Motivation: The ability of multiple transcripts to arise from a single gene occurs through alternative splicing and alternative promoter usage, collectively called alternative exon usage (AEU). Historically, measuring differences in AEU in response physiological stimuli uses the ‘1-gene-at-a-time’ method [1]. The use of Exon DNA microarrays analysed with iGEMS [2], a novel AEU analysis method, identified a greater number of AEU events between tissues than previously reported. However, analysis of the transcript ratio (TR) for a given gene, in a single tissue or cell, and how this responds to physiological changes is less studied. The most common method used for single-gene expression analysis is RT-qPCR. It is not typically used to measure TR, thus we modified the original quantification method to estimate TR. This was applied to AEU events that we identified, using iGEMS, between muscle and adipose tissue. An explanation for AEU differences in TR was investigated by considering RNA binding proteins (RBP). Methods: To measure TR we designed two sets of primers: 1) measures the transcript that contains the (‘spliced’) exon of interest (Transcript 1) and 2) measures the transcript that does not contain the exon of interest (Transcript 2). The comparative CT calculation is modified: TR = 2-(CT:Transcript 1 – CT:Transcript 2). We measured transcripts using RNA extracted from adipose (n=9) and muscle (n=14) tissue using TRIZOL and then was subsequently reversed transcribed for RT-qPCR [1]. For RBP motif analysis we used analysis of motif enrichment (AME) tool on previously identified AEU events [1] from muscle, adipose and blood. FASTA sequences +/- 300bp of the AEU event were used for motif enrichment. Gene expression of all RBP were extracted from our previous gene-chip analysis [1] and used to calculate fold chang. Results: We measured the TR of 11 AEU events between muscle and adipose tissue e.g. VCL and SRSF5. The TR was significantly different between adipose and muscle tissue (p<0.05) for all genes and noted muscle had TR’s that varied greatly between genes i.e. TTC17 ~ 1:7, DPF2 ~ 1:180 and WDR7~ 1:1000. Using AME, we identified >30 RBP motifs enriched in blood, adipose and muscle which significantly overlapped across each tissue (>90%). As RBP are responsible for driving AEU differences between tissue, we determined expressed RBP in each respective tissue. Expression of RBP was integrated with enriched RBP motif data, resulting in a RBP gene list for each tissue e.g. RBM24 for muscle or SRSF4 for adipose tissue. Conclusion: The present alternative to the comparative CT method may be a more informative approach to exploring transcript expression as it gives a relative perspective on each variant. Direct studies of RBP abundance should be contrasted with TR under a variety of physiological conditions should lead to a better understanding of the importance of gene-splicing.