{"id":13637871,"url":"https://github.com/deeptools/pyGenomeTracks","last_synced_at":"2025-04-19T17:32:30.777Z","repository":{"id":26363887,"uuid":"108555712","full_name":"deeptools/pyGenomeTracks","owner":"deeptools","description":"python module to plot beautiful and highly customizable genome browser tracks","archived":false,"fork":false,"pushed_at":"2024-07-10T07:37:52.000Z","size":190422,"stargazers_count":758,"open_issues_count":30,"forks_count":111,"subscribers_count":30,"default_branch":"master","last_synced_at":"2024-10-03T06:44:45.583Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/deeptools.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-10-27T14:23:59.000Z","updated_at":"2024-09-30T18:08:15.000Z","dependencies_parsed_at":"2023-02-12T23:15:59.060Z","dependency_job_id":"12a8d69d-e1fb-4d37-a94e-b69887f43691","html_url":"https://github.com/deeptools/pyGenomeTracks","commit_stats":{"total_commits":1275,"total_committers":16,"mean_commits":79.6875,"dds":"0.18509803921568624","last_synced_commit":"b8918d43b0bd1ca9dd2939fdd0d93a5a297d0453"},"previous_names":[],"tags_count":18,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deeptools%2FpyGenomeTracks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deeptools%2FpyGenomeTracks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deeptools%2FpyGenomeTracks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deeptools%2FpyGenomeTracks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/deeptools","download_url":"https://codeload.github.com/deeptools/pyGenomeTracks/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223804985,"owners_count":17205835,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-02T01:00:35.957Z","updated_at":"2024-11-09T08:30:28.425Z","avatar_url":"https://github.com/deeptools.png","language":"Python","funding_links":[],"categories":["Uncategorized","Epigenomics","Static"],"sub_categories":["Uncategorized","Expression"],"readme":"[![PyPI Version](https://img.shields.io/pypi/v/pyGenomeTracks.svg?style=plastic)](https://pypi.org/project/pyGenomeTracks/) [![bioconda-badge](https://img.shields.io/conda/vn/bioconda/pyGenomeTracks.svg?style=plastic)](https://anaconda.org/bioconda/pygenometracks)\n[![bioconda-badge](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=plastic)](http://bioconda.github.io)\n[![Build Status on Azure](https://dev.azure.com/wolffj/pyGenomeTracks/_apis/build/status/deeptools.pyGenomeTracks?branchName=master)](https://dev.azure.com/wolffj/pyGenomeTracks/_build/latest?definitionId=2\u0026branchName=master)\n![Coverage](./docs/coverage.svg)\n[![European Galaxy server](https://img.shields.io/badge/usegalaxy-.eu-brightgreen?logo=data:image/png;base64,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)](https://usegalaxy.eu/root?tool_id=pygenomeTracks)\n\npyGenomeTracks\n==============\n\nStandalone program and library to plot beautiful genome browser tracks\n----------------------------------------------------------------------\n\npyGenomeTracks aims to produce high-quality genome browser tracks that\nare highly customizable. Currently, it is possible to plot:\n\n * bigwig\n * bed/gtf (many options)\n * bedgraph\n * bedgraph matrices (like TAD-separation scores)\n * epilogos\n * narrow peaks\n * links (represented as arcs, triangles or squares)\n * Hi-C matrices (as triangle or squares)\n * fasta\n * maf (multiple alignment format)\n\nHere is a scheme which describe how pyGenomeTracks is working (graphical abstract of [Lopez-Delisle et al. 2020](https://doi.org/10.1093/bioinformatics/btaa692)):\n\n![pyGenomeTracks](https://github.com/deeptools/pyGenomeTracks/blob/b8918d43b0bd1ca9dd2939fdd0d93a5a297d0453/docs/content/images/graphicalabstract.png)\n\npyGenomeTracks can make plots with or without Hi-C data. The following is an example output of\npyGenomeTracks from [Ramírez et al. 2017](https://www.nature.com/articles/s41467-017-02525-w)\n\n![pyGenomeTracks example](https://github.com/deeptools/pyGenomeTracks/blob/b8918d43b0bd1ca9dd2939fdd0d93a5a297d0453/docs/content/images/hic_example_nat_comm_small.png)\n\nTable of content\n----------------\n\n* [Installation](#installation)\n* [Usage](#usage)\n* [Citation](#citation)\n* [Documentation](#documentation)\n* [External users](#external-users)\n\nInstallation\n------------\n\npyGenomeTracks works with python \u003e=3.8.\n\nThe recommended way to install pyGenomeTracks is via conda\n\n```bash\nconda create -n pygenometracks -c bioconda -c conda-forge pygenometracks\n```\n\nTo get a specific version, one can specify it. For example:\n\n```bash\nconda create -n pygenometracks -c bioconda -c conda-forge pygenometracks=3.5 python=3.7\n```\n\nHowever, we noticed that conda installation can be quite slow so using mamba can help.\nYou first need to create the environment and install mamba:\n\n```bash\nconda create -n pygenometracks -c bioconda -c conda-forge mamba python=3.9\n```\n\nThen activate the environment and install pygenometracks with mamba:\n\n```bash\nconda activate pygenometracks\nmamba install -c conda-forge -c bioconda pygenometracks\n```\n\nor if you want a specific version:\n\n```bash\nconda create -n pygenometracks -c bioconda -c conda-forge mamba python=3.7\nconda activate pygenometracks\nmamba install -c conda-forge -c bioconda pygenometracks=3.5\n```\n\nAlso, pyGenomeTracks can be installed using pip\n\n```bash\npip install pyGenomeTracks\n```\n\nSince version 3.5, pyGenomeTracks uses BEDTools, don't forget to install it or load it into your environment.\n\nUsage\n-----\n\nTo run pyGenomeTracks a configuration file describing the tracks is required. The easiest way to create this file is using the program `make_tracks_file` which creates a configuration file with\ndefaults that can be easily changed. The format is:\n\n```bash\nmake_tracks_file --trackFiles \u003cfile1.bed\u003e \u003cfile2.bw\u003e ... -o tracks.ini\n```\n\n`make_tracks_file` uses the file ending to guess the file type.\n\nThen, a region can be plotted using:\n\n```bash\npyGenomeTracks --tracks tracks.ini --region chr2:10,000,000-11,000,000 --outFileName nice_image.pdf\n```\n\nThe ending `--outFileName` defines the image format. If `.pdf` is used, then the resulting image is a pdf. The options are pdf, png and svg.\n\nDescription of other possible arguments:\n\u003c!--- Start of possible arguments of pgt --\u003e\n``` text\noptions:\n  -h, --help            show this help message and exit\n  --tracks TRACKS       File containing the instructions to plot the tracks.\n                        The tracks.ini file can be genarated using the\n                        `make_tracks_file` program.\n  --region REGION       Region to plot, the format is chr:start-end\n  --BED BED             Instead of a region, a file containing the regions to\n                        plot, in BED format, can be given. If this is the\n                        case, multiple files will be created. It will use the\n                        value of --outFileName as a template and put the\n                        coordinates between the file name and the extension.\n  --width WIDTH         figure width in centimeters (default is 40)\n  --plotWidth PLOTWIDTH\n                        width in centimeters of the plotting (central) part\n  --height HEIGHT       Figure height in centimeters. If not given, the figure\n                        height is computed based on the heights of the tracks.\n                        If given, the track height are proportionally scaled\n                        to match the desired figure height.\n  --title TITLE, -t TITLE\n                        Plot title\n  --outFileName OUTFILENAME, -out OUTFILENAME\n                        File name to save the image, file prefix in case\n                        multiple images are stored\n  --fontSize FONTSIZE   Font size for the labels of the plot (default is 0.3 *\n                        figure width)\n  --dpi DPI             Resolution for the image in case the ouput is a raster\n                        graphics image (e.g png, jpg) (default is 72)\n  --trackLabelFraction TRACKLABELFRACTION\n                        By default the space dedicated to the track labels is\n                        0.05 of the plot width. This fraction can be changed\n                        with this parameter if needed.\n  --trackLabelHAlign {left,right,center}\n                        By default, the horizontal alignment of the track\n                        labels is left. This alignemnt can be changed to right\n                        or center.\n  --decreasingXAxis     By default, the x-axis is increasing. Use this option\n                        if you want to see all tracks with a decreasing\n                        x-axis.\n  --version             show program's version number and exit\n```\n\u003c!--- End of possible arguments of pgt --\u003e\n\nCitation\n--------\n\nIf you use pyGenomeTracks in your analysis, you can cite the following papers:\n\nFidel Ramírez, Vivek Bhardwaj, Laura Arrigoni, Kin Chung Lam, Björn A. Grüning, José Villaveces, Bianca Habermann, Asifa Akhtar \u0026 Thomas Manke. High-resolution TADs reveal DNA sequences underlying genome organization in flies. Nature Communications (2018) [doi:10.1038/s41467-017-02525-w](https://www.nature.com/articles/s41467-017-02525-w)\n\nLopez-Delisle L, Rabbani L, Wolff J, Bhardwaj V, Backofen R, Grüning B, Ramírez F, Manke T. pyGenomeTracks: reproducible plots for multivariate genomic data sets. Bioinformatics. 2020 Aug 3:btaa692. [doi: 10.1093/bioinformatics/btaa692](https://doi.org/10.1093/bioinformatics/btaa692). Epub ahead of print. PMID: 32745185.\n\nDocumentation\n-------------\n\nOur [documentation](http://pygenometracks.readthedocs.io/) provide [examples](http://pygenometracks.readthedocs.org/en/latest/content/examples.html), as well as the [full list of possible parameters](http://pygenometracks.readthedocs.org/en/latest/content/possible-parameters.html) and [guidelines for developers who would like to add a new track type](http://pygenometracks.readthedocs.org/en/latest/content/adding-new-tracks.html).\n\n\u003c!-- I do not know what to do with that, is it External users?\npyGenomeTracks is used by [HiCExporer](https://hicexplorer.readthedocs.io/) and [HiCBrowser](https://github.com/maxplanck-ie/HiCBrowser) (See e.g. [Chorogenome navigator](http://chorogenome.ie-freiburg.mpg.de/) which is made with HiCBrowser)\n --\u003e\nExternal users\n--------------\n\n* [CoolBox](https://github.com/GangCaoLab/CoolBox) is an interactive genomic data explorer for Jupyter Notebooks\n* [Galaxy](https://usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/iuc/pygenometracks/pygenomeTracks) integration offers a graphical user-interface to create PGT plots. It is also possible to include PGT into workflows and automatic pipelines.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeeptools%2FpyGenomeTracks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeeptools%2FpyGenomeTracks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeeptools%2FpyGenomeTracks/lists"}