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Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology.

Mohamed Helmy^1,2, Kumar Selvarajoo^3,4,5,6

¹Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore. mohamed_helmy@bii.a-star.edu.sg.

Methods in Molecular Biology (Clifton, N.J.)

|October 13, 2022

Summary

Gene expression profiling using RNA sequencing (RNA-Seq) or microarray is crucial for understanding complex gene pathways. This study introduces GeneCloudOmics, a cloud-based tool simplifying transcriptome analysis for biologists.

Keywords:

Bioinformatics Biostatistics RNA-Seq Synthetic biology Transcriptomic data analysis

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Area of Science:

Synthetic biology and metabolic engineering research.
Gene expression profiling and transcriptome analysis.

Background:

Understanding complex pathway gene function and regulation is vital.
High-throughput techniques like RNA sequencing (RNA-Seq) and microarray quantify gene expression.
Analyzing large gene expression datasets requires significant bioinformatics expertise, posing a challenge for biologists.

Purpose of the Study:

To present a protocol for transcriptome analysis.
To address the need for accessible bioinformatics tools for biologists.
To introduce GeneCloudOmics as an end-to-end solution for high-throughput gene expression analysis.

Main Methods:

Utilizing high-throughput gene expression profiling techniques (RNA-Seq, microarray).
Identifying differentially expressed genes (DEGs) by comparing expression levels across conditions.
Employing bioinformatics and data analytic tools for DEG investigation.
Using GeneCloudOmics, a cloud-based web server for transcriptome analysis.

Main Results:

Gene expression profiling quantifies transcriptome-wide expression under various conditions.
Identification of differentially expressed genes (DEGs) is key to inferring functional roles.
GeneCloudOmics provides a user-friendly, end-to-end platform for transcriptome analysis.

Conclusions:

Accessible transcriptome analysis is essential for advancing synthetic biology and metabolic engineering.
GeneCloudOmics overcomes computational barriers for biologists lacking extensive data analysis skills.
This protocol enables researchers to fully leverage high-throughput gene expression data.