path: root/python-mhcflurry.spec

%global _empty_manifest_terminate_build 0
Name:		python-mhcflurry
Version:	2.0.6
Release:	1
Summary:	MHC Binding Predictor
License:	http://www.apache.org/licenses/LICENSE-2.0.html
URL:		https://github.com/openvax/mhcflurry
Source0:	https://mirrors.aliyun.com/pypi/web/packages/b6/8c/449b1b81d3731cd668df54ff0b14bf9083378717a09deaef8bf884be6cf2/mhcflurry-2.0.6.tar.gz
BuildArch:	noarch

Requires:	python3-six
Requires:	python3-pandas
Requires:	python3-appdirs
Requires:	python3-scikit-learn
Requires:	python3-mhcgnomes
Requires:	python3-pyyaml
Requires:	python3-tqdm
Requires:	python3-np-utils

%description
[![Build Status](https://app.travis-ci.com/openvax/mhcflurry.svg?branch=master)](https://app.travis-ci.com/openvax/mhcflurry)

# mhcflurry
[MHC I](https://en.wikipedia.org/wiki/MHC_class_I) ligand
prediction package with competitive accuracy and a fast and 
[documented](http://openvax.github.io/mhcflurry/) implementation.

MHCflurry implements class I peptide/MHC binding affinity prediction. 
The current version provides pan-MHC I predictors supporting any MHC
allele of known sequence. MHCflurry runs on Python 3.4+ using the
[tensorflow](https://www.tensorflow.org/) neural network library.
It exposes [command-line](http://openvax.github.io/mhcflurry/commandline_tutorial.html)
and [Python library](http://openvax.github.io/mhcflurry/python_tutorial.html)
interfaces.

Starting in version 1.6.0, MHCflurry also includes two expermental predictors,
an "antigen processing" predictor that attempts to model MHC allele-independent
effects such as proteosomal cleavage and a "presentation" predictor that
integrates processing predictions with binding affinity predictions to give a
composite "presentation score." Both models are trained on mass spec-identified
MHC ligands. These models were updated to incorporate minor improvements
for the MHCflurry 2.0 release.

If you find MHCflurry useful in your research please cite:

> T. O'Donnell, A. Rubinsteyn, U. Laserson. "MHCflurry 2.0: Improved pan-allele prediction of MHC I-presented peptides by incorporating antigen processing," *Cell Systems*, 2020. https://doi.org/10.1016/j.cels.2020.06.010

> T. O’Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," *Cell Systems*, 2018. https://doi.org/10.1016/j.cels.2018.05.014

Please file an issue if you have questions or encounter problems.

Have a bugfix or other contribution? We would love your help. See our [contributing guidelines](CONTRIBUTING.md).

## Installation (pip)

Install the package:

```
$ pip install mhcflurry
```

If you don't already have it, you will also need to install tensorflow version 2.2.0 or later. On most platforms you can do this with:

```
$ pip install tensorflow
```

If you are on Apple silicon (M1 processor), then you'll need to run `pip install tensorflow-macos` instead. See these [instructions](https://caffeinedev.medium.com/how-to-install-tensorflow-on-m1-mac-8e9b91d93706) for more info.

Next download our datasets and trained models:

```
$ mhcflurry-downloads fetch
```

You can now generate predictions:

```
$ mhcflurry-predict \
       --alleles HLA-A0201 HLA-A0301 \
       --peptides SIINFEKL SIINFEKD SIINFEKQ \
       --out /tmp/predictions.csv
       
Wrote: /tmp/predictions.csv
```

Or scan protein sequences for potential epitopes:

```
$ mhcflurry-predict-scan \
        --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
        --alleles HLA-A*02:01 \
        --out /tmp/predictions.csv
        
Wrote: /tmp/predictions.csv  
```


See the [documentation](http://openvax.github.io/mhcflurry/) for more details.


## Docker
You can also try the latest (GitHub master) version of MHCflurry using the Docker
image hosted on [Dockerhub](https://hub.docker.com/r/openvax/mhcflurry) by
running:

```
$ docker run -p 9999:9999 --rm openvax/mhcflurry:latest
``` 

This will start a [jupyter](https://jupyter.org/) notebook server in an
environment that has MHCflurry installed. Go to `http://localhost:9999` in a
browser to use it.

To build the Docker image yourself, from a checkout run:

```
$ docker build -t mhcflurry:latest .
$ docker run -p 9999:9999 --rm mhcflurry:latest
```
## Predicted sequence motifs
Sequence logos for the binding motifs learned by MHCflurry BA are available [here](https://openvax.github.io/mhcflurry-motifs/).

## Common issues and fixes

### Problems downloading data and models
Some users have reported HTTP connection issues when using `mhcflurry-downloads fetch`. As a workaround, you can download the data manually (e.g. using `wget`) and then use `mhcflurry-downloads` just to copy the data to the right place.

To do this, first get the URL(s) of the downloads you need using `mhcflurry-downloads url`:

```
$ mhcflurry-downloads url models_class1_presentation
https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
```

Then make a directory and download the needed files to this directory:

```
$ mkdir downloads
$ wget  --directory-prefix downloads https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```

HTTP request sent, awaiting response... 200 OK
Length: 72616448 (69M) [application/octet-stream]
Saving to: 'downloads/models_class1_presentation.20200205.tar.bz2'
```

Now call `mhcflurry-downloads fetch` with the `--already-downloaded-dir` option to indicate that the downloads should be retrived from the specified directory:

```
$ mhcflurry-downloads fetch models_class1_presentation --already-downloaded-dir downloads
```




%package -n python3-mhcflurry
Summary:	MHC Binding Predictor
Provides:	python-mhcflurry
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-mhcflurry
[![Build Status](https://app.travis-ci.com/openvax/mhcflurry.svg?branch=master)](https://app.travis-ci.com/openvax/mhcflurry)

# mhcflurry
[MHC I](https://en.wikipedia.org/wiki/MHC_class_I) ligand
prediction package with competitive accuracy and a fast and 
[documented](http://openvax.github.io/mhcflurry/) implementation.

MHCflurry implements class I peptide/MHC binding affinity prediction. 
The current version provides pan-MHC I predictors supporting any MHC
allele of known sequence. MHCflurry runs on Python 3.4+ using the
[tensorflow](https://www.tensorflow.org/) neural network library.
It exposes [command-line](http://openvax.github.io/mhcflurry/commandline_tutorial.html)
and [Python library](http://openvax.github.io/mhcflurry/python_tutorial.html)
interfaces.

Starting in version 1.6.0, MHCflurry also includes two expermental predictors,
an "antigen processing" predictor that attempts to model MHC allele-independent
effects such as proteosomal cleavage and a "presentation" predictor that
integrates processing predictions with binding affinity predictions to give a
composite "presentation score." Both models are trained on mass spec-identified
MHC ligands. These models were updated to incorporate minor improvements
for the MHCflurry 2.0 release.

If you find MHCflurry useful in your research please cite:

> T. O'Donnell, A. Rubinsteyn, U. Laserson. "MHCflurry 2.0: Improved pan-allele prediction of MHC I-presented peptides by incorporating antigen processing," *Cell Systems*, 2020. https://doi.org/10.1016/j.cels.2020.06.010

> T. O’Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," *Cell Systems*, 2018. https://doi.org/10.1016/j.cels.2018.05.014

Please file an issue if you have questions or encounter problems.

Have a bugfix or other contribution? We would love your help. See our [contributing guidelines](CONTRIBUTING.md).

## Installation (pip)

Install the package:

```
$ pip install mhcflurry
```

If you don't already have it, you will also need to install tensorflow version 2.2.0 or later. On most platforms you can do this with:

```
$ pip install tensorflow
```

If you are on Apple silicon (M1 processor), then you'll need to run `pip install tensorflow-macos` instead. See these [instructions](https://caffeinedev.medium.com/how-to-install-tensorflow-on-m1-mac-8e9b91d93706) for more info.

Next download our datasets and trained models:

```
$ mhcflurry-downloads fetch
```

You can now generate predictions:

```
$ mhcflurry-predict \
       --alleles HLA-A0201 HLA-A0301 \
       --peptides SIINFEKL SIINFEKD SIINFEKQ \
       --out /tmp/predictions.csv
       
Wrote: /tmp/predictions.csv
```

Or scan protein sequences for potential epitopes:

```
$ mhcflurry-predict-scan \
        --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
        --alleles HLA-A*02:01 \
        --out /tmp/predictions.csv
        
Wrote: /tmp/predictions.csv  
```


See the [documentation](http://openvax.github.io/mhcflurry/) for more details.


## Docker
You can also try the latest (GitHub master) version of MHCflurry using the Docker
image hosted on [Dockerhub](https://hub.docker.com/r/openvax/mhcflurry) by
running:

```
$ docker run -p 9999:9999 --rm openvax/mhcflurry:latest
``` 

This will start a [jupyter](https://jupyter.org/) notebook server in an
environment that has MHCflurry installed. Go to `http://localhost:9999` in a
browser to use it.

To build the Docker image yourself, from a checkout run:

```
$ docker build -t mhcflurry:latest .
$ docker run -p 9999:9999 --rm mhcflurry:latest
```
## Predicted sequence motifs
Sequence logos for the binding motifs learned by MHCflurry BA are available [here](https://openvax.github.io/mhcflurry-motifs/).

## Common issues and fixes

### Problems downloading data and models
Some users have reported HTTP connection issues when using `mhcflurry-downloads fetch`. As a workaround, you can download the data manually (e.g. using `wget`) and then use `mhcflurry-downloads` just to copy the data to the right place.

To do this, first get the URL(s) of the downloads you need using `mhcflurry-downloads url`:

```
$ mhcflurry-downloads url models_class1_presentation
https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
```

Then make a directory and download the needed files to this directory:

```
$ mkdir downloads
$ wget  --directory-prefix downloads https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```

HTTP request sent, awaiting response... 200 OK
Length: 72616448 (69M) [application/octet-stream]
Saving to: 'downloads/models_class1_presentation.20200205.tar.bz2'
```

Now call `mhcflurry-downloads fetch` with the `--already-downloaded-dir` option to indicate that the downloads should be retrived from the specified directory:

```
$ mhcflurry-downloads fetch models_class1_presentation --already-downloaded-dir downloads
```




%package help
Summary:	Development documents and examples for mhcflurry
Provides:	python3-mhcflurry-doc
%description help
[![Build Status](https://app.travis-ci.com/openvax/mhcflurry.svg?branch=master)](https://app.travis-ci.com/openvax/mhcflurry)

# mhcflurry
[MHC I](https://en.wikipedia.org/wiki/MHC_class_I) ligand
prediction package with competitive accuracy and a fast and 
[documented](http://openvax.github.io/mhcflurry/) implementation.

MHCflurry implements class I peptide/MHC binding affinity prediction. 
The current version provides pan-MHC I predictors supporting any MHC
allele of known sequence. MHCflurry runs on Python 3.4+ using the
[tensorflow](https://www.tensorflow.org/) neural network library.
It exposes [command-line](http://openvax.github.io/mhcflurry/commandline_tutorial.html)
and [Python library](http://openvax.github.io/mhcflurry/python_tutorial.html)
interfaces.

Starting in version 1.6.0, MHCflurry also includes two expermental predictors,
an "antigen processing" predictor that attempts to model MHC allele-independent
effects such as proteosomal cleavage and a "presentation" predictor that
integrates processing predictions with binding affinity predictions to give a
composite "presentation score." Both models are trained on mass spec-identified
MHC ligands. These models were updated to incorporate minor improvements
for the MHCflurry 2.0 release.

If you find MHCflurry useful in your research please cite:

> T. O'Donnell, A. Rubinsteyn, U. Laserson. "MHCflurry 2.0: Improved pan-allele prediction of MHC I-presented peptides by incorporating antigen processing," *Cell Systems*, 2020. https://doi.org/10.1016/j.cels.2020.06.010

> T. O’Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," *Cell Systems*, 2018. https://doi.org/10.1016/j.cels.2018.05.014

Please file an issue if you have questions or encounter problems.

Have a bugfix or other contribution? We would love your help. See our [contributing guidelines](CONTRIBUTING.md).

## Installation (pip)

Install the package:

```
$ pip install mhcflurry
```

If you don't already have it, you will also need to install tensorflow version 2.2.0 or later. On most platforms you can do this with:

```
$ pip install tensorflow
```

If you are on Apple silicon (M1 processor), then you'll need to run `pip install tensorflow-macos` instead. See these [instructions](https://caffeinedev.medium.com/how-to-install-tensorflow-on-m1-mac-8e9b91d93706) for more info.

Next download our datasets and trained models:

```
$ mhcflurry-downloads fetch
```

You can now generate predictions:

```
$ mhcflurry-predict \
       --alleles HLA-A0201 HLA-A0301 \
       --peptides SIINFEKL SIINFEKD SIINFEKQ \
       --out /tmp/predictions.csv
       
Wrote: /tmp/predictions.csv
```

Or scan protein sequences for potential epitopes:

```
$ mhcflurry-predict-scan \
        --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
        --alleles HLA-A*02:01 \
        --out /tmp/predictions.csv
        
Wrote: /tmp/predictions.csv  
```


See the [documentation](http://openvax.github.io/mhcflurry/) for more details.


## Docker
You can also try the latest (GitHub master) version of MHCflurry using the Docker
image hosted on [Dockerhub](https://hub.docker.com/r/openvax/mhcflurry) by
running:

```
$ docker run -p 9999:9999 --rm openvax/mhcflurry:latest
``` 

This will start a [jupyter](https://jupyter.org/) notebook server in an
environment that has MHCflurry installed. Go to `http://localhost:9999` in a
browser to use it.

To build the Docker image yourself, from a checkout run:

```
$ docker build -t mhcflurry:latest .
$ docker run -p 9999:9999 --rm mhcflurry:latest
```
## Predicted sequence motifs
Sequence logos for the binding motifs learned by MHCflurry BA are available [here](https://openvax.github.io/mhcflurry-motifs/).

## Common issues and fixes

### Problems downloading data and models
Some users have reported HTTP connection issues when using `mhcflurry-downloads fetch`. As a workaround, you can download the data manually (e.g. using `wget`) and then use `mhcflurry-downloads` just to copy the data to the right place.

To do this, first get the URL(s) of the downloads you need using `mhcflurry-downloads url`:

```
$ mhcflurry-downloads url models_class1_presentation
https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
```

Then make a directory and download the needed files to this directory:

```
$ mkdir downloads
$ wget  --directory-prefix downloads https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```

HTTP request sent, awaiting response... 200 OK
Length: 72616448 (69M) [application/octet-stream]
Saving to: 'downloads/models_class1_presentation.20200205.tar.bz2'
```

Now call `mhcflurry-downloads fetch` with the `--already-downloaded-dir` option to indicate that the downloads should be retrived from the specified directory:

```
$ mhcflurry-downloads fetch models_class1_presentation --already-downloaded-dir downloads
```




%prep
%autosetup -n mhcflurry-2.0.6

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-mhcflurry -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.6-1
- Package Spec generated