Abstract
Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.
Highlights
The acquisition of multi-omics data sets has become an integral component of modern molecular biology and biotechnology
This review focuses on expression-based omics from transcriptomics, proteomics, and metabolomics to further clarify strategies taken to integrate such large-scale expression data
One prominent database used for plant metabolic pathway reference is Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg/), but other more organism-specific databases such as AraCyc for Arabidopsis, CitrusCyc for citrus, and SolCyc for Solanaceae species (Foerster et al, 2018) do exist
Summary
The acquisition of multi-omics data sets has become an integral component of modern molecular biology and biotechnology. The core data sets of systems biology are transcriptomics, proteomics, and metabolomics, providing the expression levels of transcripts, proteins, and metabolites, respectively (Aizat et al, 2018a). What we need is a well-defined methodological scheme for multi-omics integration (MOI) to extract, combine, and critically associate different data sets to allow researchers to decipher the seemingly complex biological results at hand (Fondi and Liò, 2015; Hughes, 2015; Wang et al, 2018). This review focuses on expression-based omics from transcriptomics, proteomics, and metabolomics to further clarify strategies taken to integrate such large-scale expression data (transcript, protein, and metabolite)
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