What does this research mean for the field?

Routine experimental analysis of pre-mRNA splicing in a diagnostic setting enables the reclassification of over half of suspected splice-altering variants of uncertain significance, demonstrating the necessity of experimental validation over relying solely on in silico prediction tools. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Evaluate the clinical utility of routine patient pre-mRNA splicing analysis for suspected splice-altering variants and benchmark the accuracy of standard in silico prediction algorithms.

September 23, 2025Human Genetics and Genomics AdvancesOpen Access

Routine RNA-based analysis of potential splicing variants facilitates genomic diagnostics and reveals limitations of in silico prediction tools

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Authors

MDMark DrostErasmus MC JDJordy DekkerErasmus MC FFFederico FerraroErasmus MC

Discussion

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Overview

Diagnostic study reveals routine RNA analysis reclassifies 54% of uncertain splicing variants in clinical genetic testing, highlighting the limitations of in silico prediction tools.

Key Points

Evaluate the clinical utility of routine patient pre-mRNA splicing analysis for suspected splice-altering variants and benchmark the accuracy of standard in silico prediction algorithms.
Assessed pre-mRNA splicing for 202 suspected splice-altering variants from clinical genetic testing using patient-cell RT-PCR, agarose gel electrophoresis, Sanger sequencing, and/or exon trapping assays.
Benchmarked the performance of prediction algorithms (SpliceAI, SQUIRLS, SPiP, Pangolin, and Alamut tools) on the 202 clinically validated variants and the CAGI6 splicing dataset.
An effect on pre-mRNA splicing was confirmed in 63% (n = 128/202) of the investigated variants.
Among 177 variants of uncertain significance (VUS), pre-mRNA splicing analysis prompted reclassification of 54% (n = 96/177), including 48% (n = 85/177) upgraded to likely pathogenic or pathogenic.
Prediction algorithms demonstrated variable performance based on variant type and location, failing to reliably detect unexpected effects such as a novel U12-splice site subtype mutation.

Cite This Study

Drost et al. (2025) studied this question.

synapsesocial.com/papers/6aa939e38e04063aed25576a https://doi.org/10.1016/j.xhgg.2025.100521

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September 23, 2025Human Genetics and Genomics AdvancesOpen Access

Routine RNA-based analysis of potential splicing variants facilitates genomic diagnostics and reveals limitations of in silico prediction tools

View Full Paper

Ask AI

Bookmark

Authors

MDMark DrostErasmus MC JDJordy DekkerErasmus MC FFFederico FerraroErasmus MC

Discussion

Member takes

Overview

Diagnostic study reveals routine RNA analysis reclassifies 54% of uncertain splicing variants in clinical genetic testing, highlighting the limitations of in silico prediction tools.

Key Points

Evaluate the clinical utility of routine patient pre-mRNA splicing analysis for suspected splice-altering variants and benchmark the accuracy of standard in silico prediction algorithms.
Assessed pre-mRNA splicing for 202 suspected splice-altering variants from clinical genetic testing using patient-cell RT-PCR, agarose gel electrophoresis, Sanger sequencing, and/or exon trapping assays.
Benchmarked the performance of prediction algorithms (SpliceAI, SQUIRLS, SPiP, Pangolin, and Alamut tools) on the 202 clinically validated variants and the CAGI6 splicing dataset.
An effect on pre-mRNA splicing was confirmed in 63% (n = 128/202) of the investigated variants.
Among 177 variants of uncertain significance (VUS), pre-mRNA splicing analysis prompted reclassification of 54% (n = 96/177), including 48% (n = 85/177) upgraded to likely pathogenic or pathogenic.
Prediction algorithms demonstrated variable performance based on variant type and location, failing to reliably detect unexpected effects such as a novel U12-splice site subtype mutation.

Cite This Study

Drost et al. (2025) studied this question.

synapsesocial.com/papers/6aa939e38e04063aed25576a https://doi.org/10.1016/j.xhgg.2025.100521

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