*Result*: SRscore: An R Package for Quantifying Gene Stress Responsiveness Across Multiple Transcriptome Datasets Using Meta-Analysis.
Title:
SRscore: An R Package for Quantifying Gene Stress Responsiveness Across Multiple Transcriptome Datasets Using Meta-Analysis.
Authors:
Fukuda Y; Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto, Kyoto, Japan., Fukushima A; Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto, Kyoto, Japan.; RIKEN Information R&D and Strategy Headquarters, Wako, Saitama, Japan.
Source:
Genes to cells : devoted to molecular & cellular mechanisms [Genes Cells] 2026 Mar; Vol. 31 (2), pp. e70096.
Publication Type:
Journal Article; Meta-Analysis
Language:
English
Journal Info:
Publisher: Blackwell Science Ltd Country of Publication: England NLM ID: 9607379 Publication Model: Print Cited Medium: Internet ISSN: 1365-2443 (Electronic) Linking ISSN: 13569597 NLM ISO Abbreviation: Genes Cells Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford, UK : Blackwell Science Ltd., 1996-
MeSH Terms:
References:
Clough, E., T. Barrett, S. E. Wilhite, et al. 2024. “NCBI GEO: Archive for Gene Expression and Epigenomics Data Sets: 23‐Year Update.” Nucleic Acids Research 52: D138–D144.
De Toma, I., C. Sierra, and M. Dierssen. 2021. “Meta‐Analysis of Transcriptomic Data Reveals Clusters of Consistently Deregulated Gene and Disease Ontologies in Down Syndrome.” PLoS Computational Biology 17: e1009317.
Fan, M., M. Y. Bai, J. G. Kim, et al. 2014. “The bHLH Transcription Factor HBI1 Mediates the Trade‐Off Between Growth and Pathogen‐Associated Molecular Pattern‐Triggered Immunity in Arabidopsis.” Plant Cell 26: 828–841.
Fischer, M., P. Grossmann, M. Padi, and J. A. DeCaprio. 2016. “Integration of TP53, DREAM, MMB‐FOXM1 and RB‐E2F Target Gene Analyses Identifies Cell Cycle Gene Regulatory Networks.” Nucleic Acids Research 44: 6070–6086.
Fischer, M., and S. Hoffmann. 2022. “Synthesizing Genome Regulation Data With Vote‐Counting.” Trends in Genetics 38: 1208–1216.
Fukuda, Y., K. Kawaguchi, and A. Fukushima. 2025. “AtSRGA: A Shiny Application for Retrieving and Visualizing Stress‐Responsive Genes in Arabidopsis Thaliana.” Plant Physiology 197: kiaf105.
Gao, S., W. Guo, W. Feng, et al. 2016. “LTP3 Contributes to Disease Susceptibility in Arabidopsis by Enhancing Abscisic Acid (ABA) Biosynthesis.” Molecular Plant Pathology 17: 412–426.
Gautier, L., L. Cope, B. M. Bolstad, and R. A. Irizarry. 2004. “Affy—Analysis of Affymetrix GeneChip Data at the Probe Level.” Bioinformatics 20: 307–315.
Gonzalez‐Guzman, M., G. A. Pizzio, R. Antoni, et al. 2012. “Arabidopsis PYR/PYL/RCAR Receptors Play a Major Role in Quantitative Regulation of Stomatal Aperture and Transcriptional Response to Abscisic Acid.” Plant Cell 24: 2483–2496.
Gu, Z. 2022. “Complex Heatmap Visualization.” iMeta 1: e43.
Hundertmark, M., and D. K. Hincha. 2008. “LEA (Late Embryogenesis Abundant) Proteins and Their Encoding Genes in Arabidopsis Thaliana.” BMC Genomics 9: 118.
Ikeda, S., Z. Zou, H. Bono, et al. 2025. “Extraction of Biological Terms Using Large Language Models Enhances the Usability of Metadata in the BioSample Database.” GigaScience 14: giaf070.
Irizarry, R. A., B. Hobbs, F. Collin, et al. 2003. “Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data.” Biostatistics 4: 249–264.
Katz, K., O. Shutov, R. Lapoint, M. Kimelman, J. R. Brister, and C. O'Sullivan. 2022. “The Sequence Read Archive: A Decade More of Explosive Growth.” Nucleic Acids Research 50: D387–D390.
Kuhn, J. M., A. Boisson‐Dernier, M. B. Dizon, M. H. Maktabi, and J. I. Schroeder. 2006. “The Protein Phosphatase AtPP2CA Negatively Regulates Abscisic Acid Signal Transduction in Arabidopsis, and Effects of abh1 on AtPP2CA mRNA.” Plant Physiology 140: 127–139.
Liu, Y. C., Y. R. Wu, X. H. Huang, J. Sun, and Q. Xie. 2011. “AtPUB19, a U‐Box E3 Ubiquitin Ligase, Negatively Regulates Abscisic Acid and Drought Responses in Arabidopsis thaliana.” Molecular Plant 4: 938–946.
Love, M. I., C. Soneson, and R. Patro. 2018. “Swimming Downstream: Statistical Analysis of Differential Transcript Usage Following Salmon Quantification.” F1000Research 7: 952.
Lozano‐Duran, R., and C. Zipfel. 2015. “Trade‐Off Between Growth and Immunity: Role of Brassinosteroids.” Trends in Plant Science 20: 12–19.
Mahmood, K., A. El‐Kereamy, S. H. Kim, E. Nambara, and S. J. Rothstein. 2016. “ANAC032 Positively Regulates Age‐Dependent and Stress‐Induced Senescence in Arabidopsis thaliana.” Plant & Cell Physiology 57: 2029–2046.
Marin‐de la Rosa, N., C. W. Lin, Y. J. Kang, et al. 2019. “Drought Resistance Is Mediated by Divergent Strategies in Closely Related Brassicaceae.” New Phytologist 223: 783–797.
Ng, L. M., K. Melcher, B. T. Teh, and H. E. Xu. 2014. “Abscisic Acid Perception and Signaling: Structural Mechanisms and Applications.” Acta Pharmacologica Sinica 35: 567–584.
Nozu, R., M. Kadota, M. Nakamura, S. Kuraku, and H. Bono. 2024. “Meta‐Analysis of Gonadal Transcriptome Provides Novel Insights Into Sex Change Mechanism Across Protogynous Fishes.” Genes to Cells 29: 1052–1068.
Ono, Y., and H. Bono. 2021. “Multi‐Omic Meta‐Analysis of Transcriptomes and the Bibliome Uncovers Novel Hypoxia‐Inducible Genes.” Biomedicine 9: 582.
Pavlidis, P., and W. S. Noble. 2001. “Analysis of Strain and Regional Variation in Gene Expression in Mouse Brain.” Genome Biology 2: RESEARCH0042.
Ritchie, M. E., B. Phipson, D. Wu, et al. 2015. “Limma Powers Differential Expression Analyses for RNA‐Sequencing and Microarray Studies.” Nucleic Acids Research 43: e47.
Rubio, S., A. Rodrigues, A. Saez, et al. 2009. “Triple Loss of Function of Protein Phosphatases Type 2C Leads to Partial Constitutive Response to Endogenous Abscisic Acid.” Plant Physiology 150: 1345–1355.
Sarkans, U., A. Fullgrabe, A. Ali, et al. 2021. “From ArrayExpress to BioStudies.” Nucleic Acids Research 49: D1502–D1506.
Shintani, M., K. Tamura, and H. Bono. 2024. “Meta‐Analysis of Public RNA Sequencing Data of Abscisic Acid‐Related Abiotic Stresses in Arabidopsis thaliana.” Frontiers in Plant Science 15: 1343787.
Su, M., G. Huang, Q. Zhang, et al. 2016. “The LEA Protein, ABR, Is Regulated by ABI5 and Involved in Dark‐Induced Leaf Senescence in Arabidopsis thaliana.” Plant Science 247: 93–103.
Tamura, K., and H. Bono. 2022. “Meta‐Analysis of RNA Sequencing Data of Arabidopsis and Rice Under Hypoxia.” Life 12: 1079.
Tran, L. S., K. Nakashima, Y. Sakuma, et al. 2004. “Isolation and Functional Analysis of Arabidopsis Stress‐Inducible NAC Transcription Factors That Bind to a Drought‐Responsive Cis‐Element in the Early Responsive to Dehydration Stress 1 Promoter.” Plant Cell 16: 2481–2498.
Tseng, G. C., D. Ghosh, and E. Feingold. 2012. “Comprehensive Literature Review and Statistical Considerations for Microarray Meta‐Analysis.” Nucleic Acids Research 40: 3785–3799.
Yonezawa, S., and H. Bono. 2023. “Meta‐Analysis of Heat‐Stressed Transcriptomes Using the Public Gene Expression Database From Human and Mouse Samples.” International Journal of Molecular Sciences 24: 13444. https://doi.org/10.3390/ijms241713444.
Yu, G. 2024. “Thirteen Years of clusterProfiler.” Innovation 5: 100722.
Yumiya, M., and H. Bono. 2025. “Meta‐Analysis of Wild Relatives and Domesticated Species of Rice, Tomato, and Soybean Using Publicly Available Transcriptome Data.” Life 15: 1088. https://doi.org/10.3390/life15071088.
Zhang, X. L., L. Jiang, Q. Xin, Y. Liu, J. X. Tan, and Z. Z. Chen. 2015. “Structural Basis and Functions of Abscisic Acid Receptors PYLs.” Frontiers in Plant Science 6: 88.
De Toma, I., C. Sierra, and M. Dierssen. 2021. “Meta‐Analysis of Transcriptomic Data Reveals Clusters of Consistently Deregulated Gene and Disease Ontologies in Down Syndrome.” PLoS Computational Biology 17: e1009317.
Fan, M., M. Y. Bai, J. G. Kim, et al. 2014. “The bHLH Transcription Factor HBI1 Mediates the Trade‐Off Between Growth and Pathogen‐Associated Molecular Pattern‐Triggered Immunity in Arabidopsis.” Plant Cell 26: 828–841.
Fischer, M., P. Grossmann, M. Padi, and J. A. DeCaprio. 2016. “Integration of TP53, DREAM, MMB‐FOXM1 and RB‐E2F Target Gene Analyses Identifies Cell Cycle Gene Regulatory Networks.” Nucleic Acids Research 44: 6070–6086.
Fischer, M., and S. Hoffmann. 2022. “Synthesizing Genome Regulation Data With Vote‐Counting.” Trends in Genetics 38: 1208–1216.
Fukuda, Y., K. Kawaguchi, and A. Fukushima. 2025. “AtSRGA: A Shiny Application for Retrieving and Visualizing Stress‐Responsive Genes in Arabidopsis Thaliana.” Plant Physiology 197: kiaf105.
Gao, S., W. Guo, W. Feng, et al. 2016. “LTP3 Contributes to Disease Susceptibility in Arabidopsis by Enhancing Abscisic Acid (ABA) Biosynthesis.” Molecular Plant Pathology 17: 412–426.
Gautier, L., L. Cope, B. M. Bolstad, and R. A. Irizarry. 2004. “Affy—Analysis of Affymetrix GeneChip Data at the Probe Level.” Bioinformatics 20: 307–315.
Gonzalez‐Guzman, M., G. A. Pizzio, R. Antoni, et al. 2012. “Arabidopsis PYR/PYL/RCAR Receptors Play a Major Role in Quantitative Regulation of Stomatal Aperture and Transcriptional Response to Abscisic Acid.” Plant Cell 24: 2483–2496.
Gu, Z. 2022. “Complex Heatmap Visualization.” iMeta 1: e43.
Hundertmark, M., and D. K. Hincha. 2008. “LEA (Late Embryogenesis Abundant) Proteins and Their Encoding Genes in Arabidopsis Thaliana.” BMC Genomics 9: 118.
Ikeda, S., Z. Zou, H. Bono, et al. 2025. “Extraction of Biological Terms Using Large Language Models Enhances the Usability of Metadata in the BioSample Database.” GigaScience 14: giaf070.
Irizarry, R. A., B. Hobbs, F. Collin, et al. 2003. “Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data.” Biostatistics 4: 249–264.
Katz, K., O. Shutov, R. Lapoint, M. Kimelman, J. R. Brister, and C. O'Sullivan. 2022. “The Sequence Read Archive: A Decade More of Explosive Growth.” Nucleic Acids Research 50: D387–D390.
Kuhn, J. M., A. Boisson‐Dernier, M. B. Dizon, M. H. Maktabi, and J. I. Schroeder. 2006. “The Protein Phosphatase AtPP2CA Negatively Regulates Abscisic Acid Signal Transduction in Arabidopsis, and Effects of abh1 on AtPP2CA mRNA.” Plant Physiology 140: 127–139.
Liu, Y. C., Y. R. Wu, X. H. Huang, J. Sun, and Q. Xie. 2011. “AtPUB19, a U‐Box E3 Ubiquitin Ligase, Negatively Regulates Abscisic Acid and Drought Responses in Arabidopsis thaliana.” Molecular Plant 4: 938–946.
Love, M. I., C. Soneson, and R. Patro. 2018. “Swimming Downstream: Statistical Analysis of Differential Transcript Usage Following Salmon Quantification.” F1000Research 7: 952.
Lozano‐Duran, R., and C. Zipfel. 2015. “Trade‐Off Between Growth and Immunity: Role of Brassinosteroids.” Trends in Plant Science 20: 12–19.
Mahmood, K., A. El‐Kereamy, S. H. Kim, E. Nambara, and S. J. Rothstein. 2016. “ANAC032 Positively Regulates Age‐Dependent and Stress‐Induced Senescence in Arabidopsis thaliana.” Plant & Cell Physiology 57: 2029–2046.
Marin‐de la Rosa, N., C. W. Lin, Y. J. Kang, et al. 2019. “Drought Resistance Is Mediated by Divergent Strategies in Closely Related Brassicaceae.” New Phytologist 223: 783–797.
Ng, L. M., K. Melcher, B. T. Teh, and H. E. Xu. 2014. “Abscisic Acid Perception and Signaling: Structural Mechanisms and Applications.” Acta Pharmacologica Sinica 35: 567–584.
Nozu, R., M. Kadota, M. Nakamura, S. Kuraku, and H. Bono. 2024. “Meta‐Analysis of Gonadal Transcriptome Provides Novel Insights Into Sex Change Mechanism Across Protogynous Fishes.” Genes to Cells 29: 1052–1068.
Ono, Y., and H. Bono. 2021. “Multi‐Omic Meta‐Analysis of Transcriptomes and the Bibliome Uncovers Novel Hypoxia‐Inducible Genes.” Biomedicine 9: 582.
Pavlidis, P., and W. S. Noble. 2001. “Analysis of Strain and Regional Variation in Gene Expression in Mouse Brain.” Genome Biology 2: RESEARCH0042.
Ritchie, M. E., B. Phipson, D. Wu, et al. 2015. “Limma Powers Differential Expression Analyses for RNA‐Sequencing and Microarray Studies.” Nucleic Acids Research 43: e47.
Rubio, S., A. Rodrigues, A. Saez, et al. 2009. “Triple Loss of Function of Protein Phosphatases Type 2C Leads to Partial Constitutive Response to Endogenous Abscisic Acid.” Plant Physiology 150: 1345–1355.
Sarkans, U., A. Fullgrabe, A. Ali, et al. 2021. “From ArrayExpress to BioStudies.” Nucleic Acids Research 49: D1502–D1506.
Shintani, M., K. Tamura, and H. Bono. 2024. “Meta‐Analysis of Public RNA Sequencing Data of Abscisic Acid‐Related Abiotic Stresses in Arabidopsis thaliana.” Frontiers in Plant Science 15: 1343787.
Su, M., G. Huang, Q. Zhang, et al. 2016. “The LEA Protein, ABR, Is Regulated by ABI5 and Involved in Dark‐Induced Leaf Senescence in Arabidopsis thaliana.” Plant Science 247: 93–103.
Tamura, K., and H. Bono. 2022. “Meta‐Analysis of RNA Sequencing Data of Arabidopsis and Rice Under Hypoxia.” Life 12: 1079.
Tran, L. S., K. Nakashima, Y. Sakuma, et al. 2004. “Isolation and Functional Analysis of Arabidopsis Stress‐Inducible NAC Transcription Factors That Bind to a Drought‐Responsive Cis‐Element in the Early Responsive to Dehydration Stress 1 Promoter.” Plant Cell 16: 2481–2498.
Tseng, G. C., D. Ghosh, and E. Feingold. 2012. “Comprehensive Literature Review and Statistical Considerations for Microarray Meta‐Analysis.” Nucleic Acids Research 40: 3785–3799.
Yonezawa, S., and H. Bono. 2023. “Meta‐Analysis of Heat‐Stressed Transcriptomes Using the Public Gene Expression Database From Human and Mouse Samples.” International Journal of Molecular Sciences 24: 13444. https://doi.org/10.3390/ijms241713444.
Yu, G. 2024. “Thirteen Years of clusterProfiler.” Innovation 5: 100722.
Yumiya, M., and H. Bono. 2025. “Meta‐Analysis of Wild Relatives and Domesticated Species of Rice, Tomato, and Soybean Using Publicly Available Transcriptome Data.” Life 15: 1088. https://doi.org/10.3390/life15071088.
Zhang, X. L., L. Jiang, Q. Xin, Y. Liu, J. X. Tan, and Z. Z. Chen. 2015. “Structural Basis and Functions of Abscisic Acid Receptors PYLs.” Frontiers in Plant Science 6: 88.
Grant Information:
20K06043 Japan Society for the Promotion of Science; 24K08867 Japan Society for the Promotion of Science
Contributed Indexing:
Keywords: RNA‐seq analysis; differential gene expression; microarray; reproducible workflow; transcriptome meta‐analysis; transcriptomics
Entry Date(s):
Date Created: 20260224 Date Completed: 20260224 Latest Revision: 20260225
Update Code:
20260225
DOI:
10.1111/gtc.70096
PMID:
41732110
Database:
MEDLINE
*Further Information*
*Meta-analysis is a powerful statistical approach for integrating multiple datasets, being increasingly applied in genome research. Transcriptome meta-analysis provides valuable insights into the functions of stress-responsive genes. In an earlier study, we developed the Stress Response score (SRscore), a novel metric designed to identify stress-responsive genes by capturing expression variability across multiple transcriptome datasets. Using Arabidopsis thaliana as a model, we demonstrated that the SRscore effectively quantifies the degree of stress responsiveness under specific stress conditions. However, extending this approach to other species and ensuring reproducible workflows required the development of a dedicated analytical tool. In this study, we introduce SRscore, an R package designed to streamline transcriptome meta-analyses. The package organizes the analytical workflow into three key steps: (1) pairing of comparison groups, (2) calculation of the expression change ratio (SRratio), and (3) computation of the SRscore. By providing curated datasets and metadata handling functions, SRscore enables straightforward, reproducible, and error-minimized analyses. Furthermore, SRscore outputs can be seamlessly integrated with visualization, enrichment analysis, and interpretation using other R/Bioconductor packages. This package not only facilitates the study of stress-responsive genes across species but also broadens the scope of transcriptome meta-analysis. SRscore package is freely available at CRAN (https://cran.r-project.org/package=SRscore).
(© 2026 Molecular Biology Society of Japan and John Wiley & Sons Australia, Ltd.)*