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Cis-regulatory Sequences

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

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Related Experiment Video

Updated: May 9, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Machine Learning Prediction of Non-Coding Variant Impact in Cell-Class-Specific Human Retinal Cis-Regulatory

Leah S VandenBosch¹, Timothy J Cherry^1,2,3

¹Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA.

Biorxiv : the Preprint Server for Biology

|March 10, 2025

Summary

This summary is machine-generated.

Machine learning models predict how genetic variants affect inherited retinal diseases (IRDs). Using single nucleus epigenomic data, these models accurately identify disease-causing regulatory variants for faster patient diagnosis.

Keywords:

Retinal disease cis-regulatory element machine learning non-coding variants

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Last Updated: May 9, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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Published on: May 22, 2017

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Area of Science:

Genomics
Computational Biology
Ophthalmology

Background:

Non-coding variants in cis-regulatory elements (CREs) are implicated in inherited retinal diseases (IRDs).
Functional characterization of these regulatory variants is a significant challenge in genetic research.
Identifying disease-associated variants requires robust predictive tools.

Purpose of the Study:

To develop machine learning (ML) models for predicting the functional impact of non-coding variants on retinal CREs.
To enhance the identification and prioritization of variants relevant to IRDs.
To leverage single nucleus ATAC-seq data for cell-class-specific variant impact prediction.

Main Methods:

Implemented a gapped k-mer support vector machine (SVM) approach.
Trained 18 distinct ML models using single nucleus ATAC-seq data from human retina cell classes.
Predicted variant impact on 39,437 cell-class-specific regulatory elements.

Main Results:

ML models achieved over 90% accuracy with high cell class specificity.
Variant Impact Prediction (VIP) scores identified specific sequences within CREs, including transcription factor (TF) binding motifs, sensitive to mutation.
VIP scores demonstrated predictive value in massively parallel reporter assays for single nucleotide variants and indels.

Conclusions:

Single nucleus epigenomic data can effectively predict the functional impact of non-coding sequence variants.
The developed ML models and VIP scores enable rapid prioritization of patient variants for functional analysis.
This approach advances the understanding of genetic contributions to IRDs.