Serious and underappreciated sources of bias mean that extreme caution should be applied when using or interpreting functional enrichment analysis to validate findings from global RNA- or protein-expression analyses.

DNA microarrays and RNAseq are complementary methods for studying RNA molecules. Current computational methods to determine alternative exon usage (AEU) using such data require impractical visual inspection and still yield high false-positive rates. Integrated Gene and Exon Model of Splicing (iGEMS) adapts a gene-level residuals model with a gene size adjusted false discovery rate and exon-level analysis to circumvent these limitations. iGEMS was applied to two new DNA microarray datasets, including the high coverage Human Transcriptome Arrays 2.0 and performance was validated using RT-qPCR. First, AEU was studied in adipocytes treated with (n = 9) or without (n = 8) the anti-diabetes drug, rosiglitazone. iGEMS identified 555 genes with AEU, and robust verification by RT-qPCR (∼90%). Second, in a three-way human tissue comparison (muscle, adipose and blood, n = 41) iGEMS identified 4421 genes with at least one AEU event, with excellent RT-qPCR verification (95%, n = 22). Importantly, iGEMS identified a variety of AEU events, including 3′UTR extension, as well as exon inclusion/exclusion impacting on protein kinase and extracellular matrix domains. In conclusion, iGEMS is a robust method for identification of AEU while the variety of exon usage between human tissues is 5–10 times more prevalent than reported by the Genotype-Tissue Expression consortium using RNA sequencing.

Estrogen receptor alpha (ERα) activity is associated with increased cancer cell proliferation. Studies aiming to understand the impact of ERα on cancer‐associated phenotypes have largely been limited to its transcriptional activity. Herein, we demonstrate that ERα coordinates its transcriptional output with selective modulation of mRNA translation. Importantly, translational perturbations caused by depletion of ERα largely manifest as “translational offsetting” of the transcriptome, whereby amounts of translated mRNAs and corresponding protein levels are maintained constant despite changes in mRNA abundance. Transcripts whose levels, but not polysome association, are reduced following ERα depletion lack features which limit translation efficiency including structured 5′UTRs and miRNA target sites. In contrast, mRNAs induced upon ERα depletion whose polysome association remains unaltered are enriched in codons requiring U34‐modified tRNAs for efficient decoding. Consistently, ERα regulates levels of U34‐modifying enzymes and thereby controls levels of U34‐modified tRNAs. These findings unravel a hitherto unprecedented mechanism of ERα‐dependent orchestration of transcriptional and translational programs that may be a pervasive mechanism of proteome maintenance in hormone‐dependent cancers.