IGEMS: an integrated model for identification of alternative exon usage events.

Sanjana Sood,Asif Nakhuda,Jaakko Kaprio,Ola Larsson,Carl Murie,Robert J Brogan,Heikki Kainulainen,Krzysztof J Szkop,Philip J Atherton,Thomas Gustafsson,Urho M Kujala,Iain J Gallagher,James A Timmons

doi:10.1093/nar/gkw263

Abstract

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.

Highlights

RNA splicing occurs in all-eukaryotic organisms and requires a sophisticated machinery to correctly define exon boundaries for the removal of introns [1]
For development of Integrated Gene and Exon Model of Splicing (iGEMS) we generated a dataset contrasting cells with or without ROSI treatment, i.e. where more subtle changes in alternative exon usage (AEU) are expected as compared to comparisons between diverse tis
As expected the relationship of false discovery rate (FDR) to Material Unaccounted For’ (MUF) scores did not follow a simple linear trend because, depending on the number of probes for a gene, a specific MUF score will be associated with different FDRs (Figure 2A)

Summary

Introduction

RNA splicing occurs in all-eukaryotic organisms and requires a sophisticated machinery to correctly define exon boundaries for the removal of introns [1]. Splicing can give rise to diverse transcripts with alternative patterns of exon inclusion/intron retention altering protein function or posttranscriptional regulation [2]. AEU that lead to pathology, or altered physiological regulation can be studied using various global transcriptomics techniques. These include RNA sequencing (RNA-seq) [10] and DNA microarrays such as Affymetrix Exon ST arrays [11] and Human Transcriptome Arrays 2.0 (HTA 2.0). The Sequencing Quality Control Consortium (SEQC) recently concluded that deep RNAseq in combination with high-resolution DNA microarrays may provide the most efficient approach for studying the transcriptome [12], recognizing the complementary nature of the two technologies. The strength of sequencing technology is that it aspires to capture the entire diversity of the transcriptome

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