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bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/pypgatk/README.html)\n[![Codacy Badge](https://app.codacy.com/project/badge/Grade/f6d030fd7d69413987f7265a01193324)](https://www.codacy.com/gh/bigbio/py-pgatk/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=bigbio/py-pgatk\u0026amp;utm_campaign=Badge_Grade)\n[![PyPI version](https://badge.fury.io/py/pypgatk.svg)](https://badge.fury.io/py/pypgatk)\n![PyPI - Downloads](https://img.shields.io/pypi/dm/pypgatk)\n\n**pypgatk** is a Python library - part of the [ProteoGenomics Analysis Toolkit](https://pgatk.readthedocs.io/en/latest). It provides different bioinformatics tools for proteogenomics data analysis.\n\n# Requirements:\n\nThe package requirements vary depending on the way that you want to install it (you need one of the following):\n\n- pip: if installation goes through pip, you will require Python3 and pip3 installed.\n- Bioconda: if installation goes through Bioconda, you will require that [conda is installed and configured to use bioconda channels](https://bioconda.github.io/user/index.html).\n- Docker container: to use pypgatk from its docker container you will need [Docker](https://docs.docker.com/install/) installed.\n- Source code: to use and install from the source code directly, you will need to have git, Python3 and pip.\n\n# Installation\n\n## pip\n\nYou can install pypgatk with pip:\n\n```\npip install pypgatk\n```\n\n## Bioconda\n\nYou can install pypgatk with bioconda (please setup conda and the bioconda channel if you haven't first, as explained [here](https://bioconda.github.io/user/index.html)):\n\n```\nconda install pypgatk\n```\n\n## Available as a container\n\nYou can use the pypgatk tool already setup on a Docker container. You need to choose from the available tags [here](https://quay.io/repository/biocontainers/pypgatk?tab=tags) and replace it in the call below where it says `\u003ctag\u003e`.\n\n```\ndocker pull quay.io/biocontainers/pypgatk:\u003ctag\u003e\n```\n\n**NOTE**: Please note that Biocontainers containers do not have a latest tag, as such a docker pull/run without defining the tag will fail. For instance, a valid call would be (for version 0.0.2):\n\n```\ndocker run -it quay.io/biocontainers/pypgatk:0.0.2--py_0\n```\n\nInside the container, you can either use the Python interactive shell or the command line version (see below).\n\n\n## Use latest source code\n\nAlternatively, for the latest version, clone this repo and go into its directory, then execute `pip3 install .` :\n\n```\ngit clone https://github.com/bigbio/py-pgatk\ncd py-pgatk\n# you might want to create a virtualenv for pypgatk before installing\npip3 install .\n```\n\n# Usage\n\nThe pypgatk design combines multiple modules and tools into one framework. All the possible commands are accessible using the commandline tool `pypgatkc.py`.\n\nThe library provides multiple commands to download, translate and generate protein sequence databases from reference and mutation genome databases.\n\n```\n$: pypgatk -h\n\nUsage: pypgatk [OPTIONS] COMMAND [ARGS]...\n\n  This is the main tool that give access to all commands and options\n  provided by the pypgatk\n\nOptions:\n  --version   Show the version and exit.\n  -h, --help  Show this message and exit.\n\nCommands:\n  cbioportal-downloader    Command to download the the cbioportal studies\n  cbioportal-to-proteindb  Command to translate cbioportal mutation data into\n                           proteindb\n  cosmic-downloader        Command to download the cosmic mutation database\n  cosmic-to-proteindb      Command to translate Cosmic mutation data into\n                           proteindb\n  dnaseq-to-proteindb      Generate peptides based on DNA sequences\n  ensembl-check            Command to check ensembl database for stop codons,\n                           gaps\n  ensembl-downloader       Command to download the ensembl information\n  generate-decoy           Create decoy protein sequences using multiple\n                           methods DecoyPYrat, Reverse/Shuffled Proteins.\n  generate-deeplc          Generate input for deepLC tool from idXML,mzTab or\n                           consensusXML\n  msrescore-configuration  Command to generate the msrescore configuration\n                           file from idXML\n  peptide-class-fdr        Command to compute the Peptide class FDR\n  threeframe-translation   Command to perform 3'frame translation\n  vcf-to-proteindb         Generate peptides based on DNA variants VCF files\n\n```\n\n# Full Documentation\n\n[https://pgatk.readthedocs.io/en/latest/pypgatk.html](https://pgatk.readthedocs.io/en/latest/pypgatk.html)\n\n## Cite as\nHusen M Umer, Enrique Audain, Yafeng Zhu, Julianus Pfeuffer, Timo Sachsenberg, Janne Lehtiö, Rui M Branca, Yasset Perez-Riverol\nGeneration of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides\nBioinformatics, Volume 38, Issue 5, 1 March 2022, Pages 1470–1472\nhttps://doi.org/10.1093/bioinformatics/btab838\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbigbio%2Fpy-pgatk","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbigbio%2Fpy-pgatk","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbigbio%2Fpy-pgatk/lists"}