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Related Experiment Video

Updated: Jan 17, 2026

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

Transcriptome-wide outlier approach identifies individuals with minor spliceopathies.

Taylor M Arriaga¹, Rodrigo Mendez², Rachel A Ungar³

¹Department of Genetics, Stanford University, Stanford, CA, USA.

American Journal of Human Genetics

|September 20, 2025

Summary

This summary is machine-generated.

This study introduces a transcriptomics-first approach to diagnose rare diseases by analyzing RNA sequencing data for splicing outliers. This method successfully identified rare genetic variants impacting the minor spliceosome, increasing diagnostic yield for undiagnosed patients.

Keywords:

RNA sequencing RNA-seq RNU4ATAC RNU6ATAC minor spliceosome rare disease spliceopathy spliceosome splicing transcriptome wide

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

Published on: September 15, 2021

Area of Science:

Genomics
Molecular Biology
Rare Diseases

Background:

RNA sequencing enhances rare disease diagnosis, but current methods miss trans-acting variants affecting splicing.
Focusing on cis-acting variants overlooks spliceosome function disruptions.
A new transcriptomics-first strategy is needed to capture these overlooked variants.

Purpose of the Study:

To develop and apply a transcriptomics-first method for diagnosing rare diseases by analyzing transcriptome-wide splicing outliers.
To identify causal variants with trans-acting effects on splicing, particularly those impacting the minor spliceosome.
To increase the diagnostic yield for individuals with rare and undiagnosed diseases.

Main Methods:

Utilized FRASER and FRASER2 splicing outlier detection methods on whole blood RNA sequencing data.
Analyzed 385 individuals from the GREGoR and Undiagnosed Diseases Network (UDN) consortia.
Specifically examined for excess intron retention outliers in minor intron-containing genes (MIGs).

Main Results:

Identified five individuals with excess intron retention outliers in MIGs.
All five individuals harbored rare, bi-allelic variants in minor spliceosome small nuclear RNAs (snRNAs).
Discovered compound heterozygous variants in RNU4ATAC (four individuals) and RNU6ATAC (one individual), aiding variant reclassification and suggesting RNU6ATAC as a Mendelian disease gene.

Conclusions:

Analyzing RNA sequencing data for transcriptome-wide splicing signatures increases rare disease diagnostic yield.
This approach provides variant-to-function interpretation for spliceopathies.
Uncovers novel gene-disease associations, particularly for genes involved in spliceosome function.