The NMDetective tracks display genome-wide predictions of nonsense-mediated mRNA -decay (NMD) efficiency from +decay (NMD) efficiency using the NMDetective-A and NMDetective-B models from +Lindeboom et al. 2019, +trained on the NMD efficiency measure from Lindeboom et al. 2016. NMDetective scores predict whether a premature termination codon (PTC) at a given position will trigger NMD and mRNA degradation, or whether the transcript will escape NMD and potentially produce a truncated protein.
-Scores range from approximately −1 to +1. Positive values indicate that a PTC at -that position is predicted to trigger NMD (the mRNA is degraded). Negative values indicate +Scores range from 0 to 1. Values near 1 indicate that a PTC at +that position is predicted to trigger NMD (the mRNA is degraded). Values near 0 indicate that the PTC is predicted to escape NMD (the truncated mRNA may be translated into an -aberrant protein). Values near zero indicate intermediate or uncertain NMD efficiency. +aberrant protein). Values in between indicate intermediate NMD efficiency.
| Track | Description |
|---|---|
| NMDetective-A | Random forest model predicting NMD efficiency for all possible PTCs introduced by single-nucleotide variants. Explains ~71% of systematic variance in NMD efficiency. |
| NMDetective-B | Simplified decision tree model for all possible PTCs. Slightly lower accuracy (~68% variance explained) but more interpretable, making it suitable for clinical applications. |
| NMDetective-A PTC | Random forest model predicting NMD efficiency specifically for the first out-of-frame PTC introduced by frameshifting indel mutations. |
| NMDetective-B PTC | Decision tree model for the first out-of-frame PTC from frameshifting indels. |
-Each subtrack is displayed as a signal (bigWig) track. By default, the vertical axis -ranges from −1 to +1. Regions with positive values (predicted NMD-triggering) are -shown above the baseline; regions with negative values (predicted NMD escape) are shown -below. +Each subtrack is displayed as a signal (bigWig) track. Values closer to 1 indicate +predicted NMD-triggering, while values closer to 0 indicate predicted NMD escape.
-The NMDetective models were trained on somatic nonsense mutation data from 9,769 cancer -patients and validated with frameshift mutations and germline variants -(Lindeboom et al. 2019). +NMDetective-A and NMDetective-B were introduced in +Lindeboom et al. 2019, +trained on NMD efficiency scores derived from somatic nonsense mutation data from 9,769 cancer +patients +(Lindeboom et al. 2016), +and tested on an independent set of frameshift mutations. The models incorporate the following features to predict NMD efficiency:
NMDetective-A (random forest regression) captures non-linear interactions among these features and achieves the highest predictive accuracy.