From b44e36fe89300451065af9157f019be95e9f9542 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Mon, 29 May 2023 13:04:26 +0000 Subject: automatic import of python-mhcflurry --- .gitignore | 1 + python-mhcflurry.spec | 479 ++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 481 insertions(+) create mode 100644 python-mhcflurry.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..ff50b25 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/mhcflurry-2.0.6.tar.gz diff --git a/python-mhcflurry.spec b/python-mhcflurry.spec new file mode 100644 index 0000000..ea14e20 --- /dev/null +++ b/python-mhcflurry.spec @@ -0,0 +1,479 @@ +%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.nju.edu.cn/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 +* Mon May 29 2023 Python_Bot - 2.0.6-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..3eef3f8 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +61d3cd5ecaa0aaac013b7ec0802e7923 mhcflurry-2.0.6.tar.gz -- cgit v1.2.3