Publications by authors named "Francoise Bonnet-Dorion"

Article Synopsis
  • Modeling pre-mRNA splicing is crucial for understanding how nucleotide variations can affect gene expression and lead to diseases, as these variations can disrupt or create important splicing motifs.
  • Existing tools typically specialize in specific splicing motifs, which led to the development of the Splicing Prediction Pipeline (SPiP), a machine learning-based analysis that assesses the impact of variants on various splicing motifs simultaneously.
  • SPiP achieved impressive results with 83.13% sensitivity and 99% specificity in detecting spliceogenic variants, outperforming other existing tools and providing a comprehensive prediction approach for genomic medicine.
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is a clinically actionable gene implicated in breast and ovarian cancer predisposition that has become a high priority target for improving the classification of variants of unknown significance (VUS). Among all VUS, those causing partial/leaky splicing defects are the most challenging to classify because the minimal level of full-length (FL) transcripts required for normal function remains to be established. Here, we explored exon 3 (e3) as a model for calibrating variant-induced spliceogenicity and estimating thresholds for haploinsufficiency.

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Germline nonsense and canonical splice site variants identified in disease-causing genes are generally considered as loss-of-function (LoF) alleles and classified as pathogenic. However, a fraction of such variants could maintain function through their impact on RNA splicing. To test this hypothesis, we used the alternatively spliced exon 12 (E12) as a model system because its in-frame skipping leads to a potentially functional protein.

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Article Synopsis
  • Branch points (BPs) are crucial for the splicing of pre-mRNA and are located in short motifs upstream of acceptor splice sites (3'ss); several bioinformatics tools for detecting BPs have been developed recently.
  • In a study utilizing a large dataset of human 3'ss, Branchpointer was found to be the most accurate tool for identifying BPs, showing 99.48% accuracy for constitutive and 65.84% for alternative 3'ss.
  • Additionally, BPP was the best performer for predicting the impact of variants in BP regions on mRNA splicing, achieving an accuracy of 89.17%.
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Variant interpretation is the key issue in molecular diagnosis. Spliceogenic variants exemplify this issue as each nucleotide variant can be deleterious via disruption or creation of splice site consensus sequences. Consequently, reliable in silico prediction of variant spliceogenicity would be a major improvement.

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