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

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.

Scan_Tcga Tools for Integrated Epigenomic and Transcriptomic Analysis of Tumor Subgroups

“…The matrix format text files generated by scan_tcga tools are compatible with other tools for analysis. We believe the scan_tcga tools are complimentary to the existing R bioconductor based tools such as TCGAbiolinks [17], TCGA2STAT [53] or RTCGAToolbox [54]. The future development of scan_tcga will include options for retrieving multiple fields in one operation and additional function for statistical tests.…”

“…In addition, many of these web-based tools are based on curated databases and often it is not possible to access information recently released by TCGA. More recently, a new tool TCGAbiolinks that uses R statistical environment was made available [17], which will be an useful tool to the community. However, using all the different modules of the tools still requires expertise with R programming.…”

MIR7–3HG, a MYC-dependent modulator of cell proliferation, inhibits autophagy by a regulatory loop involving AMBRA1

“…50 In particular, we downloaded the data for 58 and 51 patient samples for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively. At first, we searched for possible outliers using the TCGAnalyse Preprocessing function of TCGAbiolinks, which performs an Array Array Intensity correlation analyses, i.e.…”

TCGAbiolinksGUI: A graphical user interface to analyze GDC cancer molecular and clinical data

“…http://dx.doi.org/10.1101/147496 doi: bioRxiv preprint first posted online Jun. 8, 2017; detailed in our report in which we highlight key advantages of using TCGAbiolinks [2]. Although TCGAbiolinks is a suitable R package for most data analysts with a strong knowledge and familiarity with R specifically those who can comfortably write strings of common R commands, we developed TCGAbiolinksGUI to enable user access to the methodologies offered in TCGAbiolinks and to give users the flexibility of point-and-click style analysis without the need to enter specific arguments.…”

“…To enhance these findings, several important bioinformatics tools to harness genomics cancer data were developed, many of them belonging to the Bioconductor project [1]. Among those tools is our tool TCGAbiolinks [2], which was developed to facilitate the analysis of TCGA data by incorporating the query, download and processing steps within the Bioconductor project [1]. This tool allows users to integrate TCGA data with Bioconductor packages thus harnessing a wealth of statistical methodologies for biologically derived data.…”