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Updated: Jan 10, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Long-read transcriptome analysis using IsoRanker for identifying pathogenic variants in Mendelian conditions.

Yong-Han Hank Cheng¹, Adriana E Sedeño-Cortés², Jane E Ranchalis²

¹Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.

Medrxiv : the Preprint Server for Health Sciences

|November 26, 2025

Summary

IsoRanker, a novel transcriptomics framework, identifies disease-causing non-coding variants by detecting outlier gene and isoform expression. This approach aids in diagnosing rare diseases by providing functional evidence for non-coding variants.

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Last Updated: Jan 10, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

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Published on: January 16, 2019

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Published on: August 15, 2019

Area of Science:

Genomics
Transcriptomics
Rare disease genetics

Background:

Identifying pathogenic non-coding variants for Mendelian conditions is difficult due to unknown functional impacts.
Non-coding variants can alter gene function and contribute to rare diseases, but their detection and interpretation remain challenging.

Purpose of the Study:

To develop and validate IsoRanker, a long-read transcriptome sequencing framework, for prioritizing functionally relevant non-coding variants.
To improve the diagnosis of rare diseases by providing isoform-level functional evidence for non-coding variants.

Main Methods:

Utilized paired cycloheximide-treated and untreated fibroblast transcriptomes from 31 individuals.
Employed long-read transcriptome sequencing linked to phased long-read genomes.
Developed IsoRanker to detect outlier expression, allelic imbalance, and nonsense-mediated decay (NMD) for variant prioritization.

Main Results:

IsoRanker successfully identified known transcript alterations and nominated new diagnostic leads in previously unsolved cases.
The framework demonstrated robustness in subsampling analyses.
In one case, IsoRanker identified biallelic non-coding variants in HARS1, leading to targeted therapy.

Conclusions:

IsoRanker, combined with NMD-aware transcriptomics, provides an effective method for generating isoform-level functional evidence.
This approach enhances the classification of non-coding variants and supports rare disease diagnosis.
IsoRanker facilitates the identification of novel gene isoforms and their functional impact on disease.