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Related Concept Videos

Bacterial Signaling01:30

Bacterial Signaling

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Bacterial signaling can occur within bacteria (intracellular) or between bacteria (intercellular). At times, a group of bacteria behaves like a community. To achieve this, they engage in quorum sensing, the perception of higher cell density that causes changes in gene expression. Quorum sensing involves both extracellular and intracellular signaling. The signaling cascade starts with a molecule called an autoinducer (AI). Individual bacteria produce AIs that move out of the bacterial cell...
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Gene Families01:57

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Related Experiment Video

Updated: Feb 5, 2026

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae
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Functional Transcriptomics for Bacterial Gene Detectives.

Blanca M Perez-Sepulveda1, Jay C D Hinton1

  • 1Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom.

Microbiology Spectrum
|September 15, 2018
PubMed
Summary

Analyzing bacterial gene expression data is challenging due to large datasets. This study highlights accessible online tools for visualizing transcriptomic data, aiding hypothesis generation and understanding bacterial transcriptional networks.

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Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Transcriptomic technologies provide whole-genome expression profiles for bacterial model organisms, accelerating gene function assignment.
  • The increasing volume of transcriptomic data presents significant analysis challenges for biologists.
  • Limited availability of online resources for global bacterial gene expression analysis hinders data exploration and access.

Purpose of the Study:

  • To demonstrate the value of pre-existing transcriptomic data for hypothesis generation in bacterial research.
  • To showcase accessible online resources for visualizing and analyzing bacterial gene expression profiles.
  • To facilitate a deeper understanding of transcriptional networks in model organisms like Salmonella.

Main Methods:

  • Utilized accessible online platforms, specifically SalComMac and SalComRegulon, for data visualization and analysis.
  • Analyzed expression profiles of coding genes and small RNAs.
  • Employed well-characterized small RNAs responding to various stress conditions (envelope, oxidative, osmotic, iron limitation) as case studies.

Main Results:

  • Demonstrated the effectiveness of pre-existing transcriptomic data for generating novel hypotheses.
  • Successfully visualized and analyzed gene and small RNA expression profiles using online resources.
  • Illustrated the integration of different online platforms for intuitive data exploration.

Conclusions:

  • Accessible online resources can empower researchers to intuitively analyze transcriptomic data and generate hypotheses.
  • The approach facilitates the study of transcriptional networks in bacteria, exemplified by Salmonella.
  • Encourages the development of further online tools to enhance scientific community's engagement with transcriptomic data.