RNA-seq libraries were sequenced to a depth of 5 to 15 million reads per sample. All technical validation libraries were subsampled to 10 million reads to remove potential confounding effects of sequencing depth. Sequencing reads were aligned to the UCSC hg19 transcriptome using STAR (49) and used as input to generate QC statistics with RNA-SeQC (50). RSEM (51) was used to generate an expression matrix for all samples. Both raw count and transcripts per million data were analyzed using edgeR and custom R scripts. Lowly expressed genes with log2 (counts per million) less than 5 were filtered out before analysis. Gene set analyses were performed using the Kolmogorov-Smirnov test implementation in GAGE (52). Cytoscape and the enrichMap (53) module extension were used to visualize pathway-specific differential expression data.

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