Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cis-regulatory Sequences02:02

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...
Cis-regulatory Sequences02:02

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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Cooperative Binding of Transcription Regulators02:13

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 dimers that...
Regulation of Expression at Multiple Steps01:23

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 addition of a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Developing a standard definition for sequences of concern.

Frontiers in bioengineering and biotechnology·2026
Same author

SenSet defines cell-type specific senescence signatures in the aged human lung.

The EMBO journal·2026
Same author

MolQuery: Prediction of Lipid Synthesizability Using Active Learning.

ACS omega·2026
Same author

Deep Batch Active Learning for Protein Structure Modeling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same author

Evaluation of statistical differential analysis methods for identification of senescent cells using single-cell transcriptomics.

Cell reports methods·2026
Same author

Single-cell atlas of human lung aging identifies cell type dyssynchrony and increased transcriptional entropy.

Nature communications·2026

Related Experiment Video

Updated: Jul 17, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

Inferring pairwise regulatory relationships from multiple time series datasets.

Yanxin Shi1, Tom Mitchell, Ziv Bar-Joseph

  • 1Machine Learning Department, Language Technologies Institute, Computer Science Department and Department of Biological Sciences, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)
|January 24, 2007
PubMed
Summary

This study introduces a novel computational model to discover gene regulatory relationships from multiple time-series gene expression experiments. The new method significantly reduces false positives in inferring these time-lagged interactions.

More Related Videos

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Related Experiment Videos

Last Updated: Jul 17, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Time series expression experiments are valuable for studying biological systems.
  • Inferring regulatory relationships using time lag analysis is common but prone to false positives in single experiments.
  • Analyzing multiple time series datasets is challenging due to varying biological system dynamics and time scales.

Purpose of the Study:

  • To develop a computational model and algorithm for inferring time-lagged regulatory relationships from multiple time series expression experiments.
  • To address the challenge of varying time scales across different experimental conditions.
  • To improve the accuracy of regulatory relationship discovery compared to existing methods.

Main Methods:

  • A novel computational model and algorithm are proposed.
  • The algorithm computes temporal transformations between datasets using known interacting pairs.
  • It then searches for new interacting pairs based on these transformations.

Main Results:

  • The proposed method achieves a significantly lower false-positive rate than previous approaches.
  • It effectively infers time-lagged regulatory relationships from multiple, time-scale-varying datasets.
  • New predictions can be validated using external data sources and functional annotation databases.

Conclusions:

  • The developed method offers a more accurate approach to discovering gene regulatory networks.
  • It overcomes limitations of analyzing single time series experiments.
  • The algorithm provides a robust tool for biological systems analysis.