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REGENOMICS: A web-based application for plant REGENeration-associated transcriptOMICS analyses

ABSTRACT: Graphical abstract In plants, differentiated somatic cells exhibit an exceptional ability to regenerate new tissues, organs, or whole plants. Recent studies have unveiled core genetic components and pathways underlying cellular reprogramming and de novo tissue regeneration in plants. Although high-throughput analyses have led to key discoveries in plant regeneration, a comprehensive organization of large-scale data is needed to further enhance our understanding of plant regeneration. Here, we collected all currently available transcriptome datasets related to wounding responses, callus formation, de novo organogenesis, somatic embryogenesis, and protoplast regeneration to construct REGENOMICS, a web-based application for plant REGENeration-associated transcriptOMICS analyses. REGENOMICS supports single- and multi-query analyses of plant regeneration-related gene-expression dynamics, co-expression networks, gene-regulatory networks, and single-cell expression profiles. Furthermore, it enables user-friendly transcriptome-level analysis of REGENOMICS-deposited and user-submitted RNA-seq datasets. Overall, we demonstrate that REGENOMICS can serve as a key hub of plant regeneration transcriptome analysis and greatly enhance our understanding on gene-expression networks, new molecular interactions, and the crosstalk between genetic pathways underlying each mode of plant regeneration. The REGENOMICS web-based application is available at http://plantregeneration.snu.ac.kr.

SUBMITTER: Bae S

PROVIDER: S-EPMC9249971 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The rapid assignment of genotypes to phenotypes has been a historically challenging process. The discovery of genes encoding biosynthetic pathway enzymes for defined plant specialized metabolites has been informed and accelerated by the detection of gene clusters. Unfortunately, biosynthetic pathway genes are commonly dispersed across chromosomes or reside in genes clusters that provide little predictive value. More reliably, transcript abundance of genes underlying biochemical pathways for plant specialized metabolites display significant coregulation. By rapidly identifying highly coexpressed transcripts, it is possible to efficiently narrow candidate genes encoding pathway enzymes and more easily predict both functions and functional associations. Mutual Rank (MR)-based coexpression analyses in plants accurately demonstrate functional associations for many specialized metabolic pathways; however, despite the clear predictive value of MR analyses, the application is uncommonly used to drive new pathway discoveries. Moreover, many coexpression databases aid in the prediction of both functional associations and gene functions, but lack customizability for refined hypothesis testing. To facilitate and speed flexible MR-based hypothesis testing, we developed MutRank, an R Shiny web-application for coexpression analyses. MutRank provides an intuitive graphical user interface with multiple customizable features that integrates user-provided data and supporting information suitable for personal computers. Tabular and graphical outputs facilitate the rapid analyses of both unbiased and user-defined coexpression results that accelerate gene function predictions. We highlight the recent utility of MR analyses for functional predictions and discoveries in defining two maize terpenoid antibiotic pathways. Beyond applications in biosynthetic pathway discovery, MutRank provides a simple, customizable and user-friendly interface to enable coexpression analyses relating to a breadth of plant biology inquiries. Data and code are available at GitHub: https://github.com/eporetsky/MutRank.

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REGENOMICS: A web-based application for plant REGENeration-associated transcriptOMICS analyses

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