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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...

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A practical framework for predicting splicing single nucleotide variants in exome sequencing.

Yasuhiro Utsuno¹, Kohei Hamanaka¹, Masamune Sakamoto^1,2

¹Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama 236-0004, Japan.

NAR Genomics and Bioinformatics

|December 22, 2025

Summary

We created a new framework to easily assess pathogenic splicing single nucleotide variants (SNVs) for Mendelian disorders. This tool improves the detection of these variants in exome sequencing data.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Splicing variants are key contributors to Mendelian disorders.
Predicting the pathogenicity of splicing variants remains a significant challenge in genetic diagnostics.

Purpose of the Study:

To develop a simplified framework for evaluating pathogenic splicing single nucleotide variants (SNVs).
To align with the 2023 ACMG/AMP guidelines and ClinGen recommendations for variant classification.

Main Methods:

Developed a scoring system assigning priority scores (-10 to 14) to SNVs in open reading frame regions.
Validated the framework using pathogenic splicing SNVs from the Human Gene Mutation Database and common SNVs from gnomAD.
Compared the framework's discriminatory power against SpliceAI.

Main Results:

The framework demonstrated superior discrimination compared to SpliceAI alone (AUC 0.991 vs. 0.983, P = 2.11 × 10⁻²³).
Identified pathogenic splicing variants in known genes (COL2A1, PDHA1, MECP2, JAKMIP1) in 1257 patients with unresolved diagnoses.
Suggested potential candidate disease-causing genes (UBN1, NFE2L1).

Conclusions:

The developed framework simplifies the pathogenicity assessment of splicing SNVs.
This method enhances the detection of splicing variants through exome sequencing, aiding in diagnosing genetic disorders.