Animal Molecular Breeding 2024, Vol.14, No.2, 141-153 http://animalscipublisher.com/index.php/amb 143 can identify which genes are active, how their expression levels change in different tissues, and how these changes correlate with phenotypic traits (Fang et al., 2020; Arishima et al., 2022). 3.2 Techniques for transcriptomic analysis Several techniques are employed in transcriptomic analysis, with RNA sequencing (RNA-seq) being the most prominent. RNA-seq allows for the comprehensive profiling of all transcripts in a sample, providing insights into gene expression levels, alternative splicing events, and novel transcript discovery. High-throughput sequencing technologies, such as short-read and long-read sequencing, are commonly used to capture the complexity of the transcriptome (Foissac et al., 2019; Arishima et al., 2022). Additionally, integrative approaches combining transcriptomics with other omics data, such as proteomics and epigenomics, enhance the understanding of gene regulation and functional genomics (Kumar et al., 2016; Qin et al., 2016). 3.3 Applications of transcriptomics in breeding programs Transcriptomics has numerous applications in livestock breeding programs. By identifying genes associated with economically important traits, such as milk production, meat quality, and disease resistance, transcriptomic data can inform selective breeding strategies. For instance, integrative analyses of tissue-specific genes with genome-wide association studies (GWAS) have identified candidate genes and relevant tissues for traits like male fertility and body conformation in cattle (Fang et al., 2020). Moreover, transcriptomic data can be used to improve genome annotation, predict gene function, and develop genomic selection models that enhance breeding efficiency (Diniz and Ward, 2021; Verardo et al., 2023). 3.4 Case study: transcriptomic insights into disease resistance A comprehensive analysis of 124 transcriptomes from various tissues in Japanese Black cattle revealed significant insights into disease resistance. By examining the expression profiles of causative genes for genetic disorders, researchers identified disease-relevant expression patterns that could be targeted in breeding programs to enhance disease resistance (Arishima et al., 2022). Another study involving 723 RNA-seq data from cattle tissues identified tissue-specific genes and their roles in immune response, providing a valuable resource for understanding the genetic basis of disease resistance and developing strategies to improve livestock health (Fang et al., 2020). These findings underscore the potential of transcriptomics to uncover the molecular mechanisms underlying disease resistance and inform breeding strategies aimed at producing healthier livestock. 4 Proteomics: Understanding Protein Dynamics 4.1 Role of proteomics in livestock breeding Proteomics, the large-scale study of proteins, plays a crucial role in livestock breeding by providing insights into the molecular mechanisms underlying various traits. Proteomics allows researchers to monitor in vivo performances of livestock animals, such as growth, fertility, and milk quality, and to understand the molecular processes that affect meat quality (D’Alessandro and Zolla, 2013). By identifying and validating biomarkers associated with important traits, proteomics can enhance the selection process in breeding programs, leading to improved animal performance and productivity (Long, 2020). 4.2 Techniques for protein profiling Several advanced techniques are employed in proteomic analysis to study protein composition, structure, function, and interactions. Common methods include 2D gel electrophoresis, MALDI-TOF/MS, X-ray crystallography, NMR, protein microarrays, two-hybrid screening, and western blotting (Mote and Filipov, 2020). These techniques enable the generation of large proteomic datasets, which can be used to identify changes in protein expression, interactions, or modifications. The integration of these datasets with other omics data provides a comprehensive view of cellular functions and their regulation (Loor et al., 2015; Dihazi et al., 2018). 4.3 Application of proteomics in trait selection Proteomics has been extensively applied to understand and improve various traits in livestock. For instance, changes in protein profiles during myogenesis have been studied in cattle, pigs, and fowl, providing insights into key stages of muscle development and identifying processes that are similar or divergent between species (Picard
RkJQdWJsaXNoZXIy MjQ4ODYzNA==