02:01, Mamu-A*02:0102, Mamu-A*07:01,
+ Mamu-A*07:0103, Mamu-A*11:01, Mamu-A*22:01, Mamu-A*26:01,
+ Mamu-B*01:01, Mamu-B*03:01, Mamu-B*08:01, Mamu-B*10:01,
+ Mamu-B*17:01, Mamu-B*17:04, Mamu-B*39:01, Mamu-B*52:01,
+ Mamu-B*66:01, Mamu-B*83:01, Mamu-B*87:01, Patr-A*01:01,
+ Patr-A*03:01, Patr-A*04:01, Patr-A*07:01, Patr-A*09:01,
+ Patr-B*01:01, Patr-B*13:01, Patr-B*24:01
diff --git a/docs/package_readme/readme.template.rst b/docs/package_readme/readme.template.rst
index 0041ce76b9cad7acfde8930fdd11e37cf17fbcb6..d96af450129dbcd3255d956bc4ae7999b7c04d31 100644
--- a/docs/package_readme/readme.template.rst
+++ b/docs/package_readme/readme.template.rst
@@ -1,4 +1,3 @@
-.. readme_header.md will be inserted first by pandoc
.. include:: /intro.rst
:start-line: 3
diff --git a/docs/package_readme/readme_header.rst b/docs/package_readme/readme_header.rst
new file mode 100644
index 0000000000000000000000000000000000000000..539e315a47ff04602b68a53c10dd405837554671
--- /dev/null
+++ b/docs/package_readme/readme_header.rst
@@ -0,0 +1,10 @@
+.. |Build Status| image:: https://travis-ci.org/hammerlab/mhcflurry.svg?branch=master
+ :target: https://travis-ci.org/hammerlab/mhcflurry
+
+.. |Coverage Status| image:: https://coveralls.io/repos/github/hammerlab/mhcflurry/badge.svg?branch=master
+ :target: https://coveralls.io/github/hammerlab/mhcflurry?branch=master
+
+mhcflurry
+=========
+
+Open source neural network models for peptide-MHC binding affinity prediction
\ No newline at end of file