TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data

Author(s):Show less -

Colaprico, Antonio ^{[1, 2]} ; Silva, Tiago C. ^{[3, 4]} ; Olsen, Catharina ^{[1, 2]} ; Garofano, Luciano ^{[5, 6]} ; Cava, Claudia ^[7] ; Garolini, Davide ^[8] ; Sabedot, Thais S. ^{[3, 4]} ; Malta, Tathiane M. ^{[3, 4]} ; Pagnotta, Stefano M. ^{[5, 9]} ; Castiglioni, Isabella ; Ceccarelli, Michele ^[10] ; Bontempi, Gianluca ^{[1, 2]} ; Noushmehr, Houtan ^{[3, 4]}

Affiliation

^[1] Interuniv Inst Bioinformat Brussels, Brussels - Belgium

^[2] Univ Libre Bruxelles, Dept Informat, Machine Learning Grp, Brussels - Belgium

^[3] Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Genet, Sao Paulo - Brazil

^[4] NAP USP, Ctr Integrat Syst Biol CISBi, Sao Paulo - Brazil

^[5] Univ Sannio, Dept Sci & Technol, Benevento - Italy

^[6] Unltd Software Srl, Naples - Italy

^[7] Natl Res Council IBFM CNR, Inst Mol Bioimaging & Physiol, Milan - Italy

^[8] Univ Turin, Dept Phys, Phys Complex Syst, I-10124 Turin - Italy

^[9] BIOGEM, Bioinformat Lab, Avellino - Italy

^[10] HBKU, Qatar Comp Res Inst, Doha - Qatar

• Document type: Journal article
• Source: Nucleic Acids Research; v. 44, n. 8 MAY 5 2016.
• Web of Science Citations: 151

Abstract

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries. (AU)

• FAPESP's process: 15/02844-7 - Aberrant epigenomic alterations that are defined by distinct genomic signatures associated with gliomas: methods and development
• Grantee: Tiago Chedraoui Silva
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 14/02245-3 - Identification of epigenomic signatures that define open chromatin regulatory networks associated with mesenchymal differentiation from human pluripotent stem cells
• Grantee: Tathiane Maistro Malta Pereira
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 15/07925-5 - Open source software statistical tools to aid in analyzing and integrating large cancer epigenomic datasets in order to decipher and understand regulatory networks
• Grantee: Houtan Noushmehr
• Support Opportunities: Research Grants - Young Investigators Grants