From 4ed41bb9434e7c4b8597d064457c7be6246dda77 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Thu, 21 Dec 2017 12:55:03 -0500
Subject: [PATCH] readme fix
---
docs/Makefile | 14 +-
docs/package_readme/readme.generated.rst | 388 +++++++++++------------
2 files changed, 201 insertions(+), 201 deletions(-)
diff --git a/docs/Makefile b/docs/Makefile
index cc937627..ae700144 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -63,13 +63,15 @@ generate:
# Added by Tim:
.PHONY: readme
readme: text
- #cp _build/text/package_readme/readme.template.txt package_readme/readme.generated.rst
- pandoc -B package_readme/readme_header.rst \
- -f rst \
- -t rst \
- --base-header-level 2 \
+ cat package_readme/readme_header.rst \
_build/text/package_readme/readme.template.txt \
- -o package_readme/readme.generated.rst
+ > package_readme/readme.generated.rst
+ #pandoc -B package_readme/readme_header.rst \
+ # -f rst \
+ # -t rst \
+ # --base-header-level 2 \
+ # _build/text/package_readme/readme.template.txt \
+ # -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
index 78f2da39..d75bec92 100644
--- a/docs/package_readme/readme.generated.rst
+++ b/docs/package_readme/readme.generated.rst
@@ -7,17 +7,15 @@
mhcflurry
=========
-Open source neural network models for peptide-MHC binding affinity prediction
-
-MHCflurry is a Python package for peptide/MHC I binding affinity
+Open source neural network models for peptide-MHC binding affinity predictionMHCflurry 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.
+"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
@@ -30,297 +28,297 @@ 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.
+ 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
+ pip install mhcflurry
Then download our datasets and trained models:
- mhcflurry-downloads fetch
+ mhcflurry-downloads fetch
From a checkout you can run the unit tests with:
- pip install nose nosetests .
+ 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
+ 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
+ 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:
+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.
+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.
+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 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
+ >>> 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]:
- # In[22]:
+ downloaded_predictor.predict_to_dataframe(allele="HLA-A0201", peptides=["SIINFEKL", "SIINFEQL"])
- import pandas import numpy import seaborn import logging from
- matplotlib import pyplot
- import mhcflurry
+ # In[7]:
- # # Download data and models
+ downloaded_predictor.predict_to_dataframe(alleles=["HLA-A0201", "HLA-B*57:01"], peptides=["SIINFEKL", "SIINFEQL"])
- # In[2]:
- get_ipython().system('mhcflurry-downloads fetch')
+ # In[8]:
- # # Making predictions with Class1AffinityPredictor
+ downloaded_predictor.predict_to_dataframe(
+ allele="HLA-A0201",
+ peptides=["SIINFEKL", "SIINFEQL"],
+ include_individual_model_predictions=True)
- # In[3]:
- help(mhcflurry.Class1AffinityPredictor)
+ # In[9]:
- # In[4]:
+ 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
- downloaded_predictor = mhcflurry.Class1AffinityPredictor.load()
- # In[5]:
+ # # Instantiating a `Class1AffinityPredictor` from a saved model on disk
- downloaded_predictor.predict(allele="HLA-A0201",
- peptides=["SIINFEKL", "SIINFEQL"])
+ # In[10]:
- # In[6]:
+ models_dir = mhcflurry.downloads.get_path("models_class1", "models")
+ models_dir
- downloaded_predictor.predict_to_dataframe(allele="HLA-A0201",
- peptides=["SIINFEKL", "SIINFEQL"])
- # In[7]:
+ # In[11]:
- downloaded_predictor.predict_to_dataframe(alleles=["HLA-A0201",
- "HLA-B*57:01"], peptides=["SIINFEKL", "SIINFEQL"])
+ # 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")
- # In[8]:
- downloaded_predictor.predict_to_dataframe(
- allele="HLA-A0201", peptides=["SIINFEKL", "SIINFEQL"],
- include_individual_model_predictions=True)
+ # # Fit a model: first load some data
- # In[9]:
+ # In[12]:
- 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
+ # 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
- # # Instantiating a Class1AffinityPredictor from a saved model on
- disk
- # In[10]:
+ # In[13]:
- models_dir = mhcflurry.downloads.get_path("models_class1", "models")
- models_dir
+ data_df = pandas.read_csv(data_path)
+ data_df
- # 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: Low level `Class1NeuralNetwork` interface
- # # Fit a model: first load some data
+ # In[14]:
- # In[12]:
+ # We'll use mostly the default hyperparameters here. Could also specify them as kwargs.
+ new_model = mhcflurry.Class1NeuralNetwork(layer_sizes=[16])
+ new_model.hyperparameters
- # 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]:
+ # In[16]:
- data_df = pandas.read_csv(data_path) data_df
+ 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)')
- # # Fit a model: Low level Class1NeuralNetwork interface
- # In[14]:
+ # In[17]:
- # We'll use mostly the default hyperparameters here. Could also
- specify them as kwargs. new_model =
- mhcflurry.Class1NeuralNetwork(layer_sizes=[16])
- new_model.hyperparameters
+ new_model.predict(["SYNPEPII"])
- # 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)
+ # # Fit a model: high level `Class1AffinityPredictor` interface
- ] get_ipython().magic('time new_model.fit(train_data.peptide.values,
- train_data.measurement_value.values)')
+ # In[18]:
- # In[17]:
+ affinity_predictor = mhcflurry.Class1AffinityPredictor()
- new_model.predict(["SYNPEPII"])
+ # 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",
+ )
- # # Fit a model: high level Class1AffinityPredictor interface
- # In[18]:
+ # In[19]:
- affinity_predictor = mhcflurry.Class1AffinityPredictor()
+ affinity_predictor.predict(["SYNPEPII"], allele="HLA-B*57:01")
- # 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]:
+ # # Save and restore the fit model
- affinity_predictor.predict(["SYNPEPII"], allele="HLA-B*57:01")
+ # In[20]:
- # # Save and restore the fit model
+ get_ipython().system('mkdir /tmp/saved-affinity-predictor')
+ affinity_predictor.save("/tmp/saved-affinity-predictor")
+ get_ipython().system('ls /tmp/saved-affinity-predictor')
- # 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]:
- # In[21]:
+ affinity_predictor2 = mhcflurry.Class1AffinityPredictor.load("/tmp/saved-affinity-predictor")
+ affinity_predictor2.predict(["SYNPEPII"], allele="HLA-B*57:01")
- 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
+ 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
--
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