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Related Experiment Video

Updated: May 23, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

GO for integration of expression data.

Dikla Dotan-Cohen¹, Dana Moonshine, Moshe Natan

¹Department of Computer Science, Ben-Gurion University, Be'er-Sheva, Israel.

In Silico Biology

|April 6, 2012

Summary

This summary is machine-generated.

Aggregating differentially expressed genes across multiple microarray studies, even from different platforms, enhances result reliability. This approach reveals signals missed in single experiments and improves data mining for viral infection research.

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Published on: September 18, 2021

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Low reproducibility in individual gene differential expression analysis from microarray experiments necessitates robust methodologies.
Analyzing gene sets (e.g., Gene Ontology categories, pathways) is proposed to improve result robustness.
This approach assumes experiments randomly sample genes within an active biological process.

Purpose of the Study:

To investigate the feasibility of higher-level analysis by aggregating differentially expressed genes from multiple expression-based studies.
To demonstrate that this aggregation enhances result reliability and uncovers subtle biological signals.
To facilitate more thorough data mining of abundant microarray datasets.

Main Methods:

Meta-analysis of gene expression profiles from ten independent studies.
Focus on human host gene expression changes in response to Retroviridae and Herpesviridae viral infections.
Development of a computational tool for user-guided exploration of aggregated gene data.

Main Results:

Aggregation of differentially expressed genes across diverse microarray platforms increases the reliability of findings.
The meta-analysis approach successfully identified biological signals potentially missed in individual studies.
A functional tool was provided to explore genes and processes implicated in viral infections.

Conclusions:

Aggregating gene expression data from multiple studies, irrespective of platform, is a valid strategy for robust biological inference.
This meta-analytic approach enhances the discovery of biologically relevant genes and pathways.
The developed tool aids researchers in mining complex gene expression datasets for insights into viral pathogenesis.