Package: TCGAWorkflow
Title: TCGA Workflow Analyze cancer genomics and epigenomics data using
        Bioconductor packages
Version: 0.99.84
Workflow: True
Author: Tiago Chedraoui Silva  <tiagochst@usp.br>,
    Antonio Colaprico <antonio.colaprico@ulb.ac.be>,
    Catharina Olsen <colsen@ulb.ac.be>,
    Fulvio D Angelo <fulvio.dan13@gmail.com>,
    Gianluca Bontempi <gbonte@ulb.ac.be>,
    Michele Ceccarelli <m.ceccarelli@gmail.com>,
    Houtan Noushmehr <houtan@usp.br>
Maintainer: Tiago Chedraoui Silva <tiagochst@usp.br>
Description: 
    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). 
Depends: R (>= 3.4.0)
Imports: AnnotationHub, knitr, ELMER, biomaRt,
        BSgenome.Hsapiens.UCSC.hg19, circlize, c3net, ChIPseeker,
        ComplexHeatmap, clusterProfiler, downloader (>= 0.4), gaia,
        GenomicRanges, GenomeInfoDb, ggplot2, ggthemes, graphics,
        minet, MotIV, motifStack, pathview, pbapply, parallel, rGADEM,
        pander, maftools, RTCGAToolbox, SummarizedExperiment,
        TCGAbiolinks, TCGAWorkflowData, DT
License: Artistic-2.0
VignetteBuilder: knitr
biocViews: WorkflowStep, Sequencing, Epigenetics, FunctionalGenomics
NeedsCompilation: no
URL: https://f1000research.com/articles/5-1542/v2
BugReports: https://github.com/BioinformaticsFMRP/TCGAWorkflow/issues
RoxygenNote: 6.0.1
Packaged: 2017-12-20 15:14:21 UTC; SYSTEM
Built: R 3.4.2; ; 2017-12-20 15:41:47 UTC; windows
