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

9.8K
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...
9.8K
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.3K
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...
8.3K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

6.4K
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...
6.4K
Master Transcription Regulators02:23

Master Transcription Regulators

6.9K
Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
6.9K

You might also read

Related Articles

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

Sort by
Same author

A Bayesian-Based Integrative Bioinformatics Analysis Nominates Oncogenic Drivers in Neuroblastoma.

Clinical and translational science·2026
Same author

Targeting the METTL1/m7G axis as a therapeutic strategy in myeloid leukemia.

Blood·2026
Same author

BiGER: Bayesian rank aggregation in genomics with extended ranking schemes.

Nature communications·2026
Same author

SpaFun: discovering domain-specific spatial expression patterns and new disease-relevant genes using functional principal component analysis.

Briefings in bioinformatics·2026
Same author

Claudins interact with LILRB immune inhibitory receptors to promote myeloid immunosuppression in cancer.

Science immunology·2026
Same author

Advances in predicting omics profiles from imaging data.

Briefings in bioinformatics·2026

Related Experiment Video

Updated: Jun 18, 2025

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing ChIP-seq
09:52

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing ChIP-seq

Published on: April 19, 2013

24.4K

Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query

Zeyu Lu1, Xue Xiao2, Qiang Zheng3

  • 1Department of Statistics and Data Science, Moody School of Graduate and Advanced Studies, Southern Methodist University, 3225 Daniel Ave., P.O. Box 750332, Dallas, TX, United States.

Briefings in Bioinformatics
|July 31, 2024
PubMed
Summary

This review evaluates computational methods for predicting transcriptional regulators (TRs) using next-generation sequencing (NGS) data. BART, ChIP-Atlas, and Lisa show promising performance, guiding future TR identification strategies.

Keywords:
benchmarkingnext-generation sequencingpredictionquery gene settranscriptional regulator

More Related Videos

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

10.2K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

683

Related Experiment Videos

Last Updated: Jun 18, 2025

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing ChIP-seq
09:52

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing ChIP-seq

Published on: April 19, 2013

24.4K
High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

10.2K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

683

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcriptional regulators (TRs) are crucial for understanding biological processes and disease.
  • Accurate TR identification is vital for drug discovery and therapeutic target prediction.
  • Numerous computational methods using next-generation sequencing (NGS) data exist, but lack systematic evaluation.

Purpose of the Study:

  • To systematically review and evaluate computational methods for TR prediction using NGS data.
  • To classify existing NGS-based TR prediction methods into library-based and region-based categories.
  • To benchmark method performance based on accuracy, sensitivity, coverage, and usability.

Main Methods:

  • Classification of NGS-based TR prediction methods.
  • Benchmark studies using molecular experimental datasets.
  • Evaluation of accuracy, sensitivity, coverage, and usability.

Main Results:

  • Identified library-based and region-based categories for TR prediction methods.
  • BART, ChIP-Atlas, and Lisa demonstrated relatively superior performance in benchmark studies.
  • Highlighted limitations of current NGS-based TR prediction approaches.

Conclusions:

  • BART, ChIP-Atlas, and Lisa are recommended for TR prediction tasks.
  • Further research is needed to overcome limitations and improve NGS-based TR identification.
  • This review provides a foundation for selecting and developing advanced TR prediction tools.