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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...

Cooperative Binding of Transcription Regulators

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome. Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...

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Updated: Sep 21, 2025

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DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from

Lihua Zhang^1,2,3, Jing Zhang⁴, Qing Nie^2,3,5

¹School of Computer Science, Wuhan University, Wuhan 430072, China.

Science Advances

|June 1, 2022

Summary

This summary is machine-generated.

DIRECT-NET accurately identifies genome-wide cis-regulatory elements and their target genes using single-cell multiomics data. This machine learning tool enhances understanding of transcriptional regulation and cell identity.

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Single-cell multiomics data offers insights into cell identity.
Dissecting cis-regulatory element (CRE)-to-gene relationships at single-cell resolution is challenging.

Purpose of the Study:

Introduce DIRECT-NET, a machine learning method for CRE-gene relationship inference.
Evaluate DIRECT-NET's performance against existing methods.
Enable prediction of transcriptional regulation from single-cell data.

Main Methods:

Developed DIRECT-NET, a gradient boosting-based machine learning model.
Utilized parallel single-cell gene expression and chromatin accessibility data, or chromatin accessibility data alone.
Validated DIRECT-NET predictions using independent functional genomics data.

Main Results:

DIRECT-NET accurately identifies genome-wide CREs and their target genes.
Significantly improved accuracy in inferring CRE-to-gene relationships compared to current methods.
Revealed cell subpopulation-specific and dynamic regulatory linkages.

Conclusions:

DIRECT-NET is an efficient tool for CRE-gene relationship prediction.
Facilitates the study of transcriptional regulation using single-cell multiomics.
Advances our understanding of cell identity mechanisms.