Estimation of splicing metrics for NMD-sensitive transcripts
Alternative splicing is commonly quantified using the Percent-Spliced-In (PSI) metric, which measures the relative abundances of alternatively spliced isoforms. However, some transcript isoforms are targeted by the nonsense-mediated decay (NMD) pathway, introducing a strong bias that leads to underestimation of their true splicing rates. To correct for this bias, we developed an analytical framework and a set of statistical models employing a linear fractional transformation depending on a single parameter capturing the degradation rate of NMD-sensitive transcripts relative to normal mRNA decay. Using Gaussian mixture models, we demonstrated a clear separation of splicing events into two classes, responders and non-responders, with the former exhibiting strong upregulation upon NMD inhibition and the latter showing little or no response. Moreover, non-responders displayed higher coding potential and stronger translation signals both upstream and downstream of the stop codon, which are characteristic of NMD escape through translational readthrough. We further showed that incorporation of event-specific relative decay rates improves the interpretation of differential splicing patterns for NMD-sensitive transcripts. In sum, our results provide a solid framework for unbiased estimation of splicing metrics in NMD-sensitive transcripts from short-read RNA-seq data, without requiring NMD inhibition experiments.