%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 * Wed May 31 2023 Python_Bot - 2.0.6-1 - Package Spec generated