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

• Published in: PLoS computational biology 2019-03, Vol.15 (3), p.e1006701-e1006701
• Main Authors: Mounir, Mohamed, Lucchetta, Marta, Silva, Tiago C, Olsen, Catharina, Bontempi, Gianluca, Chen, Xi, Noushmehr, Houtan, Colaprico, Antonio, Papaleo, Elena
• Format: Article
• Language: English
• Subjects: Accessibility; Accounting; Artificial intelligence; Bioinformatics; Biology; Biology and Life Sciences; Breast cancer; Cancer; Cancer genetics; Cancer research; Carcinogenesis; Computational biology; Computer applications; Data storage; Datasets; Datasets as Topic; Endometrial cancer; Filtration; Funding; Gene expression; Gene sequencing; Genes; Genome, Human; Genomes; Genomics; Genotypes; Head & neck cancer; High-Throughput Nucleotide Sequencing; Humans; Information storage and retrieval; Integration; Laboratories; Liver cancer; Machine learning; Medical research; Medicine and Health Sciences; Methods; Neoplasms - genetics; Next-generation sequencing; Patients; Platforms; Public health; Purity; Software; Squamous cell carcinoma; Statistical methods; Technology; Tumorigenesis; Tumors
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006701

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.