{"id":27957981,"url":"https://github.com/bioinformaticsfmrp/tcgaworkflow","last_synced_at":"2026-01-23T18:34:36.129Z","repository":{"id":42680665,"uuid":"60634840","full_name":"BioinformaticsFMRP/TCGAWorkflow","owner":"BioinformaticsFMRP","description":"TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages","archived":false,"fork":false,"pushed_at":"2023-06-30T15:07:43.000Z","size":38739,"stargazers_count":49,"open_issues_count":11,"forks_count":21,"subscribers_count":8,"default_branch":"master","last_synced_at":"2025-12-25T22:25:54.959Z","etag":null,"topics":["bioconductor","tcga","tcgabiolinks"],"latest_commit_sha":null,"homepage":"","language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BioinformaticsFMRP.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2016-06-07T17:59:44.000Z","updated_at":"2025-10-07T11:09:38.000Z","dependencies_parsed_at":"2023-10-20T18:26:25.472Z","dependency_job_id":null,"html_url":"https://github.com/BioinformaticsFMRP/TCGAWorkflow","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/BioinformaticsFMRP/TCGAWorkflow","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioinformaticsFMRP%2FTCGAWorkflow","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioinformaticsFMRP%2FTCGAWorkflow/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioinformaticsFMRP%2FTCGAWorkflow/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioinformaticsFMRP%2FTCGAWorkflow/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BioinformaticsFMRP","download_url":"https://codeload.github.com/BioinformaticsFMRP/TCGAWorkflow/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioinformaticsFMRP%2FTCGAWorkflow/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28697429,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-23T17:25:48.045Z","status":"ssl_error","status_checked_at":"2026-01-23T17:25:47.153Z","response_time":59,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["bioconductor","tcga","tcgabiolinks"],"created_at":"2025-05-07T18:15:42.503Z","updated_at":"2026-01-23T18:34:36.102Z","avatar_url":"https://github.com/BioinformaticsFMRP.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Workflow link\n\nLink to complete workflow http://bioinformaticsfmrp.github.io/TCGAWorkflow/\n\n# Abstract\n\nBiotechnological advances in sequencing have led to an explosion of\n    publicly available data via large international consortia such as [The\n    Cancer Genome Atlas (TCGA)](http://cancergenome.nih.gov/), [The\n    Encyclopedia of DNA Elements (ENCODE)](http://www.encodeproject.org/),\n    and [The NIH Roadmap Epigenomics Mapping Consortium\n    (Roadmap)](http://www.roadmapepigenomics.org/). These projects have\n    provided unprecedented opportunities to interrogate the epigenome of\n    cultured cancer cell lines as well as normal and tumor tissues with high\n    genomic resolution. The [Bioconductor](http://www.bioconductor.org/)\n    project offers more than 1,000 open-source software and statistical\n    packages to analyze high-throughput genomic data. However, most packages\n    are designed for specific data types (e.g. expression, epigenetics,\n    genomics) and there is no one comprehensive tool that provides a\n    complete integrative analysis of the resources and data provided by all\n    three public projects. A need to create an integration of these\n    different analyses was recently proposed. In this workflow, we provide a\n    series of biologically focused integrative analyses of different\n    molecular data. We describe how to download, process and prepare TCGA\n    data and by harnessing several key Bioconductor packages, we describe\n    how to extract biologically meaningful genomic and epigenomic data.\n    Using Roadmap and ENCODE data, we provide a work plan to identify\n    biologically relevant functional epigenomic elements associated with\n    cancer. \n    \nTo illustrate our workflow, we analyzed two types of brain\ntumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma\nmultiform or GBM). \n\nAll the  package landing pages used in this workflow can be found through the [biocViews interface](http://www.bioconductor.org/packages/release/BiocViews.html#___Software).\n    \n**Keywords:** Epigenomics, Genomics, Cancer, non-coding, TCGA, ENCODE, Roadmap, Bioinformatics.\n\n# Installation\n\nTo be able to execute all the steps of this workflow please install it with the following code:\n\n```R\nlibrary(BiocManager)\ndeps \u003c- c(\"pathview\",\"clusterProfiler\",\"ELMER\", \"DO.db\",\"GO.db\", \n          \"ComplexHeatmap\",\"EDASeq\", \"TCGAbiolinks\",\"AnnotationHub\",\n          \"gaia\",\"ChIPseeker\",\"minet\",\"BSgenome.Hsapiens.UCSC.hg19\",\n          \"MotifDb\",\"MotIV\", \"rGADEM\", \"motifStack\",\"RTCGAToolbox\")\nfor(pkg in deps)  if (!pkg %in% installed.packages()) install(pkg, dependencies = TRUE)\ndeps \u003c- c(\"devtools\",\"DT\",\"pbapply\",\"readr\",\"circlize\")\nfor(pkg in deps)  if (!pkg %in% installed.packages())  install.packages(pkg,dependencies = TRUE)\ndevtools::install_github(\"BioinformaticsFMRP/TCGAWorkflowData\")\ndevtools::install_github(\"BioinformaticsFMRP/TCGAWorkflow\", dependencies = TRUE)\n```\n\n# Docker image\n\nA complete enviroment with all packages installed is available as Docker image, which can be easily run on Mac OS, Windows and Linux systems. The image can be obtained from Docker Hub: https://hub.docker.com/r/tiagochst/tcgabiolinksgui/\n\nDownload image:\n```{bash, eval = FALSE}\ndocker pull tiagochst/tcgabiolinksgui\n```\n\nTo run R from the command line:\n```{bash, eval = FALSE}\ndocker run -ti tiagochst/tcgabiolinksgui R\n```\n\nTo run RStudio Server (user: rstudio, password: rstudio): \n```{bash, eval = FALSE}\ndocker run -p 8787:8787 tiagochst/tcgabiolinksgui\n```\n\nFor more information please check: https://docs.docker.com/\n\n# Loading packages\n\nAt the beginning of each section, the packages required to execute the code will be loaded. However the following packages are required for all sections.\n\n- TCGAWorkflowData: this package contains the data necessary to execute each of the analysis\nsteps. This is a subset of the downloaded to make the example faster. For a real analysis, please\nuse all the data available.\n- DT: we will use it to visualize the results\n\n```R\nlibrary(TCGAWorkflowData)\nlibrary(DT)\n```\n\n# Building the vignette\n```R\ndevtools::build_vignettes()\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbioinformaticsfmrp%2Ftcgaworkflow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbioinformaticsfmrp%2Ftcgaworkflow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbioinformaticsfmrp%2Ftcgaworkflow/lists"}