TCGAWorkflow
TCGA Workflow Analyze cancer genomics and epigenomics data using Bioconductor packages
Bioconductor version: Release (3.20)
Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM).
Author: Tiago Chedraoui Silva <tiagochst at gmail.com>, Antonio Colaprico <antonio.colaprico at ulb.ac.be>, Catharina Olsen <colsen at ulb.ac.be>, Fulvio D Angelo <fulvio.dan13 at gmail.com>, Gianluca Bontempi <gbonte at ulb.ac.be>, Michele Ceccarelli <m.ceccarelli at gmail.com>, Houtan Noushmehr <houtan at usp.br>
Maintainer: Tiago Chedraoui Silva <tiagochst at gmail.com>
citation("TCGAWorkflow")):
Installation
To install this package, start R (version "4.4") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("TCGAWorkflow")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("TCGAWorkflow")| 'TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages' | HTML | R Script |
| NEWS | Text |
Details
| biocViews | ResourceQueryingWorkflow, Workflow |
| Version | 1.30.0 |
| License | Artistic-2.0 |
| Depends | R (>= 3.4.0) |
| Imports | AnnotationHub, knitr, ELMER, biomaRt, BSgenome.Hsapiens.UCSC.hg19, circlize, c3net, ChIPseeker, ComplexHeatmap, ggpubr, clusterProfiler, downloader (>= 0.4), GenomicRanges, GenomeInfoDb, ggplot2, ggthemes, graphics, minet, motifStack, pathview, pbapply, parallel, rGADEM, pander, maftools, RTCGAToolbox, stringr, SummarizedExperiment, dplyr, plyr, matlab, MultiAssayExperiment, TCGAbiolinks, TCGAWorkflowData(>= 1.25.3), DT, gt |
| System Requirements | |
| URL | https://f1000research.com/articles/5-1542/v2 |
| Bug Reports | https://github.com/BioinformaticsFMRP/TCGAWorkflow/issues |
See More
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Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | TCGAWorkflow_1.30.0.tar.gz |
| Windows Binary (x86_64) | |
| macOS Binary (x86_64) | |
| macOS Binary (arm64) | |
| Source Repository | git clone https://git.bioconductor.org/packages/TCGAWorkflow |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/TCGAWorkflow |
| Package Short Url | https://bioconductor.org/packages/TCGAWorkflow/ |
| Package Downloads Report | Download Stats |