{"title":"使用元分析统一RNA-seq数据:生物信息学框架及其在植物基因组学中的应用","authors":"Bahman Panahi , Rasmieh Hamid , Feba Jacob , Hossein Mohammadzadeh Jalaly","doi":"10.1016/j.cpb.2025.100523","DOIUrl":null,"url":null,"abstract":"<div><div>RNA sequencing (RNA-Seq) has transformed plant genomics by enabling high-resolution profiling of gene expression across various conditions. However, integrating RNA-Seq data from different studies is challenging due to variability in experimental designs, sequencing platforms, and data processing workflows, which limits the comparability and applicability of transcriptomic datasets. This review provides an overview of current meta-analysis approaches that address these challenges and enhance the consistency, accuracy, and interpretability of RNA-Seq data integration. We discuss methodologies such as data normalization techniques, statistical frameworks for aggregating results, and computational tools that reduce inter-study variability. We also highlight preprocessing strategies, including batch effect correction and standardized gene annotation pipelines, which facilitate reliable cross-study comparisons. We emphasize the practical significance of RNA-Seq meta-analysis in plant genomics. Meta-analysis improves the identification of consistent differentially expressed genes (DEGs), enhances functional annotation, and uncovers conserved regulatory mechanisms across plant species. These insights have applications in precision breeding, stress-response studies, and trait improvement programs. For researchers implementing meta-analysis, this review outlines key considerations, recommended practices, and available resources. We conclude by highlighting the need for standardized protocols and promoting multi-omics integration to unlock deeper insights. As transcriptomic datasets expand, meta-analysis will play a crucial role in advancing our understanding of plant biology and its application in agriculture.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100523"},"PeriodicalIF":4.5000,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unifying RNA-seq data using meta-analysis: Bioinformatics frameworks and application for plant genomics\",\"authors\":\"Bahman Panahi , Rasmieh Hamid , Feba Jacob , Hossein Mohammadzadeh Jalaly\",\"doi\":\"10.1016/j.cpb.2025.100523\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>RNA sequencing (RNA-Seq) has transformed plant genomics by enabling high-resolution profiling of gene expression across various conditions. However, integrating RNA-Seq data from different studies is challenging due to variability in experimental designs, sequencing platforms, and data processing workflows, which limits the comparability and applicability of transcriptomic datasets. This review provides an overview of current meta-analysis approaches that address these challenges and enhance the consistency, accuracy, and interpretability of RNA-Seq data integration. We discuss methodologies such as data normalization techniques, statistical frameworks for aggregating results, and computational tools that reduce inter-study variability. We also highlight preprocessing strategies, including batch effect correction and standardized gene annotation pipelines, which facilitate reliable cross-study comparisons. We emphasize the practical significance of RNA-Seq meta-analysis in plant genomics. Meta-analysis improves the identification of consistent differentially expressed genes (DEGs), enhances functional annotation, and uncovers conserved regulatory mechanisms across plant species. These insights have applications in precision breeding, stress-response studies, and trait improvement programs. For researchers implementing meta-analysis, this review outlines key considerations, recommended practices, and available resources. We conclude by highlighting the need for standardized protocols and promoting multi-omics integration to unlock deeper insights. As transcriptomic datasets expand, meta-analysis will play a crucial role in advancing our understanding of plant biology and its application in agriculture.</div></div>\",\"PeriodicalId\":38090,\"journal\":{\"name\":\"Current Plant Biology\",\"volume\":\"43 \",\"pages\":\"Article 100523\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Plant Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221466282500091X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PLANT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Plant Biology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221466282500091X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
Unifying RNA-seq data using meta-analysis: Bioinformatics frameworks and application for plant genomics
RNA sequencing (RNA-Seq) has transformed plant genomics by enabling high-resolution profiling of gene expression across various conditions. However, integrating RNA-Seq data from different studies is challenging due to variability in experimental designs, sequencing platforms, and data processing workflows, which limits the comparability and applicability of transcriptomic datasets. This review provides an overview of current meta-analysis approaches that address these challenges and enhance the consistency, accuracy, and interpretability of RNA-Seq data integration. We discuss methodologies such as data normalization techniques, statistical frameworks for aggregating results, and computational tools that reduce inter-study variability. We also highlight preprocessing strategies, including batch effect correction and standardized gene annotation pipelines, which facilitate reliable cross-study comparisons. We emphasize the practical significance of RNA-Seq meta-analysis in plant genomics. Meta-analysis improves the identification of consistent differentially expressed genes (DEGs), enhances functional annotation, and uncovers conserved regulatory mechanisms across plant species. These insights have applications in precision breeding, stress-response studies, and trait improvement programs. For researchers implementing meta-analysis, this review outlines key considerations, recommended practices, and available resources. We conclude by highlighting the need for standardized protocols and promoting multi-omics integration to unlock deeper insights. As transcriptomic datasets expand, meta-analysis will play a crucial role in advancing our understanding of plant biology and its application in agriculture.
期刊介绍:
Current Plant Biology aims to acknowledge and encourage interdisciplinary research in fundamental plant sciences with scope to address crop improvement, biodiversity, nutrition and human health. It publishes review articles, original research papers, method papers and short articles in plant research fields, such as systems biology, cell biology, genetics, epigenetics, mathematical modeling, signal transduction, plant-microbe interactions, synthetic biology, developmental biology, biochemistry, molecular biology, physiology, biotechnologies, bioinformatics and plant genomic resources.