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Canson, D. M.

Publications and source records attributed to Canson, D. M..

2 recordsLinked to original sources

TP53 minigene analysis of 161 sequence changes provides evidence for role of spatial constraint and regulatory elements on variant-induced splicing impact

Germline TP53 genetic variants that disrupt splicing are implicated in hereditary cancer predisposition, while somatic variants contribute to tumorigenesis. We investigated the role of TP53 splicing regulatory elements (SREs), including G-runs that act as intronic splicing enhancers, using exons 3 and 6 and their downstream introns as models. Minigene microdeletion assays revealed four SRE-rich intervals: c.573_598, c.618_641, c.653_669 and c.672+14_672+36. A diagnostically reported deletion c.655_670del, overlapping an SRE-rich interval, induced an in-frame transcript {Delta}(E6q21) from new donor site usage. Within intron 6, deletion of at least four G-runs led to 100% aberrant transcript expression. Additionally, assay results suggested a donor-to-branchpoint distance cutoff of <50 nt for complete splicing aberration due to spatial constraint, and >75 nt for low risk of splicing abnormality. Overall, splicing data for 134 single nucleotide variants (SNVs) and 27 deletions in TP53 demonstrated that SRE-disrupting SNVs have weak splicing impact (up to 26% exon skipping), while deletions spanning multiple SREs can have profound splicing effects. Results also provide more data to inform splicing impact prediction for intronic deletions that shorten intron size.

genetics↗

SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion

SummarySpliceAI is a widely used splicing prediction tool and its most common application relies on the maximum delta score to assign variant impact on splicing. We developed the SpliceAI-10k calculator (SAI-10k-calc) to extend use of this tool to predict: the splicing aberration type including pseudoexonization, intron retention, partial exon deletion, and (multi)exon skipping using a 10 kb analysis window; the size of inserted or deleted sequence; the effect on reading frame; and the altered amino acid sequence. SAI-10k-calc has 95% sensitivity and 96% specificity for predicting variants that impact splicing, computed from a control dataset of 1,212 single nucleotide variants (SNVs) with curated splicing assay results. Notably, it has high performance ([&ge;]84% accuracy) for predicting pseudoexon and partial intron retention. The automated amino acid sequence prediction allows for efficient identification of variants that are expected to result in mRNA nonsense-mediated decay or translation of truncated proteins. Availability and implementationSAI-10k-calc is implemented in R (https://github.com/adavi4/SAI-10k-calc) and also available as a Microsoft Excel spreadsheet. Users can adjust the default thresholds to suit their target performance values. Supplementary informationSupplementary data are available online.

bioinformatics↗