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Related Experiment Video

Updated: Nov 21, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases.

Noam Auslander^1,2, Daniel M Ramos³, Ivette Zelaya⁴

¹Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Molecular Systems Biology

|January 13, 2021

Summary

This summary is machine-generated.

Identifying genetic modifiers for Mendelian disorders is challenging. GENDULF (GENetic moDULators identiFication) predicts disease modifiers using gene expression data, aiding in understanding disease variability and discovering therapeutic targets.

Keywords:

cystic fibrosis digenic inheritance gene expression modifier gene spinal muscular atrophy

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Last Updated: Nov 21, 2025

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Area of Science:

Genetics
Molecular Biology
Bioinformatics

Background:

Clinical variability in Mendelian disorders is often attributed to modifier genes.
Identifying these modifiers is difficult due to limited large-scale genomic data.

Purpose of the Study:

To introduce GENDULF (GENetic moDULators identiFication), a novel computational method for predicting genetic disease modifiers.
To leverage healthy and diseased tissue gene expression data for modifier prediction in monogenic loss-of-function disorders.

Main Methods:

GENDULF analyzes gene expression data from healthy and diseased tissues.
The method is specifically designed for monogenic diseases with a loss-of-function mechanism.

Main Results:

GENDULF successfully identified known modifiers (EHF, SLC6A14, CLCA1) in cystic fibrosis.
In spinal muscular atrophy (SMA), U2AF1 was predicted as a modifier, with low expression correlating to SMN2 pre-mRNA splicing.
Experimental validation showed U2AF1 knockdown in SMA cells increased full-length SMN2 transcript and SMN protein.

Conclusions:

GENDULF is a valuable tool for predicting genetic disease modifiers, utilizing transcriptomic data.
The method offers insights into disease pathogenesis and identifies potential therapeutic targets for genetic disorders.