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Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

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TimesVector-Web: A Web Service for Analysing Time Course Transcriptome Data with Multiple Conditions.

Jaeyeon Jang¹, Inseung Hwang¹, Inuk Jung¹

¹Department of Computer Science and Engineering, Kyungpook National University, Buk-gu, Deagu 41566, Korea.

|January 21, 2022

Summary

TimesVector-web simplifies time course gene expression analysis for multiple conditions. This web service aids in identifying differential gene expression patterns and offers downstream biological interpretation, making complex transcriptomic data more accessible.

Keywords:

clustering gene expression pattern time course web service

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Time course gene expression data reveals dynamic transcriptomic responses to conditions.
Traditional differential gene expression (DEG) analysis struggles with time series data and multiple conditions.
Existing tools are often local software, requiring significant technical expertise.

Purpose of the Study:

To develop an accessible web service, TimesVector-web, for analyzing time course gene expression data across multiple conditions.
To overcome limitations of traditional DEG methods in handling time series and multi-class comparisons.
To provide integrated downstream analyses for biological interpretation.

Main Methods:

Development of TimesVector-web, a user-friendly web service.
Implementation of methods for analyzing multi-class time course gene expression data.
Integration of downstream analysis modules: transcription factor (TF) and microRNA (miRNA) target analysis, gene ontology (GO) enrichment, and pathway analysis.

Main Results:

TimesVector-web successfully analyzes time course gene expression data from multiple conditions.
The service provides valuable downstream biological insights, including TF, miRNA, GO, and pathway associations.
Validation using microarray and RNA-seq data demonstrated the capture of significant biological findings.

Conclusions:

TimesVector-web effectively addresses the challenges of analyzing complex time course gene expression data.
The web service democratizes access to advanced transcriptomic analysis and biological interpretation.
This tool facilitates a deeper understanding of dynamic biological processes across different experimental conditions.