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

By Antonio Colaprico, Tiago C. Silva, Catharina Olsen, L. Garofano, Claudia Cava, Davide Garolini, Thais S. Sabedot, T. Malta, S. Pagnotta, Isabella Castiglioni and 3 more. Nucleic Acids Research, 2015

3,480 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/439ede62248e5f6202982afead02b33d3feffae7

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In Cancer Research

In Molecular Biology

In Pulmonary and Respiratory Medicine

Arul M. Chinnaiyan

Citations in the corpus

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, methodology): “Kaplan-Meier overall survival curves were generated using the function TCGAanalyze_survival from the package TCGAbiolinks (Colaprico et al., 2016).”
Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation (2018, methodology): “html) (Colaprico et al., 2016; Silva et al., 2017), through the function “PanCancerAtlas_subtypes()”, which provides full access to the curated matrix used for this study), and clinical features (more than 10,000 features).”
Human Primary Liver Cancer -derived Organoid Cultures for disease modelling and drug screening (2017, neutral): “10) [79] and by splitting, per gene, the tumour samples into high- and low-expression 1284 groups.”
Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures (2019, neutral): “Updated clinical data and sample information for TCGA-STAD samples were obtained from the Genomic Data Commons (https://portal.gdc.cancer.gov/) using the R package TCGAbiolinks (25).”