From 883e106b036aa70b38af6d489522174d0aca6e7b Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Fri, 22 Dec 2017 01:52:58 -0500
Subject: [PATCH] remove docs/package_readme
---
docs/README.md | 11 -
docs/package_readme/readme.generated.txt | 628 -----------------------
docs/package_readme/readme.template.rst | 183 -------
docs/package_readme/readme_header.rst | 13 -
4 files changed, 835 deletions(-)
delete mode 100644 docs/package_readme/readme.generated.txt
delete mode 100644 docs/package_readme/readme.template.rst
delete mode 100644 docs/package_readme/readme_header.rst
diff --git a/docs/README.md b/docs/README.md
index 78fdc827..b6330f59 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -12,14 +12,3 @@ $ make generate html
Documentation is written to the _build/ directory. These files should not be
checked into the repo.
-
-We have experimented with using the documentation system to generate the mhcflurry
-package level README, but this is not currently in use. To build the readme, run:
-
-```
-$ make readme
-```
-
-This will write `docs/package_readme/readme.generated.txt`. The main
-[README.rst](../README.rst) could be symlinked to this file at a later point.
-
diff --git a/docs/package_readme/readme.generated.txt b/docs/package_readme/readme.generated.txt
deleted file mode 100644
index 2ba05d58..00000000
--- a/docs/package_readme/readme.generated.txt
+++ /dev/null
@@ -1,628 +0,0 @@
-:orphan:
-
-.. image:: https://travis-ci.org/hammerlab/mhcflurry.svg?branch=master
- :target: https://travis-ci.org/hammerlab/mhcflurry
-
-.. image:: https://coveralls.io/repos/github/hammerlab/mhcflurry/badge.svg?branch=master
- :target: https://coveralls.io/github/hammerlab/mhcflurry
-
-mhcflurry
-===================
-
-Open source neural network models for peptide-MHC binding affinity prediction
-
-MHCflurry is an open source package for peptide/MHC I binding affinity
-prediction. It provides competitive accuracy with a fast and
-documented implementation.
-
-You can download pre-trained MHCflurry models fit to affinity
-measurements deposited in IEDB or train a MHCflurry predictor on your
-own data.
-
-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 (see Supported alleles
-and peptide lengths).
-
-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 tutorial
-=====================
-
-
-Downloading models
-******************
-
-Most users will use pre-trained MHCflurry models that we release.
-These models are distributed separately from the pip package and may
-be downloaded with the mhcflurry-downloads tool:
-
- $ mhcflurry-downloads fetch models_class1
-
-Files downloaded with mhcflurry-downloads are stored in a platform-
-specific directory. To get the path to downloaded data, you can use:
-
- $ mhcflurry-downloads path models_class1
- /Users/tim/Library/Application Support/mhcflurry/4/1.0.0/models_class1/
-
-We also release a few other “downloads,” such as curated training data
-and some experimental models. To see what’s available and what you
-have downloaded, run:
-
- $ mhcflurry-downloads info
- Environment variables
- MHCFLURRY_DATA_DIR [unset or empty]
- MHCFLURRY_DOWNLOADS_CURRENT_RELEASE [unset or empty]
- MHCFLURRY_DOWNLOADS_DIR [unset or empty]
-
- Configuration
- current release = 1.0.0
- downloads dir = '/Users/tim/Library/Application Support/mhcflurry/4/1.0.0' [exists]
-
- DOWNLOAD NAME DOWNLOADED? DEFAULT? URL
- models_class1 YES YES http://github.com/hammerlab/mhcflurry/releases/download/pre-1.0/models_class1.tar.bz2
- models_class1_experiments1 NO NO http://github.com/hammerlab/mhcflurry/releases/download/pre-1.0/models_class1_experiments1.tar.bz2
- cross_validation_class1 YES NO http://github.com/hammerlab/mhcflurry/releases/download/pre-1.0/cross_validation_class1.tar.bz2
- data_iedb NO NO https://github.com/hammerlab/mhcflurry/releases/download/pre-1.0/data_iedb.tar.bz2
- data_kim2014 NO NO http://github.com/hammerlab/mhcflurry/releases/download/0.9.1/data_kim2014.tar.bz2
- data_curated YES YES https://github.com/hammerlab/mhcflurry/releases/download/pre-1.0/data_curated.tar.bz2
-
-Note: The code we use for *generating* the downloads is in the
- "downloads_generation" directory in the repository.
-
-
-Generating predictions
-**********************
-
-The mhcflurry-predict command generates predictions from the command-
-line. By default it will use the pre-trained models you downloaded
-above; other models can be used by specifying the "--models" argument.
-
-Running:
-
- $ mhcflurry-predict
- --alleles HLA-A0201 HLA-A0301
- --peptides SIINFEKL SIINFEKD SIINFEKQ
- --out /tmp/predictions.csv
- Wrote: /tmp/predictions.csv
-
-results in a file like this:
-
- $ cat /tmp/predictions.csv
- allele,peptide,mhcflurry_prediction,mhcflurry_prediction_low,mhcflurry_prediction_high,mhcflurry_prediction_percentile
- HLA-A0201,SIINFEKL,4899.04784343,2767.76365395,7269.68364294,6.5097875
- HLA-A0201,SIINFEKD,21050.420243,16834.6585914,24129.0460917,34.297175
- HLA-A0201,SIINFEKQ,21048.4726578,16736.5612549,24111.0131144,34.297175
- HLA-A0301,SIINFEKL,28227.2989092,24826.3079098,32714.285974,33.9512125
- HLA-A0301,SIINFEKD,30816.7212184,27685.5084708,36037.3259046,41.225775
- HLA-A0301,SIINFEKQ,24183.0210465,19346.154182,32263.7124753,24.8109625
-
-The predictions are given as affinities (KD) in nM in the
-"mhcflurry_prediction" column. The other fields give the 5-95
-percentile predictions across the models in the ensemble and the
-quantile of the affinity prediction among a large number of random
-peptides tested on that allele.
-
-The predictions shown above were generated with MHCflurry 1.0.0.
-Different versions of MHCflurry can give considerably different
-results. Even on the same version, exact predictions may vary (up to
-about 1 nM) depending on the Keras backend and other details.
-
-In most cases you’ll want to specify the input as a CSV file instead
-of passing peptides and alleles as commandline arguments. See
-mhcflurry-predict docs.
-
-
-Fitting your own models
-***********************
-
-The mhcflurry-class1-train-allele-specific-models command is used to
-fit models to training data. The models we release with MHCflurry are
-trained with a command like:
-
- $ mhcflurry-class1-train-allele-specific-models \
- --data TRAINING_DATA.csv \
- --hyperparameters hyperparameters.yaml \
- --percent-rank-calibration-num-peptides-per-length 1000000 \
- --min-measurements-per-allele 75 \
- --out-models-dir models
-
-MHCflurry predictors are serialized to disk as many files in a
-directory. The command above will write the models to the output
-directory specified by the "--out-models-dir" argument. This directory
-has files like:
-
- manifest.csv
- percent_ranks.csv
- weights_BOLA-6*13:01-0-1e6e7c0610ac68f8.npz
- ...
- weights_PATR-B*24:01-0-e12e0ee723833110.npz
- weights_PATR-B*24:01-0-ec4a36529321d868.npz
- weights_PATR-B*24:01-0-fd5a340098d3a9f4.npz
-
-The "manifest.csv" file gives metadata for all the models used in the
-predictor. There will be a "weights_..." file for each model giving
-its weights (the parameters for the neural network). The
-"percent_ranks.csv" stores a histogram of model predictions for each
-allele over a large number of random peptides. It is used for
-generating the percent ranks at prediction time.
-
-To call mhcflurry-class1-train-allele-specific-models you’ll need some
-training data. The data we use for our released predictors can be
-downloaded with mhcflurry-downloads:
-
- $ mhcflurry-downloads fetch data_curated
-
-It looks like this:
-
- $ bzcat "$(mhcflurry-downloads path data_curated)/curated_training_data.csv.bz2" | head -n 3
- allele,peptide,measurement_value,measurement_type,measurement_source,original_allele
- BoLA-1*21:01,AENDTLVVSV,7817.0,quantitative,Barlow - purified MHC/competitive/fluorescence,BoLA-1*02101
- BoLA-1*21:01,NQFNGGCLLV,1086.0,quantitative,Barlow - purified MHC/direct/fluorescence,BoLA-1*02101
-
-
-Scanning protein sequences for predicted epitopes
-*************************************************
-
-The mhctools package provides support for scanning protein sequences
-to find predicted epitopes. It supports MHCflurry as well as other
-binding predictors. Here is an example.
-
-First, install "mhctools" if it is not already installed:
-
- $ pip install mhctools
-
-We’ll generate predictions across "example.fasta", a FASTA file with
-two short sequences:
-
- >protein1
- MDSKGSSQKGSRLLLLLVVSNLLLCQGVVSTPVCPNGPGNCQV
- EMFNEFDKRYAQGKGFITMALNSCHTSSLPTPEDKEQAQQTHH
- >protein2
- VTEVRGMKGAPDAILSRAIEIEEENKRLLEGMEMIFGQVIPGA
- ARYSAFYNLLHCLRRDSSKIDTYLKLLNCRIIYNNNC
-
-Here’s the "mhctools" invocation. See "mhctools -h" for more
-information.
-
- $ mhctools
- --mhc-predictor mhcflurry
- --input-fasta-file example.fasta
- --mhc-alleles A02:01,A03:01
- --mhc-peptide-lengths 8,9,10,11
- --extract-subsequences
- --output-csv /tmp/subsequence_predictions.csv
- 2017-12-22 01:12:44,974 - mhctools.cli.args - INFO - Building MHC binding prediction type for alleles ['HLA-A*02:01', 'HLA-A*03:01'] and epitope lengths [8, 9, 10, 11]
- 2017-12-22 01:12:48,868 - mhctools.mhcflurry - INFO - BindingPrediction(peptide='AARYSAFY', allele='HLA-A*03:01', affinity=5744.3443, percentile_rank=None, source_sequence_name=None, offset=0, prediction_method_name='mhcflurry')
- ...
-
- [1192 rows x 8 columns]
-
-This will write a file giving predictions for all subsequences of the
-specified lengths:
-
- $ head -n 3 /tmp/subsequence_predictions.csv
- ,source_sequence_name,offset,peptide,allele,affinity,percentile_rank,prediction_method_name,length
- 0,protein2,42,AARYSAFY,HLA-A*03:01,5744.3442744,,mhcflurry,8
- 1,protein2,42,AARYSAFYN,HLA-A*03:01,10576.5364408,,mhcflurry,9
-
-
-Python library tutorial
-=======================
-
-
-Predicting
-**********
-
-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. Use the "load" static method to load a trained predictor
-from disk. With no arguments this method will load the predictor
-released with MHCflurry (see Downloading models). If you pass a path
-to a models directory, then it will load that predictor instead.
-
- >>> from mhcflurry import Class1AffinityPredictor
- >>> predictor = Class1AffinityPredictor.load()
- >>> predictor.supported_alleles[:10]
- ['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']
-
-With a predictor loaded we can now generate some binding predictions:
-
- >>> predictor.predict(allele="HLA-A0201", peptides=["SIINFEKL", "SIINFEQL"])
- /Users/tim/miniconda3/envs/py2k/lib/python2.7/site-packages/h5py/__init__.py:34: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 80
- from ._conv import register_converters as _register_converters
- /Users/tim/miniconda3/envs/py2k/lib/python2.7/site-packages/h5py/__init__.py:43: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 80
- from . import h5a, h5d, h5ds, h5f, h5fd, h5g, h5r, h5s, h5t, h5p, h5z
- /Users/tim/miniconda3/envs/py2k/lib/python2.7/site-packages/h5py/_hl/group.py:21: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 80
- from .. import h5g, h5i, h5o, h5r, h5t, h5l, h5p
- Using TensorFlow backend.
- array([ 4899.04784343, 5685.25682682])
-
-Note: MHCflurry normalizes allele names using the mhcnames package.
- Names like "HLA-A0201" or "A*02:01" will be normalized to
- "HLA-A*02:01", so most naming conventions can be used with methods
- such as "predict".
-
-For more detailed results, we can use "predict_to_dataframe".
-
- >>> predictor.predict_to_dataframe(allele="HLA-A0201", peptides=["SIINFEKL", "SIINFEQL"])
- allele peptide prediction prediction_low prediction_high \
- 0 HLA-A0201 SIINFEKL 4899.047843 2767.763654 7269.683643
- 1 HLA-A0201 SIINFEQL 5685.256827 3815.923563 7476.714466
-
- prediction_percentile
- 0 6.509787
- 1 7.436687
-
-Instead of a single allele and multiple peptides, we may need
-predictions for allele/peptide pairs. We can predict across pairs by
-specifying the "alleles" argument instead of "allele". The list of
-alleles must be the same length as the list of peptides (i.e. it is
-predicting over pairs, *not* taking the cross product).
-
- >>> predictor.predict(alleles=["HLA-A0201", "HLA-B*57:01"], peptides=["SIINFEKL", "SIINFEQL"])
- array([ 4899.04794216, 26704.22011499])
-
-
-Training
-********
-
-Let’s fit our own MHCflurry predictor. First we need some training
-data. If you haven’t already, run this in a shell to download the
-MHCflurry training data:
-
- $ mhcflurry-downloads fetch data_curated
-
-We can get the path to this data from Python using
-"mhcflurry.downloads.get_path":
-
- >>> from mhcflurry.downloads import get_path
- >>> data_path = get_path("data_curated", "curated_training_data.csv.bz2")
- >>> data_path
- '/Users/tim/Library/Application Support/mhcflurry/4/1.0.0/data_curated/curated_training_data.csv.bz2'
-
-Now let’s load it with pandas and filter to reasonably-sized peptides:
-
- >>> import pandas
- >>> df = pandas.read_csv(data_path)
- >>> df = df.loc[(df.peptide.str.len() >= 8) & (df.peptide.str.len() <= 15)]
- >>> df.head(5)
- allele peptide measurement_value measurement_type \
- 0 BoLA-1*21:01 AENDTLVVSV 7817.0 quantitative
- 1 BoLA-1*21:01 NQFNGGCLLV 1086.0 quantitative
- 2 BoLA-2*08:01 AAHCIHAEW 21.0 quantitative
- 3 BoLA-2*08:01 AAKHMSNTY 1299.0 quantitative
- 4 BoLA-2*08:01 DSYAYMRNGW 2.0 quantitative
-
- measurement_source original_allele
- 0 Barlow - purified MHC/competitive/fluorescence BoLA-1*02101
- 1 Barlow - purified MHC/direct/fluorescence BoLA-1*02101
- 2 Barlow - purified MHC/direct/fluorescence BoLA-2*00801
- 3 Barlow - purified MHC/direct/fluorescence BoLA-2*00801
- 4 Barlow - purified MHC/direct/fluorescence BoLA-2*00801
-
-We’ll make an untrained "Class1AffinityPredictor" and then call
-"fit_allele_specific_predictors" to fit some models.
-
- >>> new_predictor = Class1AffinityPredictor()
- >>> single_allele_train_data = df.loc[df.allele == "HLA-B*57:01"].sample(100)
- >>> new_predictor.fit_allele_specific_predictors(
- ... n_models=1,
- ... architecture_hyperparameters={
- ... "layer_sizes": [16],
- ... "max_epochs": 5,
- ... "random_negative_constant": 5,
- ... },
- ... peptides=single_allele_train_data.peptide.values,
- ... affinities=single_allele_train_data.measurement_value.values,
- ... allele="HLA-B*57:01")
- Train on 112 samples, validate on 28 samples
- Epoch 1/1
-
- 112/112 [==============================] - 0s 3ms/step - loss: 0.3730 - val_loss: 0.3472
- Epoch 0 / 5: loss=0.373015. Min val loss (None) at epoch None
- Train on 112 samples, validate on 28 samples
- Epoch 1/1
-
- 112/112 [==============================] - 0s 38us/step - loss: 0.3508 - val_loss: 0.3345
- Train on 112 samples, validate on 28 samples
- Epoch 1/1
-
- 112/112 [==============================] - 0s 37us/step - loss: 0.3375 - val_loss: 0.3218
- Train on 112 samples, validate on 28 samples
- Epoch 1/1
-
- 112/112 [==============================] - 0s 36us/step - loss: 0.3227 - val_loss: 0.3092
- Train on 112 samples, validate on 28 samples
- Epoch 1/1
-
- 112/112 [==============================] - 0s 37us/step - loss: 0.3104 - val_loss: 0.2970
- [<mhcflurry.class1_neural_network.Class1NeuralNetwork object at 0x11e28ad10>]
-
-The "fit_allele_specific_predictors" method can be called any number
-of times on the same instance to build up ensembles of models across
-alleles. The "architecture_hyperparameters" we specified are for
-demonstration purposes; to fit real models you would usually train for
-more epochs.
-
-Now we can generate predictions:
-
- >>> new_predictor.predict(["SYNPEPII"], allele="HLA-B*57:01")
- array([ 610.30706541])
-
-We can save our predictor to the specified directory on disk by
-running:
-
- >>> new_predictor.save("/tmp/new-predictor")
-
-and restore it:
-
- >>> new_predictor2 = Class1AffinityPredictor.load("/tmp/new-predictor")
- >>> new_predictor2.supported_alleles
- ['HLA-B*57:01']
-
-
-Lower level interface
-*********************
-
-The high-level "Class1AffinityPredictor" delegates to low-level
-"Class1NeuralNetwork" objects, each of which represents a single
-neural network. The purpose of "Class1AffinityPredictor" is to
-implement several important features:
-
-ensembles
- More than one neural network can be used to generate each
- prediction. The predictions returned to the user are the geometric
- mean of the individual model predictions. This gives higher
- accuracy in most situations
-
-multiple alleles
- A "Class1NeuralNetwork" generates predictions for only a single
- allele. The "Class1AffinityPredictor" maps alleles to the relevant
- "Class1NeuralNetwork" instances
-
-serialization
- Loading and saving predictors is implemented in
- "Class1AffinityPredictor".
-
-Sometimes it’s easiest to work directly with "Class1NeuralNetwork".
-Here is a simple example of doing so:
-
- >>> from mhcflurry import Class1NeuralNetwork
- >>> network = Class1NeuralNetwork()
- >>> network.fit(
- ... single_allele_train_data.peptide.values,
- ... single_allele_train_data.measurement_value.values,
- ... verbose=0)
- Epoch 0 / 500: loss=0.533378. Min val loss (None) at epoch None
- Early stopping at epoch 124 / 500: loss=0.0115427. Min val loss (0.0719302743673) at epoch 113
- >>> network.predict(["SIINFEKLL"])
- array([ 23004.58985458])
-
-
-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
-
-
-mhcflurry
-=========
-
-Open source neural network models for peptide-MHC binding affinity
-prediction
-
-The adaptive immune system depends on the presentation of protein
-fragments by MHC molecules. Machine learning models of this
-interaction are used in studies of infectious diseases, autoimmune
-diseases, vaccine development, and cancer immunotherapy.
-
-MHCflurry supports Class I peptide/MHC binding affinity prediction
-using ensembles of allele-specific models. You can fit MHCflurry
-models to your own data or download models that we fit to data from
-IEDB and Kim 2014. Our combined dataset is available for download
-here.
-
-Pan-allelic prediction is supported in principle but is not yet
-performing accurately. Infrastructure for modeling other aspects of
-antigen processing is also implemented but experimental.
-
-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.
-
-
-Setup (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:
-
- nosetests .
-
-The MHCflurry predictors are implemented in Python using keras.
-
-MHCflurry works with both the tensorflow and theano keras backends.
-The tensorflow backend gives faster model-loading time but is
-undergoing more rapid development and sometimes hits issues. If you
-encounter tensorflow errors running MHCflurry, try setting this
-environment variable to switch to the theano backend:
-
- export KERAS_BACKEND=theano
-
-You may also needs to "pip install theano".
-
-
-Setup (conda)
-*************
-
-You can alternatively get up and running with a conda environment as
-follows:
-
- conda create -q -n mhcflurry-env python=3.6 'tensorflow>=1.1.0'
- source activate mhcflurry-env
-
-Then continue as above:
-
- pip install mhcflurry
- mhcflurry-downloads fetch
-
-If you wish to test your installation, you can install "nose" and run
-the tests from a checkout:
-
- pip install nose
- nosetests .
-
-
-Making predictions from the command-line
-****************************************
-
- $ 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.
-
-
-Making predictions from Python
-******************************
-
- >>> from mhcflurry import Class1AffinityPredictor
- >>> predictor = Class1AffinityPredictor.load()
- >>> predictor.predict_to_dataframe(peptides=['SIINFEKL'], allele='A0201')
-
-
- allele peptide prediction prediction_low prediction_high
- A0201 SIINFEKL 6029.084473 4474.103253 7771.297702
-
-See the class1_allele_specific_models.ipynb notebook for an overview
-of the Python API, including fitting your own predictors.
-
-
-Scanning protein sequences for predicted epitopes
-*************************************************
-
-The mhctools package provides support for scanning protein sequences
-to find predicted epitopes. It supports MHCflurry as well as other
-binding predictors. Here is an example:
-
- # First install mhctools if needed:
- pip install mhctools
-
- # Now generate predictions for protein sequences in FASTA format:
- mhctools \
- --mhc-predictor mhcflurry \
- --input-fasta-file INPUT.fasta \
- --mhc-alleles A02:01,A03:01 \
- --mhc-peptide-lengths 8,9,10,11 \
- --extract-subsequences \
- --out RESULT.csv
-
-
-Details on the downloadable models
-**********************************
-
-
-Environment variables
-*********************
-
-The path where MHCflurry looks for model weights and data can be set
-with the "MHCFLURRY_DOWNLOADS_DIR" environment variable. This
-directory should contain subdirectories like “models_class1”.
diff --git a/docs/package_readme/readme.template.rst b/docs/package_readme/readme.template.rst
deleted file mode 100644
index 52d542e4..00000000
--- a/docs/package_readme/readme.template.rst
+++ /dev/null
@@ -1,183 +0,0 @@
-.. include:: /intro.rst
- :start-line: 3
-
-.. include:: /commandline_tutorial.rst
-
-.. include:: /python_tutorial.rst
-
-.. include:: /models_supported_alleles.rst
-
-mhcflurry
-=========
-
-Open source neural network models for peptide-MHC binding affinity
-prediction
-
-The `adaptive immune
-system <https://en.wikipedia.org/wiki/Adaptive_immune_system>`__ depends
-on the presentation of protein fragments by
-`MHC <https://en.wikipedia.org/wiki/Major_histocompatibility_complex>`__
-molecules. Machine learning models of this interaction are used in
-studies of infectious diseases, autoimmune diseases, vaccine
-development, and cancer immunotherapy.
-
-MHCflurry supports Class I peptide/MHC binding affinity prediction using
-ensembles of allele-specific models. You can fit MHCflurry models to
-your own data or download models that we fit to data from
-`IEDB <http://www.iedb.org/home_v3.php>`__ and `Kim
-2014 <http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-241>`__.
-Our combined dataset is available for download
-`here <https://github.com/hammerlab/mhcflurry/releases/download/pre-1.0.0-alpha/data_curated.tar.bz2>`__.
-
-Pan-allelic prediction is supported in principle but is not yet
-performing accurately. Infrastructure for modeling other aspects of
-antigen processing is also implemented but experimental.
-
-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.
-
-Setup (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:
-
-::
-
- nosetests .
-
-The MHCflurry predictors are implemented in Python using
-`keras <https://keras.io>`__.
-
-MHCflurry works with both the tensorflow and theano keras backends. The
-tensorflow backend gives faster model-loading time but is undergoing
-more rapid development and sometimes hits issues. If you encounter
-tensorflow errors running MHCflurry, try setting this environment
-variable to switch to the theano backend:
-
-::
-
- export KERAS_BACKEND=theano
-
-You may also needs to ``pip install theano``.
-
-Setup (conda)
--------------
-
-You can alternatively get up and running with a
-`conda <https://conda.io/docs/>`__ environment as follows:
-
-::
-
- conda create -q -n mhcflurry-env python=3.6 'tensorflow>=1.1.0'
- source activate mhcflurry-env
-
-Then continue as above:
-
-::
-
- pip install mhcflurry
- mhcflurry-downloads fetch
-
-If you wish to test your installation, you can install ``nose`` and run
-the tests from a checkout:
-
-::
-
- pip install nose
- nosetests .
-
-Making predictions from the command-line
-----------------------------------------
-
-.. code:: shell
-
- $ 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.
-
-Making predictions from Python
-------------------------------
-
-.. code:: python
-
- >>> from mhcflurry import Class1AffinityPredictor
- >>> predictor = Class1AffinityPredictor.load()
- >>> predictor.predict_to_dataframe(peptides=['SIINFEKL'], allele='A0201')
-
-
- allele peptide prediction prediction_low prediction_high
- A0201 SIINFEKL 6029.084473 4474.103253 7771.297702
-
-See the
-`class1_allele_specific_models.ipynb <https://github.com/hammerlab/mhcflurry/blob/master/examples/class1_allele_specific_models.ipynb>`__
-notebook for an overview of the Python API, including fitting your own
-predictors.
-
-Scanning protein sequences for predicted epitopes
--------------------------------------------------
-
-The `mhctools <https://github.com/hammerlab/mhctools>`__ package
-provides support for scanning protein sequences to find predicted
-epitopes. It supports MHCflurry as well as other binding predictors.
-Here is an example:
-
-::
-
- # First install mhctools if needed:
- pip install mhctools
-
- # Now generate predictions for protein sequences in FASTA format:
- mhctools \
- --mhc-predictor mhcflurry \
- --input-fasta-file INPUT.fasta \
- --mhc-alleles A02:01,A03:01 \
- --mhc-peptide-lengths 8,9,10,11 \
- --extract-subsequences \
- --out RESULT.csv
-
-Details on the downloadable models
-----------------------------------
-
-Environment variables
----------------------
-
-The path where MHCflurry looks for model weights and data can be set
-with the ``MHCFLURRY_DOWNLOADS_DIR`` environment variable. This
-directory should contain subdirectories like "models_class1".
-
-.. |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
diff --git a/docs/package_readme/readme_header.rst b/docs/package_readme/readme_header.rst
deleted file mode 100644
index dcffcad6..00000000
--- a/docs/package_readme/readme_header.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-:orphan:
-
-.. image:: https://travis-ci.org/hammerlab/mhcflurry.svg?branch=master
- :target: https://travis-ci.org/hammerlab/mhcflurry
-
-.. image:: https://coveralls.io/repos/github/hammerlab/mhcflurry/badge.svg?branch=master
- :target: https://coveralls.io/github/hammerlab/mhcflurry
-
-mhcflurry
-===================
-
-Open source neural network models for peptide-MHC binding affinity prediction
-
--
GitLab