TCGAWorkflow

DOI: 10.18129/B9.bioc.TCGAWorkflow    

TCGA Workflow Analyze cancer genomics and epigenomics data using Bioconductor packages

Bioconductor version: Release (3.11)

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>

Installation

To install this package, start R (version "4.0") and enter:

if (!requireNamespace("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")

 

HTML	R Script	'TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages'
Text		NEWS

Details

biocViews	ResourceQueryingWorkflow, Workflow
Version	1.12.0
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), gaia, GenomicRanges, GenomeInfoDb, ggplot2, ggthemes, graphics, minet, MotIV, motifStack, pathview, pbapply, parallel, rGADEM, pander, maftools, RTCGAToolbox, SummarizedExperiment, TCGAbiolinks, TCGAWorkflowData(>= 1.9.0), DT
LinkingTo
Suggests
SystemRequirements
Enhances
URL	https://f1000research.com/articles/5-1542/v2
BugReports	https://github.com/BioinformaticsFMRP/TCGAWorkflow/issues
Depends On Me
Imports Me
Suggests Me
Links To Me

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	TCGAWorkflow_1.12.0.tar.gz
Windows Binary
macOS 10.13 (High Sierra)
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