New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx

Author(s)

Mounir, Mohamed ^[1] ; Lucchetta, Marta ^[1] ; Silva, Tiago C. ^[2] ; Olsen, Catharina ^{[3, 4]} ; Bontempi, Gianluca ^{[3, 4]} ; Chen, Xi ^{[5, 6]} ; Noushmehr, Houtan ^{[2, 7]} ; Colaprico, Antonio ^{[6, 3, 4]} ; Papaleo, Elena ^{[1, 8]}

Affiliation

^[1] Danish Canc Soc Res Ctr, Computat Biol Lab, Copenhagen - Denmark

^[2] Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Genet, Ribeirao Preto - Brazil

^[3] Interuniv Inst Bioinformat Brussels IB 2, Brussels - Belgium

^[4] ULB, MLG, Dept Informat, Brussels - Belgium

^[5] Sylvester Comprehens Canc Ctr, Miami, FL - USA

^[6] Univ Miami, Miller Sch Med, Dept Publ Hlth Sci, Div Biostat, Miami, FL 33136 - USA

^[7] Henry Ford Hosp, Dept Neurosurg, Detroit, MI 48202 - USA

^[8] Univ Copenhagen, Novo Nordisk Fdn, Ctr Prot Res, Translat Dis Syst Biol, Fac Hlth & Med Sci, Copenhagen - Denmark

• Document type: Journal article
• Source: PLOS COMPUTATIONAL BIOLOGY; v. 15, n. 3 MAR 2019.
• Web of Science Citations: 7

Abstract

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7. Author summary The advent of Next-Generation Sequencing (NGS) technologies has been generating a massive amount of data which require continuous efforts in developing and maintain computational tool for data analyses. The Genomic Data Commons (GDC) Data Portal is a platform that contains different cancer genomic studies. Such platforms have often the primary focus on the data storage and they do not provide a comprehensive toolkit for analyses. To fulfil this urgent need, comprehensive but accessible computational protocols that do not renounce a robust statistical framework are thus required. In this context, we here present the new functions of the R/Bioconductor package TCGAbiolinks to improve the discovery of differentially expressed genes in cancer and tumor (sub)types, include the estimate of tumor purity and tumor infiltrations, use normal samples from other platforms and support more broadly other genomics datasets. (AU)

• FAPESP's process: 16/01389-7 - Bioinformatic tool to integrate and understand aberrant epigenomic and genomic changes associated with cancer: methods, development and analysis
• Grantee: Tiago Chedraoui Silva
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• 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