Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants
Abstract
:1. Introduction
2. Development of Sequencing Technologies for Ruminants
3. Single-Omics Technologies
3.1. Genomics
3.2. Epigenomics
3.3. Transcriptomics
3.4. Proteomics
3.5. Metabolomics
3.6. Microbiomics
4. Pan-Omics Integrated Analysis and Data Integration Methods
5. Application of Pan-Omics Technologies in the Study of Important Economic Traits and Functions of Ruminants
5.1. Application of Pan-Omics Technologies in the Study of Growth and Reproductive Traits of Ruminants
5.2. Application of Pan-Omics Technologies in the Study of Production Performance of Ruminants
5.3. Application of Pan-Omics Technologies in the Study of the Digestive System and Symbiotic Mechanisms of Ruminants
5.4. Application of Pan-Omics Technologies in Adaptation to Extreme Environments and Diseases Prevention in Ruminants
6. Conclusions and Outlook
- (1)
- Data Integration and Interpretation: The integration of multi-omics data (genomics, transcriptomics, proteomics, metabolomics, etc.) poses significant challenges due to the complexity of biological systems and the sheer volume of data generated. The heterogeneity of data types and the lack of standardized analytical frameworks can lead to difficulties in data harmonization and interpretation, ultimately affecting the reliability and reproducibility of findings [130].
- (2)
- Technological and Computational Barriers: Advanced computational tools and resources are required to handle, process, and analyze large-scale omics data. However, the availability of such resources can be limited, particularly in the context of ruminant research, where funding and access to cutting-edge technology may be constrained. Additionally, the development and application of machine learning algorithms for predictive modeling in ruminants are still in their infancy, posing a barrier to the widespread adoption of pan-omics [131].
- (3)
- Biological Complexity and Trait Variability: Ruminants exhibit a high degree of biological complexity and trait variability, influenced by environmental factors, management practices, and genetic diversity. This complexity can obscure the relationships between omics layers and phenotypic traits, making it challenging to draw meaningful conclusions. Furthermore, the dynamic nature of the ruminant microbiome adds another layer of complexity to pan-omics studies [132].
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Omics | Pan-Omics Integrative Analysis | Function | References |
---|---|---|---|
Genomics | Transcriptomics | (1) Gene expression regulation: Effects of gene structural variations (SNP, CNV, SV, etc.) on gene expression. (2) Identification of functional variants: Finding gene variants that significantly impact the organism’s function. (3) Developmental and tissue-specific insights: Revealing gene expression patterns in different tissues or developmental stages. | [87,88] |
Proteomics | (1) Correlation between genotype and phenotype and functional validation: Analyzing the relationship between gene variants and protein expression levels and post-translational modifications, and verifying the functional changes at the protein level caused by sequence variations. (2) Complex trait analysis and biomarker discovery: Dissecting the genetic variations in traits caused by changes in protein networks, and identifying protein biomarkers influenced by gene variants. | [89] | |
Epigenomics Transcriptomics | (1) Regulation of gene activity and phenotypic variation: Elucidating the effects of DNA methylation, histone modifications, and chromatin accessibility on gene expression and genome stability to obtain different phenotypes. (2) Environmental interactions: Exploring the regulation of gene expression and phenotypic changes by environmental factors. | [90,91] | |
Transcriptomics | Proteomics | (1) Correlation between mRNA and protein levels and functional validation: Understanding the mechanisms and effects of post-transcriptional regulation of mRNA, and verifying the relationship between gene expression and protein levels and activity. (2) Complex trait analysis: Gaining a more comprehensive understanding of functional protein changes in the regulatory mechanisms of gene expression changes. | [92,93] |
Metagenomics Metabolomics | (1) Regulation of metabolic products by the combined effects of gene expression and the microbiome. (2) Revealing the roles and impacts of microorganisms in host metabolic processes. (3) Discovering the interactions between gene expression processes, the microbiome, and metabolites on the physiological state of the organism. (4) Providing a deeper understanding of the internal regulatory networks and their complexity within the organism. | [93,94] | |
Epigenomics | (1) Gene expression regulation: Analyzing how epigenetic modifications (DNA methylation, histone modifications, etc.) regulate gene expression. (2) Understanding cellular differentiation and different physiological mechanisms: Revealing the processes of cell and tissue development and identifying epigenetic dysregulation associated with abnormal gene expression in various physiological states. | [94] | |
Metabolomics | Proteomics | Analyzing the molecular mechanisms and regulatory pathways of different omics in response to external stimuli, to more intuitively understand the upstream and downstream regulatory relationships between metabolites, enzymes, and genes. | [95] |
Metagenomics | (1) Identifying metabolites produced by the microbiome and their impact on host metabolic pathways. (2) Elucidating the relationship between microbial diversity and function and host metabolic health. | [96,97] |
Data Integration Methodology | Advantages | Disadvantages | Typical Methods | References |
---|---|---|---|---|
Connection-based methods | Simple to apply and does not require complex transformations | Increased computational complexity; unable to effectively capture unique structures and relationships of data types | Multi-Omics Factor Analysis (MOFA); iCluster | [98] |
Transformation-based methods | Effectively identifies and captures correlations between datasets; simplifies analysis through low-dimensional space | Unable to capture complex biological interactions; the transformation process is computationally demanding | Canonical Correlation Analysis (CCA); Partial Least Squares (PLS) | [99] |
Network construction-based methods | Effectively captures interactions and relationships between datasets; visually interprets biological data intuitively | Building and integrating multiple networks is computationally and conceptually complex; limited scalability | Similarity Network Fusion (SNF); Multi-Omics Graph Convolutional Network (MOGCN) | [100] |
Matrix factorization-based methods | Effectively reduces dimensions while capturing underlying patterns and structures; can handle various types of omics data and missing values | The decomposed components may be difficult to interpret biologically | Joint Non-Negative Matrix Factorization (NMF); Multi-Omics Tensor Decomposition | [101] |
Bayesian and probabilistic methods | Effectively models uncertainty and variability in data; can capture complex, non-linear relationships | Computationally demanding; requires expertise in probabilistic modeling and Bayesian statistics | Bayesian Network Models; Multi-Omics Factor Analysis (MOFA+) | [102] |
Deep learning-based methods | Can capture complex, non-linear relationships between omics datasets; automatically learns relevant features and representations from data | High data requirements; deep learning models are challenging to interpret biologically | Deep Learning Autoencoders; Variational Autoencoders (VAE) | [103] |
Hybrid methods | Leverages multiple integration strategies to improve overall performance | Increased complexity; balancing and integrating results from different methods is challenging | Integrative Clustering (iClusterPlus); Multi-Omics Factor Analysis via Transfer Learning | [104] |
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Gao, Z.; Lu, Y.; Li, M.; Chong, Y.; Hong, J.; Wu, J.; Wu, D.; Xi, D.; Deng, W. Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants. Int. J. Mol. Sci. 2024, 25, 9271. https://doi.org/10.3390/ijms25179271
Gao Z, Lu Y, Li M, Chong Y, Hong J, Wu J, Wu D, Xi D, Deng W. Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants. International Journal of Molecular Sciences. 2024; 25(17):9271. https://doi.org/10.3390/ijms25179271
Chicago/Turabian StyleGao, Zhendong, Ying Lu, Mengfei Li, Yuqing Chong, Jieyun Hong, Jiao Wu, Dongwang Wu, Dongmei Xi, and Weidong Deng. 2024. "Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants" International Journal of Molecular Sciences 25, no. 17: 9271. https://doi.org/10.3390/ijms25179271