Alternative preprocessing of RNA-Sequencing data in The XXXX leads to improved analysis results.

@entity5 @entity2 @entity0 @entity1722
MOTIVATION: The @entity5 (TCGA) RNA-Sequencing data are used widely for research. TCGA provides 'Level 3' data, which have been processed using a pipeline specific to that resource. However, we 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, we have reprocessed the data for 9264 @entity5 and 741 normal samples across 24 @entity5 types using the Rsubread package. We have also collated corresponding clinical data for these samples. We provide these data as a community resource. RESULTS: We 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, we used a genomic-signature approach to estimate @entity1722 ( @entity1722 ) activation status for 662 @entity0 samples and found that the Rsubread data resulted in stronger predictions of @entity1722 pathway activity. Finally, we used data from both pipelines to classify 575 @entity2 samples based on histological type. This analysis identified various non-coding RNA that may influence @entity2 histology. AVAILABILITY AND IMPLEMENTATION: 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_piccolo@byu.edu or andreab@genetics.utah.edu Supplementary information: Supplementary material is available at Bioinformatics online.
