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Regulation of Expression at Multiple Steps

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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 17, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Pathway expression analysis.

Nathan Mankovich¹, Eric Kehoe², Amy Peterson²

¹Colorado State University, Mathematics, Fort Collins, 80523, USA. nathan.mankovich@gmail.com.

Scientific Reports

|December 17, 2022

Summary

This summary is machine-generated.

This study introduces a pathway expression framework for creating novel biomarkers. This method improves the classification of respiratory virus infections by analyzing gene pathways in blood samples.

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Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Published on: October 27, 2017

Area of Science:

Bioinformatics
Molecular Biology
Infectious Disease Research

Background:

Biomarker discovery is crucial for diagnosing and managing viral infections.
Traditional gene expression analysis may not fully capture complex biological processes.
Understanding host responses to respiratory viruses like H1N1, H3N2, HRV, and RSV is vital.

Purpose of the Study:

To introduce a novel pathway expression framework for derived biomarker construction.
To develop a biologically relevant model for distinguishing infection states.
To enhance classification accuracy in respiratory virus transcriptomic studies.

Main Methods:

Developed a pathway expression framework incorporating gene-network connections.
Applied the framework to human blood transcriptomic data from H1N1, H3N2, HRV, and RSV infections.
Selected discriminatory pathways based on pathway expression data.
Benchmarked against standard gene expression classification and pathway ranking methods.

Main Results:

The pathway expression framework successfully distinguished between pre-infection and post-infection states.
Selected pathways showed minimal overlap with those identified by traditional methods.
Utilizing pathway expression data and selected pathways improved classification rates across viral challenges.

Conclusions:

The pathway expression framework offers a robust approach for biomarker discovery.
This method enhances the biological relevance and predictive power of biomarkers.
It provides a more effective strategy for classifying viral respiratory infections compared to conventional techniques.