TCGA_RNASeq_Clinical – Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA) RNA-Sequencing data are used widely for research. TCGA provides “Le3” data, which have been processed using a pipeline specific to that resource. However, researchers from the University of Utah have found using experimentally derived data that this pipeline produces gene-expression values that vary considerably across biological replicates. In addition, some RNA-Sequencing analysis tools require integer-based read counts, which are not provided with the Level 3 data. As an alternative, the researchers reprocessed the data for 9,264 tumor and 741 normal samples across 24 cancer types using the Rsubread package. They also collated corresponding clinical data for these samples and now provide these data as a community resource.

The researchers compared TCGA samples processed using either pipeline and found that the Rsubread pipeline produced fewer zero-expression genes and more consistent expression levels across replicate samples than the TCGA pipeline. Additionally, they used a genomic-signature approach to estimate HER2 (ERBB2) activation status for 662 breast-tumor samples and found that the Rsubread data resulted in stronger predictions of HER2 pathway activity. Finally, they used data from both pipelines to classify 575 lung cancer samples based on histological type. This analysis identified various non-coding RNA that may influence lung-cancer histology.

rna-seq

Heat maps of normalized expression values for the 200 genes most differentially expressed between HER2-activated human mammary epithelial cells (n = 5) and GFP-treated controls (n = 12). Each column in the heat maps represents data for a given cell line replicate. Each row represents data for a given gene.

Availability – The RNA-Sequencing and clinical data can be downloaded from Gene Expression Omnibus (accession number GSE62944). Scripts and code that were used to process and analyze the data are available from https://github.com/srp33/TCGA_RNASeq_Clinical

Contact – Stephen R Piccolo (stephen_piccolo@byu.edu) or Andrea H. Bild (andreab@genetics.utah.edu)

Rahman, Jackson LK, Johnson WE, Y Li D, Bild AH, Piccolo SR. (2015) Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results. Bioinformatics [Epub ahead of print]. [abstract]

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