Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.

Transcriptomics has significantly grown as a functional genomics tool for understanding the expression of biological systems. The generated transcriptomics data can be utilised to produce a gene co-expression network that is one of the essential downstream omics data analyses. To date, several gene co-expression network databases that store correlation values, expression profiles, gene names and gene descriptions have been developed. Although these resources remain scattered across the Internet, such databases complement each other and support efficient growth in the functional genomics area. This review presents the features and the most recent gene co-expression network databases in crops and summarises the present status of the tools that are widely used for constructing the gene co-expression network. The highlights of gene co-expression network databases and the tools presented here will pave the way for a robust interpretation of biologically relevant information. With this effort, the researcher would be able to explore and utilise gene co-expression network databases for crops improvement.

Protein–protein interaction (PPI) is involved in every biological process that occurs within an organism. The understanding of PPI is essential for deciphering the cellular behaviours in a particular organism. The experimental data from PPI methods have been used in constructing the PPI network. PPI network has been widely applied in biomedical research to understand the pathobiology of human diseases. It has also been used to understand the plant physiology that relates to crop improvement. However, the application of the PPI network in aquaculture is limited as compared to humans and plants. This review aims to demonstrate the workflow and step-by-step instructions for constructing a PPI network using bioinformatics tools and PPI databases that can help to predict potential interaction between proteins. We used zebrafish proteins, the oestrogen receptors (ERs) to build and analyse the PPI network. Thus, serving as a guide for future steps in exploring potential mechanisms on the organismal physiology of interest that ultimately benefit aquaculture research.

Papaya (Carica papaya) is an economically important fruit crop that is mostly planted in tropical and subtropical regions. Major diseases of papaya, such as the papaya dieback disease (PDD), papaya ringspot virus (PRSV) disease, and papaya sticky disease (PSD), have caused large yield and economic losses in papaya-producing countries worldwide. Postharvest losses have also contributed to the decline in papaya production. Hence, there is an urgent need to secure the production of papaya for a growing world population. Integration of omics resources in crop breeding is anticipated in order to facilitate better-designed crops in the breeding programme. In papaya research, the application of omics and bioinformatics approaches are gradually increased and are underway. Hence, this review focuses on addressing omics technologies and bioinformatics that are used in papaya research. To date, four traits of the papaya have been studied using omics and bioinformatics approaches, which include its ripening process, abiotic stress, disease resistance, and fruit quality (i.e., sweetness, fruit shape, and fruit size). This review also highlights the potential of genetics and genomics data, as well as the systems biology approach that can be applied in a papaya-breeding programme in the near future.

Rice, a staple food consumed by half of the world’s population, is severely affected by the combined impact of abiotic and biotic stresses, with the former causing increased susceptibility of the plant to pathogens. Four microarray datasets for drought, salinity, tungro virus, and blast pathogen were retrieved from the Gene Expression Omnibus database. A modular gene co-expression (mGCE) analysis was conducted, followed by gene set enrichment analysis to evaluate the upregulation of module activity across different stress conditions. Over-representation analysis was conducted to determine the functional association of each gene module with stress-related processes and pathways. The protein–protein interaction network of mGCE hub genes was constructed, and the Maximal Clique Centrality (MCC) algorithm was applied to enhance precision in identifying key genes. Finally, genes implicated in both abiotic and biotic stress responses were validated using RT-qPCR. A total of 11, 12, 46, and 14 modules containing 85, 106, 253, and 143 hub genes were detected in drought, salinity, tungro virus, and blast. Modular genes in drought were primarily enriched in response to heat stimulus and water deprivation, while salinity-related genes were enriched in response to external stimuli. For the tungro virus and blast pathogen, enrichment was mainly observed in the defence and stress responses. Interestingly, RPS5, PKG, HSP90, HSP70, and MCM were consistently present in abiotic and biotic stresses. The DEG analysis revealed the upregulation of MCM under the tungro virus and downregulation under blast and drought in resistant rice, indicating its role in viral resistance. HSP70 showed no changes, while HSP90 was upregulated in susceptible rice during blast and drought. PKG increased during drought but decreased in japonica rice under salinity. RPS5 was highly upregulated during blast in both resistant and susceptible rice. The RT-qPCR analysis showed that all five hub genes were upregulated in all treatments, indicating their role in stress responses and potential for crop improvement.

Glucosinolates (GSLs) and cyanogenic glycosides (CGs) fulfil functions in plant defence and have been reported to be anticancer agents. Generally, GSL-containing plants do not produce CG, and vice versa, CG-containing plants do not synthesise GSLs. However, the production of both GSL and CG compounds was observed in Carica papaya. Additionally, several studies found both GSL glucotropaeolin and CG prunasin in papaya leaves. The advancement of genome technologies can be explored to elucidate the gene functions and other molecular discoveries in plants that might relate to GSLs and CGs. This review aims to discuss the complex interplay of the rare events whereby these two compounds (GSL and CG) co-occur in a bifurcation pathway in papaya. To our knowledge, this is the first review that highlights novel GSL and CG genes in papaya. Furthermore, species-specific pathways in papaya are also discussed and comprehensively described. The transcription factors involved in regulating GSL and CG biosynthesis pathways are also discussed, accompanied by relevant bioinformatic approaches that can help discover potential regulatory genes that control the production of prunasin and glucotropaeolin in papaya.