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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

InterTransViewer: a comparative description of differential gene expression profiles from different experiments.

А V Tyapkin¹, V V Lavrekha¹, E V Ubogoeva²

¹Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia.

Vavilovskii Zhurnal Genetiki I Selektsii

|January 19, 2024

Summary

This study introduces InterTransViewer, a tool for selecting transcriptomic experiments for meta-analysis. It uses quantitative indicators to compare gene expression data, improving the accuracy of identifying candidate genes and testing new hypotheses.

Keywords:

Arabidopsis thaliana L.auxin data integration ethylene transcriptome

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Area of Science:

Transcriptomics
Bioinformatics
Computational Biology

Background:

Meta-analysis of transcriptomic data is increasingly common for enhanced accuracy and hypothesis testing.
Optimizing experiment selection is crucial for relevant data integration in transcriptomic meta-analysis.

Purpose of the Study:

To propose quantitative indicators for comparative description of transcriptomic data.
To develop a program, InterTransViewer, for automatic calculation and visualization of these indicators.
To enable efficient selection of experiments for meta-analysis based on data characteristics.

Main Methods:

Development of InterTransViewer for calculating and visualizing quantitative indicators.
Indicators include number of differentially expressed genes (DEGs), unique DEGs, pairwise similarity, and profile homogeneity.
Application to 23 auxin- and 16 ethylene/ACC-induced transcriptomes in Arabidopsis thaliana.

Main Results:

InterTransViewer facilitates ranking, integration/segregation assessment, and hypothesis generation regarding transcriptional responses.
Analysis of DEG profiles and pairwise comparisons aids in identifying homogeneous experiment groups.
Profile homogeneity estimation with resampling and significance thresholds determines suitability for meta-analysis.

Conclusions:

InterTransViewer enables efficient, task-dependent selection of experiments for transcriptomic meta-analysis.
The tool enhances the reliability of meta-analysis by identifying appropriate datasets.
This approach supports robust identification of candidate genes and validation of biological hypotheses.