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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.
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.
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