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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...

Combinatorial Gene Control

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

Regulation of Expression at Multiple Steps

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...

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Related Experiment Video

Updated: Aug 19, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

SRGS: sparse partial least squares-based recursive gene selection for gene regulatory network inference.

Jinting Guan^1,2, Yang Wang³, Yongjie Wang³

¹Department of Automation, Xiamen University, Xiamen, Fujian, China. jtguan@xmu.edu.cn.

|November 30, 2022

Summary

This summary is machine-generated.

We developed SRGS, a novel method for inferring gene regulatory networks (GRNs) from gene expression data. SRGS is robust to data dropouts and performs competitively with existing GRN inference tools.

Keywords:

Bulk gene expression Gene regulatory network Recursive gene selection Single-cell gene expression Sparse partial least squares

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Gene regulatory network (GRN) identification is crucial for understanding biological processes and diseases.
Single-cell RNA sequencing (scRNA-seq) data necessitates advanced GRN inference methods.
Existing methods often lack accuracy and robustness for scRNA-seq data.

Purpose of the Study:

To develop a robust and accurate method for inferring GRNs from both bulk and single-cell gene expression data.
To address challenges posed by data dropouts in single-cell expression matrices.

Main Methods:

Introduced SRGS (sparse partial least squares)-based recursive gene selection for GRN inference.
Implemented a data augmentation strategy involving sample scrambling and zero-imputation to enhance robustness against dropouts.
Evaluated SRGS on simulated and experimental bulk and single-cell expression data.

Main Results:

SRGS demonstrates competitive performance compared to existing GRN inference methods.
The method effectively infers GRNs from various types of gene expression data, including those with dropouts.
SRGS shows robustness in handling the complexities of single-cell expression data.

Conclusions:

SRGS is an effective tool for gene regulatory network inference from bulk and single-cell expression data.
The method offers a robust solution for analyzing scRNA-seq data with dropouts.
SRGS is publicly available for research use.