International Journal of Molecular Zoology, 2025, Vol.15, No.1, 10-19 http://animalscipublisher.com/index.php/ijmz 17 among different omics platforms. Wang et al. (2022) found that compared with the traditional RNA-seq, 3’UTR-seq has a larger error and a lower alignment rate, which may have an impact on data interpretation and repeatability. Zampiga (2018) suggested that the integration of multi-omics data is also quite challenging. This is because the data types are diverse, the dimensions are large, and the analysis methods are not uniform, which limits the construction of a more complete FE regulation model. 9.2 Advancing toward predictive omics models Some recent studies have identified many key genes, pathways and regulatory elements related to FE, such as lncRNA, circRNA and protein-coding genes, laying the foundation for the construction of the model (Yang et al., 2020; Karimi et al., 2021; Xiao et al., 2021). To turn these research results into reliable tools that can be used in breeding and management, it is highly necessary to establish larger and more representative datasets and develop new algorithms that can integrate transcriptome, metabolome and phenotypic data. Some circRNA and gene expression characteristics have now shown the potential to predict FE, but for application in actual breeding, further verification and optimization are still needed (Zampiga et al., 2018; Yuan et al., 2024). 9.3 Future integration with proteomics and microbiomics Future research should also integrate proteomic and microbiome data to gain a more comprehensive understanding of the feed efficiency (FE) mechanism in chickens. The fact that the proteome can reveal changes in post-transcriptional regulation and protein expression is a good complement to the transcriptome. The microbiome can help analyze the impact of gut microbiota on nutrient utilization and metabolic efficiency (Zampiga et al., 2018). It is promising to discover new regulatory networks and metabolic pathways through the joint analysis of proteo-genomics and the microbiome. The research conducted by Zampiga et al. (2018) demonstrated that this could provide support for the construction of a more accurate and comprehensive FE prediction model, promoting the improvement of chicken feed efficiency towards the integration of multiple omics. Acknowledgments AnimalSci Publisher appreciates the comments from Dr. Xie and Dr. Yang on the manuscript of this study. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Aggrey S., Karnuah A., Sebastian B., and Anthony N., 2010, Genetic properties of feed efficiency parameters in meat-type chickens, Genetics, Selection, Evolution, 42: 25. https://doi.org/10.1186/1297-9686-42-25 Aggrey S., Lee J., Karnuah A., and Rekaya R., 2014, Transcriptomic analysis of genes in the nitrogen recycling pathway of meat-type chickens divergently selected for feed efficiency, Animal Genetics, 45(2): 215-222. https://doi.org/10.1111/age.12098 Beauclercq S., Nadal-Desbarats L., Hennequet-Antier C., Gabriel I., Tesseraud S., Calenge F., Bihan-Duval L., and Mignon-Grasteau S., 2018, Relationships between digestive efficiency and metabolomic profiles of serum and intestinal contents in chickens, Scientific Reports, 8: 6678. https://doi.org/10.1038/s41598-018-24978-9 Bernard M., Lecoeur A., Coville J., Bruneau N., Jardet D., Lagarrigue S., Meynadier A., Calenge F., Pascal G., and Zerjal T., 2024, Relationship between feed efficiency and gut microbiota in laying chickens under contrasting feeding conditions, Scientific Reports, 14: 8210. https://doi.org/10.1038/s41598-024-58374-3 Choi J., Kong B., Bowker B., Hong Z., and Kim W., 2023, Nutritional strategies to improve meat quality and composition in the challenging conditions of broiler production: a review, Animals, 13(8): 1386. https://doi.org/10.3390/ani13081386 Dao H., Sharma N., Swick R., and Moss A., 2023, Feeding recycled food waste improved feed efficiency in laying hens from 24 to 43 weeks of age, Scientific Reports, 13: 8261. https://doi.org/10.1038/s41598-023-34878-2
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