AI Article Synopsis

  • - Contribution of splicing variants to inherited disease diagnostics is reported to be <10%, likely underestimating their impact due to challenges in prediction, need for functional analysis, and detection issues with existing technologies.
  • - This study aimed to evaluate Nanopore sequencing for identifying and quantifying splicing variants in patients with inherited retinal dystrophies, by using 19 selected candidate variants.
  • - The results showed that 13 of the variants caused abnormal splicing events, and Nanopore sequencing provided a reliable way to identify and quantify these low-abundance transcripts, which traditional methods might miss.

Article Abstract

The contribution of splicing variants to molecular diagnostics of inherited diseases is reported to be less than 10%. This figure is likely an underestimation due to several factors including difficulty in predicting the effect of such variants, the need for functional assays, and the inability to detect them (depending on their locations and the sequencing technology used). The aim of this study was to assess the utility of Nanopore sequencing in characterizing and quantifying aberrant splicing events. For this purpose, we selected 19 candidate splicing variants that were identified in patients affected by inherited retinal dystrophies. Several in silico tools were deployed to predict the nature and estimate the magnitude of variant-induced aberrant splicing events. Minigene assay or whole blood-derived cDNA was used to functionally characterize the variants. PCR amplification of minigene-specific cDNA or the target gene in blood cDNA, combined with Nanopore sequencing, was used to identify the resulting transcripts. Thirteen out of nineteen variants caused aberrant splicing events, including cryptic splice site activation, exon skipping, pseudoexon inclusion, or a combination of these. Nanopore sequencing allowed for the identification of full-length transcripts and their precise quantification, which were often in accord with in silico predictions. The method detected reliably low-abundant transcripts, which would not be detected by conventional strategies, such as RT-PCR followed by Sanger sequencing.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11395040PMC
http://dx.doi.org/10.3390/ijms25179569DOI Listing

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