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Review
. 2022 Oct 5;11(19):2614.
doi: 10.3390/plants11192614.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

Muhammad-Redha Abdullah-Zawawi  1   2 , Nisha Govender  2 , Sarahani Harun  2 , Nor Azlan Nor Muhammad  2 , Zamri Zainal  2   3 , Zeti-Azura Mohamed-Hussein  2   3
Affiliations
Review

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

Muhammad-Redha Abdullah-Zawawi et al. Plants (Basel). .

Abstract

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

Keywords: computational approaches; functional genomics; metabolomics; plant breeding; systems biology; transcriptomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Scholarly omics-related articles published under the plant sciences category from 2012 to 2022. The literature search using Web of Science (https://www.webofknowledge.com) search engine was accessed on 18 September 2022 with Boolean ‘or’ and the following keywords: genomic, genome, transcriptomic, transcriptome, proteomic, proteome, metabolomic and metabolome.
Figure 2
Figure 2
Type of omics datasets. Omics datasets can be divided into two categories: modules and interactions. Module data indicate molecular sequences of biological systems; DNA (genome) transcribed into mRNA (transcriptome), later translated into proteins (proteome), and lastly synthesized into metabolite (metabolome). Interaction data, known as interactomes, represent the relationships of module data generated from respective platforms. The omics resources of the omics technologies and network interactions can be downloaded from the databases.
Figure 3
Figure 3
Guilt-by-association techniques for candidate gene discovery. (A) Preliminary selection of candidate gene using the biological network, in which the co-functional information of candidate gene with known gene (i.e., metabolic or other functional genes) can be extracted from gene correlation and coregulation/regulatory network (steps 1–2). (B) Co-functional information can be inferred by gene context analyses (steps 3–7). Candidate genes will be observed based on enriched in a similar function (step 3), clustered in a monophyletic group (step 4), shared similar distributions of motifs (step 5) and exon/intron structure (step 6), and lastly consist similar CREs (step 7). (C) The ranking of the high confidence candidate gene will be observed based on the predicted co-function similarity.
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