Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology

• Main Authors: Ili Nadhirah Jamil, Juwairiah Remali, Kamalrul Azlan Azizan, Nor Azlan Nor Muhammad, Masanori Arita, Hoe-Han Goh, Wan Mohd Aizat
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-06-01
• Series: Frontiers in Plant Science
• Subjects: bioinformatics; co-expression analysis; correlation; k-means clustering; machine learning; multivariate analysis
• Online Access: https://www.frontiersin.org/article/10.3389/fpls.2020.00944/full

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.