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 Experiment Videos

Complementing computationally predicted regulatory sites in Tractor_DB using a pattern matching approach.

Marylens Hernández Guía1, Abel González Pérez, Vladimir Espinosa Angarica

  • 1National Bioinformatics Center, Industria y San José, Capitolio Nacional, Habana, Cuba.

In Silico Biology
|June 24, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Fine-tuned GPT-based foundation models effectively reconstruct bacterial transcriptional regulatory networks from literature.

Frontiers in artificial intelligence·2026
Same author

Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli.

Nature communications·2025
Same author

The EcoCyc database (2025).

EcoSal Plus·2025
Same author

Predictive Biophysical Neural Network Modeling of a Compendium of <i>in vivo</i> Transcription Factor DNA Binding Profiles for <i>Escherichia coli</i>.

bioRxiv : the preprint server for biology·2024
Same author

Flexible gold standards for transcription factor regulatory interactions in <i>Escherichia coli</i> K-12: architecture of evidence types.

Frontiers in genetics·2024
Same author

RegulonDB v12.0: a comprehensive resource of transcriptional regulation in E. coli K-12.

Nucleic acids research·2023
Same journal

Regulatory Effects of Cooperativity and Signal Profile on Adaptive Dynamics in Incoherent Feedforward Loop Networks.

In silico biology·2025
Same journal

scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data.

In silico biology·2023
Same journal

Modelling speciation: Problems and implications.

In silico biology·2022
Same journal

Where Do CABs Exist? Verification of a specific region containing concave Actin Bundles (CABs) in a 3-Dimensional confocal image.

In silico biology·2022
Same journal

Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases.

In silico biology·2022
Same journal

Multiscale modeling of tumor response to vascular endothelial growth factor (VEGF) inhibitor.

In silico biology·2022
See all related articles

This study introduces a comparative genomics method to identify new transcriptional regulators and their binding sites in gamma-proteobacteria. This approach enhances the understanding of cellular transcriptional regulatory networks by analyzing closely related genomes.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Prokaryotic genome annotation traditionally focuses on gene location and function, neglecting crucial regulatory information.
  • Limited knowledge of cellular transcriptional regulatory networks hinders a comprehensive understanding of gene expression.
  • The increasing availability of phylogenetically close prokaryotic genomes necessitates advanced computational strategies for regulatory element discovery.

Purpose of the Study:

  • To develop and apply a comparative genomics approach for predicting novel gamma-proteobacterial regulon members.
  • To identify new transcriptional regulators and their corresponding binding sites within closely related gamma-proteobacteria.
  • To assess the conservation of transcriptional regulatory network architecture across gamma-proteobacteria.

Related Experiment Videos

Main Methods:

  • Utilized phylogenetic footprinting strategies by comparing the genomes of Escherichia coli and 16 other closely related gamma-proteobacteria.
  • Employed a pattern-matching strategy for intensive sequence space searching to identify conserved regulatory elements.
  • Integrated computational predictions with existing statistical models in Tractor_DB to expand the repertoire of known regulatory sites.

Main Results:

  • Successfully predicted new members of gamma-proteobacterial regulons, significantly increasing the number of transcriptional regulators with identified binding sites to 86.
  • Complemented existing predictions of regulatory sites stored in Tractor_DB, enhancing the database's utility.
  • Initiated an evaluation of the conservation of transcriptional regulatory network architecture and overall connectivity within gamma-proteobacteria.

Conclusions:

  • The comparative genomics approach effectively identifies novel transcriptional regulators and their regulons in gamma-proteobacteria.
  • This method significantly expands the knowledge of regulatory elements and enhances the Tractor_DB resource.
  • The study provides a foundation for understanding the evolutionary conservation of regulatory network structures in this bacterial group.