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https://github.com/bigbio/py-pgatk
Python tools for proteogenomics analysis toolkit
https://github.com/bigbio/py-pgatk
ensembl mass-spectrometry proteogenomics proteogenomics-analysis-toolkit proteomics python vcf
Last synced: 3 months ago
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Python tools for proteogenomics analysis toolkit
- Host: GitHub
- URL: https://github.com/bigbio/py-pgatk
- Owner: bigbio
- License: apache-2.0
- Created: 2019-03-12T22:25:27.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-04-26T10:18:12.000Z (10 months ago)
- Last Synced: 2024-04-26T11:29:38.846Z (10 months ago)
- Topics: ensembl, mass-spectrometry, proteogenomics, proteogenomics-analysis-toolkit, proteomics, python, vcf
- Language: Python
- Size: 125 MB
- Stars: 9
- Watchers: 6
- Forks: 11
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
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README
# ProteoGenomics Analysis Toolkit
[](http://bioconda.github.io/recipes/pypgatk/README.html)
[](https://www.codacy.com/gh/bigbio/py-pgatk/dashboard?utm_source=github.com&utm_medium=referral&utm_content=bigbio/py-pgatk&utm_campaign=Badge_Grade)
[](https://badge.fury.io/py/pypgatk)
**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.
# Requirements:
The package requirements vary depending on the way that you want to install it (you need one of the following):
- pip: if installation goes through pip, you will require Python3 and pip3 installed.
- 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).
- Docker container: to use pypgatk from its docker container you will need [Docker](https://docs.docker.com/install/) installed.
- Source code: to use and install from the source code directly, you will need to have git, Python3 and pip.
# Installation
## pip
You can install pypgatk with pip:
```
pip install pypgatk
```
## Bioconda
You 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)):
```
conda install pypgatk
```
## Available as a container
You 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 ``.
```
docker pull quay.io/biocontainers/pypgatk:
```
**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):
```
docker run -it quay.io/biocontainers/pypgatk:0.0.2--py_0
```
Inside the container, you can either use the Python interactive shell or the command line version (see below).
## Use latest source code
Alternatively, for the latest version, clone this repo and go into its directory, then execute `pip3 install .` :
```
git clone https://github.com/bigbio/py-pgatk
cd py-pgatk
# you might want to create a virtualenv for pypgatk before installing
pip3 install .
```
# Usage
The pypgatk design combines multiple modules and tools into one framework. All the possible commands are accessible using the commandline tool `pypgatk_cli.py`.
The library provides multiple commands to download, translate and generate protein sequence databases from reference and mutation genome databases.
```
$: pypgatk_cli -h
Usage: pypgatk [OPTIONS] COMMAND [ARGS]...
This is the main tool that give access to all commands and options
provided by the pypgatk
Options:
--version Show the version and exit.
-h, --help Show this message and exit.
Commands:
cbioportal-downloader Command to download the the cbioportal studies
cbioportal-to-proteindb Command to translate cbioportal mutation data into
proteindb
cosmic-downloader Command to download the cosmic mutation database
cosmic-to-proteindb Command to translate Cosmic mutation data into
proteindb
dnaseq-to-proteindb Generate peptides based on DNA sequences
ensembl-check Command to check ensembl database for stop codons,
gaps
ensembl-downloader Command to download the ensembl information
generate-decoy Create decoy protein sequences using multiple
methods DecoyPYrat, Reverse/Shuffled Proteins.
generate-deeplc Generate input for deepLC tool from idXML,mzTab or
consensusXML
msrescore-configuration Command to generate the msrescore configuration
file from idXML
peptide-class-fdr Command to compute the Peptide class FDR
threeframe-translation Command to perform 3'frame translation
vcf-to-proteindb Generate peptides based on DNA variants VCF files
```
# Full Documentation
[https://pgatk.readthedocs.io/en/latest/pypgatk.html](https://pgatk.readthedocs.io/en/latest/pypgatk.html)
## Cite as
Husen M Umer, Enrique Audain, Yafeng Zhu, Julianus Pfeuffer, Timo Sachsenberg, Janne Lehtiö, Rui M Branca, Yasset Perez-Riverol
Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides
Bioinformatics, Volume 38, Issue 5, 1 March 2022, Pages 1470–1472
https://doi.org/10.1093/bioinformatics/btab838