Description
The NMDetective tracks display genome-wide predictions of nonsense-mediated mRNA
decay (NMD) efficiency 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
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.
Subtracks
| 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. |
Display Conventions and Configuration
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.
- Blue tracks (NMDetective-A and -B): predictions
for all possible PTCs from single-nucleotide nonsense variants.
- Green tracks (NMDetective-A PTC and -B PTC):
predictions for the first out-of-frame PTC from frameshifting indels.
Methods
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).
The models incorporate the following features to predict NMD efficiency:
- Whether the PTC falls in the last exon
- Distance to the last 50 nt of the penultimate exon (the EJC-based “50 bp rule”)
- Distance from the coding start (start-proximal NMD insensitivity)
- Exon length
- mRNA half-life
- Distance to the downstream exon-junction complex
- Distance to the wild-type stop codon
NMDetective-A (random forest regression) captures non-linear interactions among
these features and achieves the highest predictive accuracy.
NMDetective-B (decision tree) applies a simpler rule-based classification that
is more transparent, with a modest reduction in accuracy.
The predictions were generated for every possible PTC-introducing single-nucleotide
variant and for the first out-of-frame PTC from every possible single-nucleotide
frameshifting indel across all human protein-coding transcripts. The original bedGraph
custom track files were downloaded from the
NMDetective Figshare page
resource and converted to bigWig format at UCSC.
Data Access
The data underlying these tracks can be explored interactively with the
Table Browser or the
Data Integrator. For automated analysis,
the data may be queried from our
REST API. Please refer to our
mailing list archives for questions, or our
Data Access FAQ for more
information.
Credits
Thanks to Rik Lindeboom for providing custom tracks and the original NMDetective data
on Figshare.
References
Lindeboom RG, Supek F, Lehner B.
The rules and impact of nonsense-mediated mRNA decay in human cancers.
Nat Genet. 2016 Oct;48(10):1112-8.
PMID: 27618451; PMC: PMC5045715
Lindeboom RGH, Vermeulen M, Lehner B, Supek F.
The impact of nonsense-mediated mRNA decay on genetic disease, gene editing and cancer
immunotherapy.
Nat Genet. 2019 Nov;51(11):1645-1651.
PMID: 31659324; PMC: PMC6858879