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        "author": "Tim O'Donnell and Alex Rubinsteyn",
        "author_email": "timodonnell@gmail.com",
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            "Development Status :: 5 - Production/Stable",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
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        "description": "[![Build Status](https://app.travis-ci.com/openvax/mhcflurry.svg?branch=master)](https://app.travis-ci.com/openvax/mhcflurry)\n\n# mhcflurry\n[MHC I](https://en.wikipedia.org/wiki/MHC_class_I) ligand\nprediction package with competitive accuracy and a fast and \n[documented](http://openvax.github.io/mhcflurry/) implementation.\n\nMHCflurry implements class I peptide/MHC binding affinity prediction. \nThe current version provides pan-MHC I predictors supporting any MHC\nallele of known sequence. MHCflurry runs on Python 3.4+ using the\n[tensorflow](https://www.tensorflow.org/) neural network library.\nIt exposes [command-line](http://openvax.github.io/mhcflurry/commandline_tutorial.html)\nand [Python library](http://openvax.github.io/mhcflurry/python_tutorial.html)\ninterfaces.\n\nStarting in version 1.6.0, MHCflurry also includes two expermental predictors,\nan \"antigen processing\" predictor that attempts to model MHC allele-independent\neffects such as proteosomal cleavage and a \"presentation\" predictor that\nintegrates processing predictions with binding affinity predictions to give a\ncomposite \"presentation score.\" Both models are trained on mass spec-identified\nMHC ligands. These models were updated to incorporate minor improvements\nfor the MHCflurry 2.0 release.\n\nIf you find MHCflurry useful in your research please cite:\n\n> 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\n\n> T. O\u2019Donnell, 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\n\nPlease file an issue if you have questions or encounter problems.\n\nHave a bugfix or other contribution? We would love your help. See our [contributing guidelines](CONTRIBUTING.md).\n\n## Installation (pip)\n\nInstall the package:\n\n```\n$ pip install mhcflurry\n```\n\nThen download our datasets and trained models:\n\n```\n$ mhcflurry-downloads fetch\n```\n\nYou can now generate predictions:\n\n```\n$ mhcflurry-predict \\\n       --alleles HLA-A0201 HLA-A0301 \\\n       --peptides SIINFEKL SIINFEKD SIINFEKQ \\\n       --out /tmp/predictions.csv\n\nWrote: /tmp/predictions.csv\n```\n\nOr scan protein sequences for potential epitopes:\n\n```\n$ mhcflurry-predict-scan \\\n        --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \\\n        --alleles HLA-A*02:01 \\\n        --out /tmp/predictions.csv\n\nWrote: /tmp/predictions.csv  \n```\n\n\nSee the [documentation](http://openvax.github.io/mhcflurry/) for more details.\n\n\n## Docker\nYou can also try the latest (GitHub master) version of MHCflurry using the Docker\nimage hosted on [Dockerhub](https://hub.docker.com/r/openvax/mhcflurry) by\nrunning:\n\n```\n$ docker run -p 9999:9999 --rm openvax/mhcflurry:latest\n``` \n\nThis will start a [jupyter](https://jupyter.org/) notebook server in an\nenvironment that has MHCflurry installed. Go to `http://localhost:9999` in a\nbrowser to use it.\n\nTo build the Docker image yourself, from a checkout run:\n\n```\n$ docker build -t mhcflurry:latest .\n$ docker run -p 9999:9999 --rm mhcflurry:latest\n```\n## Predicted sequence motifs\nSequence logos for the binding motifs learned by MHCflurry BA are available [here](https://openvax.github.io/mhcflurry-motifs/).\n\n## Common issues and fixes\n\n### Problems downloading data and models\nSome 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.\n\nTo do this, first get the URL(s) of the downloads you need using `mhcflurry-downloads url`:\n\n```\n$ mhcflurry-downloads url models_class1_presentation\nhttps://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```\n```\n\nThen make a directory and download the needed files to this directory:\n\n```\n$ mkdir downloads\n$ wget  --directory-prefix downloads https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```\n\nHTTP request sent, awaiting response... 200 OK\nLength: 72616448 (69M) [application/octet-stream]\nSaving to: 'downloads/models_class1_presentation.20200205.tar.bz2'\n```\n\nNow call `mhcflurry-downloads fetch` with the `--already-downloaded-dir` option to indicate that the downloads should be retrived from the specified directory:\n\n```\n$ mhcflurry-downloads fetch models_class1_presentation --already-downloaded-dir downloads\n```\n\n\n\n\n",
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