International Journal of Molecular Zoology, 2025, Vol.15, No.1, 10-19 http://animalscipublisher.com/index.php/ijmz 14 species (ROS) is stronger (Yang et al., 2020; Ye et al., 2024). Low-efficiency chickens are often active in immune and inflammatory pathways, which may indicate that they have devoted more energy to immune activities and affected feed conversion (Yang et al., 2020; Sinpru et al., 2021). Aggrey et al. (2014) and Xiao et al. (2021) found that the differences in gene expression related to vitamin transport, amino acid metabolism, and nitrogen cycling reflected the adaptability of different chickens in terms of nutrient utilization methods. The research by Wang et al. (2022) found that extracellular matrix remodeling and peroxisome function were related to differences in fat deposition and feed efficiency. 5.3 Computational tools and challenges In multi-omics integrated analysis, commonly used tools include RNA-Seq (for analyzing the transcriptome) and qRT-PCR (for verifying results), and some bioinformatics tools are used for functional annotation and network analysis (Yang et al., 2020; Xiao et al., 2021; Wang et al., 2022; Wang, 2024). However, omics data themselves have high dimensions and many types, and the expression differences among different tissues are also significant. Only by using robust statistical methods can the relationship between complex traits such as molecular characteristics and feed efficiency be accurately identified. The addition of non-coding RNAs such as lncRNA and circRNA and their regulatory networks will make the analysis more complex, and more advanced algorithms and models are needed for processing (Figure 2) (Karimi et al., 2021; Ye et al., 2024; Yuan et al., 2024). Figure 2 The bioinformatics pipeline for identifying annotated, known and novel lncRNAs (Adopted from Karimi et al., 2021) Image caption: The middle and right Venn diagrams illustrate the results of the potential coding ability of the transcript using five software and blasting the transcripts against four different databases, respectively (Adopted from Karimi et al., 2021) 6 Genotype and Breed-Specific Responses 6.1 Broiler vs. layer differences in FE mechanisms In the abdominal adipose tissue of broilers with high FE, extracellular matrix remodeling and pathways related to fat metabolism are more active, and the activity of peroxisomes is also stronger. Moreover, the G0/G1 switch gene 2 (G0S2) is considered to be related to fat deposition and muscle growth (Wang et al., 2022). Karimi et al. (2021) also found that long non-coding RNAs (lncRNA) in commercial broilers (such as Ross) regulate fat, carbohydrate metabolism, energy balance and growth genes compared with local chicken breeds, indicating that regulatory RNAs are important in the FE differences among different chicken breeds. Broilers with low RFI exhibited stronger mitochondrial function and better control of reactive oxygen species (ROS) in skeletal muscle.
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