The changing landscape of gene expression analysis.
Qiongyi Zhao1, Sophie Shen1, Woo Jun Shim1
1Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, Brisbane QLD 4072, Australia.
Briefings in Bioinformatics
|April 24, 2026
Summary
Gene expression analysis has transformed through four revolutions, shifting from basic transcript surveys to advanced single-cell and spatial transcriptomics. The future focuses on organizing and querying transcriptomic data for predictive and programmable gene expression biology.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- Gene expression analysis has evolved significantly over 25 years, driven by technological advancements.
- Early methods like expressed sequence tags and microarrays have been succeeded by RNA sequencing and single-cell/spatial transcriptomics.
- These advancements enable deeper insights into transcriptional programs, gene regulatory networks, and therapeutic strategies.
Purpose of the Study:
- To synthesize major methodological milestones and bibliometric trends in gene expression analysis.
- To describe four key revolutions that have redefined the field.
- To highlight current challenges and future directions in bioinformatics for gene expression analysis.
Main Methods:
- Bibliometric analysis of leading bioinformatics journals.
- Analysis of 70,783,831 open-access full-text articles to map computational tools.
- Synthesis of methodological evolution and computational tool adoption over time.
Main Results:
- Identification of four major revolutions in gene expression analysis, expanding scale and resolution.
- Demonstration of the coexistence of enduring statistical frameworks and rapidly growing analysis ecosystems.
- Mapping of widely used computational tools onto a timeline, illustrating field progression.
Conclusions:
- The future of gene expression analysis lies in organizing, searching, and reusing transcriptomic and multimodal data at scale.
- Proposes three future directions: consortium-scale knowledgebases, foundation models, and programmable regulatory design.
- The field is transitioning from descriptive measurement to queryable, predictive, and programmable gene expression biology.
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