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Related Experiment Video

Updated: Oct 10, 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

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Computational Assessment of the Expression-modulating Potential for Non-coding Variants.

Fang-Yuan Shi¹, Yu Wang¹, Dong Huang²

¹State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Biomedical Pioneering Innovative Center (BIOPIC) & Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), Peking University, Beijing 100871, China.

Genomics, Proteomics & Bioinformatics

|December 10, 2021

Summary

This summary is machine-generated.

CARMEN is a new algorithm that precisely identifies disease-causing genetic variants affecting gene expression. It outperforms existing tools and aids in discovering novel causal variants from large genetic datasets.

Keywords:

Algorithm Expression-modulating variant Gene regulation Non-coding variant Web server

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Last Updated: Oct 10, 2025

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Published on: April 4, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies link non-coding variants to genetic diseases by altering gene expression.
Identifying the specific causal variants responsible for these effects is a significant challenge in genetic research.

Purpose of the Study:

To develop and validate a novel algorithm, CARMEN, for accurately identifying functional non-coding variants that modulate gene expression.
To demonstrate CARMEN's effectiveness in pinpointing causal variants from large-scale genetic datasets.

Main Methods:

Development of CARMEN, a novel computational algorithm designed to identify functional non-coding expression-modulating variants.
Performance evaluation of CARMEN against existing state-of-the-art tools using benchmark datasets.
Application of CARMEN to integrated GWAS and eQTL datasets to identify novel causal variants.

Main Results:

CARMEN demonstrated superior performance compared to current leading methods in identifying functional non-coding variants.
The algorithm successfully pinpointed several causal variants that were not the primary lead single-nucleotide polymorphisms (SNPs) reported in GWAS.
CARMEN exhibits excellent scalability, efficiently processing massive genomic datasets.

Conclusions:

CARMEN is a powerful and efficient tool for identifying functional non-coding variants linked to genetic diseases.
The algorithm enhances the discovery of causal variants beyond traditional GWAS findings.
CARMEN is accessible as a web server, facilitating its use in genetic research.