Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent

The gap

Evidence profile

Sourced from the abstract and future work of the source papers, classified as general, drawn from work published between 2022 and 2026, spanning 3 journals. Those papers have been cited 739 times in total.

Supporting evidence — 4 representative gaps

• METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks (2022) · Microbiome · cited 625× · doi

  Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling.

  generalabstractevidence 5/5
  Keywords: currently available metabolic microbial tools analyses genomic annotate depict functions extent standardized approaches comprehensive characterization
• Omics approaches in understanding the benefits of plant-microbe interactions (2024) · Frontiers in Microbiology · cited 114× · doi

  Intensive agricultural practices on limited land with reduced inputs present a significant global fertilizer and agrochemical challenge. Plants such as humans interact with a diverse range of microorganisms that can have both beneficial and harmful effects. Despite ongoing advancements in omics-based approaches, lack experimental and computational validation procedures standardized protocols (Diwan et al., 2022). Discovering complex microbial communities remains a significant challenge, compounded by the fact that only a small fraction of microbes has been thoroughly characterized to date. The primary obstacle lies in the selection of the most suitable technology and methods to effectively address specific problems. Additionally, limitations in available databases present another hurdle that in diverse environments must be overcome to advance microbial research comprehensively (Kumar et al., 2023). Despite these advancements, the development of analytical methods to integrate multiple datasets remains a challenge in the field. To address this, there is a growing need for more multi-omics studies that incorporate classical approaches, such as metabolomics, transcriptomics, proteomics, and metagenomics, and embrace new and emerging techniques, such as genomics, epigenomics, and lipidomics (Kimotho and Maina, 2024). and systems requires pathways, The complexity of these interactions, encompassing various signaling molecules, regulatory networks, makes deciphering their dynamics and outcomes challenging. Additionally, elucidating the precise mechanisms and kinetics transduction, and response of molecular signal recognition, in different plant-microbe sophisticated experimental approaches and analytical techniques. Furthermore, the diversity of microbial species and their adaptation strategies add another layer of complexity, necessitating comprehensive studies across diverse microbial taxa and ecological niches. Integrating multi-omics data from genomics, transcriptomics, proteomics, and metabolomics poses computational and analytical standardization, and challenges, interpretation. Metabolomics, proteomics, and metagenomics are invaluable tools for deciphering the complexities of plant-microbe interactions, but they pose distinct challenges. Metabolomics applies to the analytical complexity of diverse metabolites, identification hurdles, and dynamic nature of metabolite levels. Transcriptomics faces issues with RNA including data integration, transcriptomics,

  generalfuture workevidence 5/5
  Keywords: diverse microbial analytical metabolomics transcriptomics challenge omics approaches proteomics complexity present signi cant despite advancements
• A Review on Modeling the Fermentation Process of Dairy Products Using Multi-Omics and Artificial Intelligence Approaches (2026) · Fermentation · doi

  The fermentation of dairy products is a complex biological process shaped by the dynamic interaction of substrates, microbial communities, metabolites, and environmental factors. This review emphasizes that multi-omics approaches provide molecular-level information about fermentation by correlating processes such as gene expression, micro- bial composition, protein conversion, and metabolite production, thus facilitating a better understanding of the process. However, the integration of multi-omics datasets with advanced computational approaches such as ML, DL, and XAI represents a reliable step towards data-driven and predictive fermentation dynamics control. Although multi-omics technologies and artificial intelligence-based modeling approaches have provided impor- tant insights into understanding the process at the molecular level in dairy fermentation, several challenges remain in fully integrating these methods or technologies into routine scientific research or industrial production. Metabolomics, genomics, proteomics, or tran- scriptomics approaches differ significantly in terms of the structure, scale, and size of the data. Therefore, evaluating data from different omics layers together becomes difficult, and up-to-date computational methods are needed for data integration. Additionally, limitations in multi-omics approaches include missing data in terms of reproducibility, biological interpretability, and generalizability, small sample sizes, batch effect, and high computational costs. Ultimately, the importance of multi-omics data integration methods such as DIABLO, SNF, and MOFA in overcoming these limiting factors is noteworthy. https://doi.org/10.3390/fermentation12080391 Fermentation 2026, 12, 391 25 of 32 Through these methods, which have been discussed in detail earlier, the relationships between microbial communities, metabolite formation, hydrolysis in proteins or fats, and sensory quality characteristics can be evaluated and explained in a holistic approach. On the other hand, in addition to these methods, XAI approaches make a significant contri- bution to eliminating uncertainties in relationships that cannot be obtained from ML and DL models and that remain hidden. For example, SHAP can reveal the effects of genes, metabolites, and proteins on outputs, while DT can facilitate fermentation optimization. XAI approaches make it possible not only to make accurate predictions but also to explain biological mechanisms and eliminate uncertainties. Furthermore, DT, developed by com- bining multi-omics data, IoT and artificial intelligence-based models in dairy fermentation, contributes significantly to advancements in this field. This system, which simulates fer- mentation, offers advantages such as real-time monitoring, optimization of the production process, and early error detection. Ultimately, this allows for standardized, high-quality, and sustainable production, and the completion of fermentation with minimal losses. As a result, thanks to these technologies, the fermentation of dairy products is moving from a descriptive approach to a data-driven and predictive one. Furthermore, the fact that most current studies are based on laboratory-scale experiments and that validation under indus- trial fermentation conditions is limited remains a challenge. In the future, the widespread adoption of multi-omics data integration methods in both industrial and scientific studies, and the real-time implementation of these systems, indicates that precision fermentation applications can be used across a wider range. Ultimately, these approaches have the potential to transform dairy fermentation from an empirical process into a predictive and precise biotechnological platform. Author Contributions: Conceptualization, M.E.T.; investigation, M.E.T. and Z.Ç.T.; writing—original draft preparation, M.E.T. and Z.Ç.T.; writing—review and editing, M.E.T. and Z.Ç.T.; visualization, M.E.T. and Z.Ç.T.; supervision, M.E.T. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article. Conflicts of Interest: The authors declare no conflicts of interest.

  generalfuture workevidence 5/5
  Keywords: fermentation omics approaches multi dairy process production integration biological review computational predictive technologies based ultimately
• Feature Review Papers in Microbial Metabolism, Physiology & Genetics, 2nd Edition (2026) · Fermentation · doi

  Several priorities emerge for the coming years. First, computation is reshaping dis- covery: machine learning and generative models now mine microbiomes and design peptides at a scale unattainable with classical screening, as demonstrated by the recovery of thousands of candidate AMPs from the global microbiome [9]. Coupling such in silico design with rapid fermentation-based expression would shorten the cycle from sequence https://doi.org/10.3390/fermentation12100450 Fermentation 2026, 12, 450 3 of 4 to validated molecule. Second, genome mining and the targeted activation of silent biosyn- thetic gene clusters [10] (together with advances in promoter engineering and heterologous expression in tractable microbial chassis [11]) may enable access to the large “dark mat- ter” of microbial chemistry unattainable with conventional cultivation, offering a route to the higher titres required for tacrolimus-, hypocrellin-, and p-cymene-type products for economic production. Third, the physiological dimension deserves renewed attention. Because metabolite output depends on growth conditions, bioprocess and fermentation control (pH, dissolved oxygen, feeding strategy, co-culture, and stress conditioning) will be as decisive as genetic engineering in determining what a strain produces. Finally, the “postbiotic” field must move from association to causation and standardisation: defining which metabolites, at which doses, produce reproducible host effects, and building the analytical and regulatory frameworks to deliver them safely. Underpinning all of these priorities is a need for integration: combining genomics, transcriptomics, proteomics, and metabolomics so that the genotype–physiology–metabolite relationship can be predicted rather than merely observed.

  generalfuture workevidence 5/5
  Keywords: fermentation priorities design unattainable expression engineering microbial metabolite several emerge coming years first computation reshaping

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