Determinant genetic markers of semen quality in livestock.

The reproductive efficiency of livestock is crucial for agricultural productivity and economic sustainability. One critical factor in successful fertilization and the viability of offspring is the quality of semen. Poor semen quality, especially in frozen-thawed semen used in artificial insemination (AI) have been shown to influence conception outcomes, resulting a negative impact on livestock production. Recent advancements in genetic research have identified specific markers linked to semen quality traits in various livestock species, such as cattle, sheep, goats, pigs, buffalo, and equines. These genetic markers are essential in screening males for breeding suitability, which in turn enhances selective breeding programs. Understanding these markers is crucial for improving reproductive performance and increasing productivity in livestock populations. This review offers a comprehensive overview of the genetic markers associated with semen quality in key livestock. It explores the underlying genetic mechanisms and their practical implications in animal breeding and management. The review underscores the importance of integrating genetic insights into breeding strategies to optimize reproductive efficiency and ensure the sustainable development of livestock industries.

A comprehensive review of livestock development: insights into domestication, phylogenetics, diversity, and genomic advances.

Livestock plays an essential role in sustaining human livelihoods, offering a diverse range of species integral to food security, economic stability, and cultural traditions. The domestication of livestock, which began over 10,000 years ago, has driven significant genetic changes in species such as cattle, buffaloes, sheep, goats, and pigs. Recent advancements in genomic technologies, including next-generation sequencing (NGS), genome-wide association studies (GWAS), and genomic selection, have dramatically enhanced our understanding of these genetic developments. This review brings together key research on the domestication process, phylogenetics, genetic diversity, and selection signatures within major livestock species. It emphasizes the importance of admixture studies and evolutionary forces like natural selection, genetic drift, and gene flow in shaping livestock populations. Additionally, the integration of machine learning with genomic data offers new perspectives on the functional roles of genes in adaptation and evolution. By exploring these genomic advancements, this review provides insights into genetic variation and evolutionary processes that could inform future approaches to improving livestock management and adaptation to environmental challenges, including climate change.

Opportunities for CRISPR-Cas9 application in farm animal genetic improvement.

CRISPR-Cas9 has emerged as a powerful tool in livestock breeding, enabling precise genetic modifications to address genetic diseases, enhance productivity, and develop disease-resistant animal breeds. A thorough analysis of previous research highlights the potential of CRISPR-Cas9 in overcoming genetic disorders by targeting specific mutations in genes. Furthermore, its integration with reproductive biotechnologies and genomic selection facilitates the production of gene-edited animals with high genomic value, contributing to genetic enhancement and improved productivity. Additionally, CRISPR-Cas9 opens new avenues for developing disease-resistant livestock and creating innovative breeding models for high-quality production. A key trend in the field is the development of multi-sgRNA vectors to correct mutations in various genes linked to productivity traits or certain diseases within individual genomes, thereby increasing resistance in animals. However, despite the potential advantages of CRISPR-Cas9, public acceptance of genetically modified agricultural products remains uncertain. Would consumers be willing to purchase such products? It is essential to advocate for bold and innovative research into genetically edited animals, with a focus on safety, careful promotion, and strict regulatory oversight to align with long-term goals and public acceptance. Continued advancements in this technology and its underlying mechanisms promise to improve poultry products and genetically modified livestock. Overall, CRISPR-Cas9 technology offers a promising pathway for advancing livestock breeding practices, with opportunities for genetic improvement, enhanced disease resistance, and greater productivity.

Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics.

Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the Tharparkar cattle breed using genomics tools combined with machine learning (ML) techniques. By leveraging data from the Bovine SNP 50K chip, we developed a breed-specific panel of single nucleotide polymorphisms (SNPs) for Tharparkar cattle and integrated data from seven other Indian cattle populations to enhance panel robustness. Genome-wide association studies (GWAS) and principal component analysis were employed to identify 500 SNPs, which were then refined using ML models-AdaBoost, bagging tree, gradient boosting machines, and random forest-to determine the minimal number of SNPs needed for accurate breed identification. Panels of 23 and 48 SNPs achieved accuracy rates of 95.2-98.4%. Importantly, the identified SNPs were associated with key productive and adaptive traits, thus attesting to the value and potentials of digital transformation in livestock genomics. The ML-aided ultra-low-density SNP panel approach reported here not only facilitates breed identification but also contributes to preserving genetic diversity and guiding future breeding programs.

Unravelling the impact of epigenetic mechanisms on offspring growth, production, reproduction and disease susceptibility.

Epigenetic mechanisms, such as DNA methylation, histone modifications and non-coding RNA molecules, play a critical role in gene expression and regulation in livestock species, influencing development, reproduction and disease resistance. DNA methylation patterns silence gene expression by blocking transcription factor binding, while histone modifications alter chromatin structure and affect DNA accessibility. Livestock-specific histone modifications contribute to gene expression and genome stability. Non-coding RNAs, including miRNAs, piRNAs, siRNAs, snoRNAs, lncRNAs and circRNAs, regulate gene expression post-transcriptionally. Transgenerational epigenetic inheritance occurs in livestock, with environmental factors impacting epigenetic modifications and phenotypic traits across generations. Epigenetic regulation revealed significant effect on gene expression profiling that can be exploited for various targeted traits like muscle hypertrophy, puberty onset, growth, metabolism, disease resistance and milk production in livestock and poultry breeds. Epigenetic regulation of imprinted genes affects cattle growth and metabolism while epigenetic modifications play a role in disease resistance and mastitis in dairy cattle, as well as milk protein gene regulation during lactation. Nutri-epigenomics research also reveals the influence of maternal nutrition on offspring's epigenetic regulation of metabolic homeostasis in cattle, sheep, goat and poultry. Integrating cyto-genomics approaches enhances understanding of epigenetic mechanisms in livestock breeding, providing insights into chromosomal structure, rearrangements and their impact on gene regulation and phenotypic traits. This review presents potential research areas to enhance production potential and deepen our understanding of epigenetic changes in livestock, offering opportunities for genetic improvement, reproductive management, disease control and milk production in diverse livestock species.

Analysis of miRNAs in milk of four livestock species.

BACKGROUND: Milk is essential for mammalian nutrition because it provides vital nutrients for growth and development. Milk composition, which is influenced by genetic and environmental factors, supports lactation, a complex process crucial for milk production and quality. Recent research has focused on noncoding RNAs, particularly microRNAs (miRNAs), which are present in body fluids and regulate gene expression post-transcriptionally. This study comprehensively characterizes miRNAs in milk of four livestock species, namely Bubalus bubalis, Capra hircus, Equus asinus, and Ovis aries and identifies potential target genes. RESULTS: High-throughput sequencing of milk RNA resulted in distinct read counts across species: B. bubalis (8,790,441 reads), C. hircus (12,976,275 reads), E. asinus (9,385,067 reads), and O. aries (7,295,297 reads). E. asinus had the highest RNA mapping rate (94.6%) and O. aries the lowest (84.8%). A substantially greater proportion of miRNAs over other small RNAs was observed for the donkey milk sample (7.74%) compared to buffalo (0.87%), goat (1.57%), and sheep (1.12%). Shared miRNAs, which included miR-200a, miR-200b, miR-200c, and miR-23a among others, showed varying expression levels across species, confirmed by qPCR analysis. Functional annotation of predicted miRNA target genes highlighted diverse roles, with an enrichment in functions linked to metabolism and immunity. Pathway analysis identified immune response pathways as significant, with several miRNAs targeting specific genes across species, suggesting their regulatory function in milk. CONCLUSIONS: Both conserved and species-specific miRNAs were detected in milk of the investigated species. The identified target genes of these miRNAs have important roles in neonatal development, adaptation, growth, and immune response. Furthermore, they influence milk and meat production traits in livestock.

Progress on deep learning in genomics.

With the rapid growth of data driven by high-throughput sequencing technologies, genomics has entered an era characterized by big data, which presents significant challenges for traditional bioinformatics methods in handling complex data patterns. At this critical juncture of technological progress, deep learning-an advanced artificial intelligence technology-offers powerful capabilities for data analysis and pattern recognition, revitalizing genomic research. In this review, we focus on four major deep learning models: Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), and Generative Adversarial Network(GAN). We outline their core principles and provide a comprehensive review of their applications in DNA, RNA, and protein research over the past five years. Additionally, we also explore the use of deep learning in livestock genomics, highlighting its potential benefits and challenges in genetic trait analysis, disease prevention, and genetic enhancement. By delivering a thorough analysis, we aim to enhance precision and efficiency in genomic research through deep learning and offer a framework for developing and applying livestock genomic strategies, thereby advancing precision livestock farming and genetic breeding technologies.

Ruminant livestock TR V(D)J genes and CDR3 repertoire.

Ruminant livestock exhibit certain immune characteristics that make them valuable models for studying T cell receptor diversity and immune responses. This resistance is attributed to their well-developed immune system, comprising both innate and adaptive components. In this review, we delve into the intricate workings of the immune system of ruminant livestock, focusing on innate immunity and adaptive immunity. Specifically, we discuss the TR V(D)J genes (including TRB, TRG, and TRA/D chain) and the characteristics of the complementary determining region 3 (CDR3) repertoire in bovine and ovine species, shedding light on the diversity and functionality of the T-cell receptor(TCR) repertoire in these species. Understanding the distinct features of these germline genes and CDR3 repertoires is essential for unraveling the complexities of immune responses in ruminant livestock. Lastly, we outline future prospects in this field, emphasizing the importance of further research to enhance our understanding of ruminant livestock immunity and its potential applications in disease management, vaccine development, and breeding strategies.

Genetics and Genomics 101.

Genetic mutations, both favorable and unfavorable, are the raw material for improvement in livestock populations. The random inheritance of these mutations is essential for generating progenies with genetic potential greater than their parents. These mutations can act either in a simple manner, such that a single alteration disrupts phenotype, or in a complex manner where hundreds or thousands of mutations of small effect create a continuous distribution of phenotypes. Selection tools leverage phenotypic records, pedigrees, and genomics to estimate the genetic potential of individual animals. This more accurate accounting of genetic potential has generated enormous gains in livestock populations.

Research Progress on the Role of M6A in Regulating Economic Traits in Livestock.

Reversible regulation of N6-methyladenosine (m6A) methylation of eukaryotic RNA via methyltransferases is an important epigenetic event affecting RNA metabolism. As such, m6A methylation plays crucial roles in regulating animal growth, development, reproduction, and disease progression. Herein, we review the latest research advancements in m6A methylation modifications and discuss regulatory aspects in the context of growth, development, and reproductive traits of livestock. New insights are highlighted and perspectives for the study of m6A methylation modifications in shaping economically important traits are discussed.

The Benefit of a National Genomic Testing Scheme.

Although a significant cost, genotyping an entire population offers many benefits, many of which can reduce the workload and effort in decision-making on farm. As well as providing more accurate predictions of the genetic merit of individuals (and by extension their expected performance), national genotyping strategies enable complete traceability from the cradle to the grave as well as parentage discovery. The information available per animal aids more informed breeding and management decisions, including mating advice, and determining the optimal role and eventual fate of each animal.

genomeprofile: Unveiling the genomic profile for livestock breeding through comprehensive SNP array-based genotyping.

In livestock breeding, single nucleotide polymorphism arrays have become a cornerstone of modern livestock breeding. SNP arrays facilitate the identification of genetic markers linked to economically important traits and provide a powerful tool for predicting breeding values. However, conventional breeding programs often overlook additional genomic features contained in the SNP array data that can provide valuable insights into the genetic diversity, copy number variation, inbreeding levels and potential challenges in breeding lines. Here we present genomeprofile, a tool using SNP array-based genomic data, offering a comprehensive profile of breeding animals including the identification of copy number variants and runs of homozygosity, and screening for aneuploidy. By integrating these features into the breeding landscape, genomeprofile enables a more comprehensive picture of genomic variation, ultimately enhancing precision breeding strategies. To illustrate the practicality and efficacy of genomeprofile, we applied the tool to a dataset of four pig breeding lines. The genomeprofile tool is a user-friendly tool that processes genotype data in finalreport or plink ped format efficiently into useful output. The output contains copy number variations, runs of homozygosity, selection signatures, aneuploidy and inbreeding per individual and across populations. This allows breeding companies and researchers to identify unique individuals or regions in the genome of interest based on routinely collected data.

Evaluation of heritability partitioning approaches in livestock populations.

BACKGROUND: Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium. RESULTS: Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively). CONCLUSIONS: Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

Phenomics in Livestock Research: Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on a Global Scale.

Bottlenecks in moving genomics to real-life applications also include phenomics. This is true not only for genomics medicine and public health genomics but also in ecology and livestock phenomics. This expert narrative review explores the intricate relationship between genetic makeup and observable phenotypic traits across various biological levels in the context of livestock research. We unpack and emphasize the significance of precise phenotypic data in selective breeding outcomes and examine the multifaceted applications of phenomics, ranging from improvement to assessing welfare, reproductive traits, and environmental adaptation in livestock. As phenotypic traits exhibit strong correlations, their measurement alongside specific biological outcomes provides insights into performance, overall health, and clinical endpoints like morbidity and disease. In addition, automated assessment of livestock holds potential for monitoring the dynamic phenotypic traits across various species, facilitating a deeper comprehension of how they adapt to their environment and attendant stressors. A key challenge in genetic improvement in livestock is predicting individuals with optimal fitness without direct measurement. Temporal predictions from unmanned aerial systems can surpass genomic predictions, offering in-depth data on livestock. In the near future, digital phenotyping and digital biomarkers may further unravel the genetic intricacies of stress tolerance, adaptation and welfare aspects of animals enabling the selection of climate-resilient and productive livestock. This expert review thus delves into challenges associated with phenotyping and discusses technological advancements shaping the future of biological research concerning livestock.

Selection signatures for high altitude adaptation in livestock: A review.

High altitude adapted livestock species (cattle, yak, goat, sheep, and horse) has critical role in the human socioeconomic sphere and acts as good source of animal source products including milk, meat, and leather, among other things. These species sustain production and reproduction even in harsh environments on account of adaptation resulting from continued evolution of beneficial traits. Selection pressure leads to various adaptive strategies in livestock whose footprints are evident at the different genomic sites as the "Selection Signature". Scrutiny of these signatures provides us crucial insight into the evolutionary process and domestication of livestock adapted to diverse climatic conditions. These signatures have the potential to change the sphere of animal breeding and further usher the selection programmes in right direction. Technological revolution and recent strides made in genomic studies has opened the routes for the identification of selection signatures. Numerous statistical approaches and bioinformatics tools have been developed to detect the selection signature. Consequently, studies across years have identified candidate genes under selection region found associated with numerous traits which have a say in adaptation to high-altitude environment. This makes it pertinent to have a better understanding about the selection signature, the ways to identify and how to utilize them for betterment of livestock populations as well as farmers. This review takes a closer look into the general concept, various methodologies, and bioinformatics tools commonly employed in selection signature studies and summarize the results of recent selection signature studies related to high-altitude adaptation in various livestock species. This review will serve as an informative and useful insight for researchers and students in the field of animal breeding and evolutionary biology.

Potential negative effects of genomic selection.

Initial findings on genomic selection (GS) indicated substantial improvement for major traits, such as performance, and even successful selection for antagonistic traits. However, recent unofficial reports indicate an increased frequency of deterioration of secondary traits. This phenomenon may arise due to the mismatch between the accelerated selection process and resource allocation. Traits explicitly or implicitly accounted for by a selection index move toward the desired direction, whereas neglected traits change according to the genetic correlations with selected traits. Historically, the first stage of commercial genetic selection focused on production traits. After long-term selection, production traits improved, whereas fitness traits deteriorated, although this deterioration was partially compensated for by constantly improving management. Adding these fitness traits to the breeding objective and the used selection index also helped offset their decline while promoting long-term gains. Subsequently, the trend in observed fitness traits was a combination of a negative response due to genetic antagonism, positive response from inclusion in the selection index, and a positive effect of improving management. Under GS, the genetic trends accelerate, especially for well-recorded higher heritability traits, magnifying the negatively correlated responses for fitness traits. Then, the observed trend for fitness traits can become negative, especially because management modifications do not accelerate under GS. Additional deterioration can occur due to the rapid turnover of GS, as heritabilities for production traits can decline and the genetic antagonism between production and fitness traits can intensify. If the genetic parameters are not updated, the selection index will be inaccurate, and the intended gains will not occur. While the deterioration can accelerate for unrecorded or sparsely recorded fitness traits, GS can lead to an improvement for widely recorded fitness traits. In the context of GS, it is crucial to look for unexpected changes in relevant traits and take rapid steps to prevent further declines, especially in secondary traits. Changes can be anticipated by investigating the temporal dynamics of genetic parameters, especially genetic correlations. However, new methods are needed to estimate genetic parameters for the last generation with large amounts of genomic data.

Initial findings on genomic selection indicated substantial improvement for major traits such as growth or milk yield and even successful selection for secondary traits such as fertility or survival. However, recent unofficial reports indicate an increased frequency of problems in several secondary traits. This study looks at potential sources of those problems and mitigation strategies. Under selection initially carried out for production traits, production improved, but fertility (i.e., a secondary trait) declined, with the decline partially compensated for by improving management. Later, also because the observed deteriorations were becoming too strong, these traits became part of the breeding objectives, and used selection indexes were modified to include secondary traits, halting the deterioration. Under genomic selection, genetic gains accelerate, especially for higher heritability production traits, potentially magnifying the negative responses for secondary traits, and management modifications may not be fast enough to alleviate the decline. The responses can especially decline for unrecorded or sparsely recorded fitness traits. While the decline may be slow and hard to see, it may be serious in the long term and hard to reverse. Changes under genomic selection may be monitored by recalculating genetic parameters every generation. Secondary traits that become more antagonistic with production traits will likely deteriorate more and will need special attention.

The evolution of contemporary livestock species: Insights from mitochondrial genome.

The domestication of animals marks a pivotal moment in human history, profoundly influencing our demographic and cultural progress. This process has led to significant genetic, behavioral, and physical changes in livestock species compared to their wild ancestors. Understanding the evolutionary history and genetic diversity of livestock species is crucial, and mitochondrial DNA (mtDNA) has emerged as a robust marker for investigating molecular diversity in animals. Its highly conserved gene content across animal species, minimal duplications, absence of introns, and short intergenic regions make mtDNA analysis ideal for such studies. Mitochondrial DNA analysis has uncovered distinct cattle domestication events dating back to 8000 years BC in Southwestern Asia. The sequencing of water buffalo mtDNA in 2004 provided important insights into their domestication history. Caprine mtDNA analysis identified three haplogroups, indicating varied maternal origins. Sheep, domesticated 12,000 years ago, exhibit diverse mtDNA lineages, suggesting multiple domestication events. Ovine mtDNA studies revealed clades A, B, C, and a fourth lineage, group D. The origins of domestic pigs were traced to separate European and Asian events followed by interbreeding. In camels, mtDNA elucidated the phylogeographic structure and genetic differentiation between wild and domesticated species. Horses, domesticated around 3500 BC, show significant mtDNA variability, highlighting their diverse origins. Yaks exhibit unique adaptations for high-altitude environments, with mtDNA analysis providing insights into their adaptation. Chicken mtDNA studies supported a monophyletic origin from Southeast Asia's red jungle fowl, with evidence of multiple origins. This review explores livestock evolution and diversity through mtDNA studies, focusing on cattle, water buffalo, goat, sheep, pig, camel, horse, yak and chicken. It highlights mtDNA's significance in unraveling maternal lineages, genetic diversity, and domestication histories, concluding with insights into its potential application in improving livestock production and reproduction dynamics.

Genome editing: An insight into disease resistance, production efficiency, and biomedical applications in livestock.

One of the primary concerns for the survival of the human species is the growing demand for food brought on by an increasing global population. New developments in genome-editing technology present promising opportunities for the growth of wholesome and prolific farm animals. Genome editing in large animals is used for a variety of purposes, including biotechnology to improve food production, animal health, and pest management, as well as the development of animal models for fundamental research and biomedicine. Genome editing entails modifying genetic material by removing, adding, or manipulating particular DNA sequences from a particular locus in a way that does not happen naturally. The three primary genome editors are CRISPR/Cas 9, TALENs, and ZFNs. Each of these enzymes is capable of precisely severing nuclear DNA at a predetermined location. One of the most effective inventions is base editing, which enables single base conversions without the requirement for a DNA double-strand break (DSB). As reliable methods for precise genome editing in studies involving animals, cytosine and adenine base editing are now well-established. Effective zygote editing with both cytosine and adenine base editors (ABE) has resulted in the production of animal models. Both base editors produced comparable outcomes for the precise editing of point mutations in somatic cells, advancing the field of gene therapy. This review focused on the principles, methods, recent developments, outstanding applications, the advantages and disadvantages of ZFNs, TALENs, and CRISPR/Cas9 base editors, and prime editing in diverse lab and farm animals. Additionally, we address the methodologies that can be used for gene regulation, base editing, and epigenetic alterations, as well as the significance of genome editing in animal models to better reflect real disease. We also look at methods designed to increase the effectiveness and precision of gene editing tools. Genome editing in large animals is used for a variety of purposes, including biotechnology to improve food production, animal health, and pest management, as well as the development of animal models for fundamental research and biomedicine. This review is an overview of the existing knowledge of the principles, methods, recent developments, outstanding applications, the advantages and disadvantages of zinc finger nucleases (ZFNs), transcription-activator-like endonucleases (TALENs), and clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR/Cas 9), base editors and prime editing in diverse lab and farm animals, which will offer better and healthier products for the entire human race.

DeepOCR: A multi-species deep-learning framework for accurate identification of open chromatin regions in livestock.

A wealth of experimental evidence has suggested that open chromatin regions (OCRs) are involved in many critical biological activities, such as DNA replication, enhancer activity, and gene transcription. Accurately identifying OCRs in livestock species can provide critical insights into the distribution and characteristics of OCRs for disease treatment in livestock, thereby improving animal welfare. However, most current machine-learning methods for OCR prediction were originally designed for a limited number of model organisms, such as humans and some model organisms, and thus their performance on non-model organisms, specifically livestock, is often unsatisfactory. To bridge this gap, we propose DeepOCR, a lightweight depth-separable residual network model for predicting OCRs in livestock, including chicken, cattle, and sheep. DeepOCR integrates a single convolution layer and two improved residue structure blocks to extract and learn important features from the input DNA sequences. A fully connected layer was also employed to further process the extracted features and improve the robustness of the entire network. Our benchmarking experiments demonstrated superior prediction performance of DeepOCR compared to state-of-the-art approaches on testing datasets of the three species. The source code of DeepOCR is freely available for academic purposes at https://github.com/jasonzhao371/DeepOCR/. We anticipate DeepOCR servers as a practical and reliable computational tool for OCR-related studies in livestock species.

Progress on CRISPR/Cas9 system in the genetic improvement of livestock and poultry.

CRISPR/Cas9 gene editing technology, as a highly efficient genome editing method, has been extensively employed in the realm of animal husbandry for genetic improvement. With its remarkable efficiency and precision, this technology has revolutionized the field of animal husbandry. Currently, CRISPR/Cas9-based gene knockout, gene knock-in and gene modification techniques are widely employed to achieve precise enhancements in crucial production traits of livestock and poultry species. In this review, we summarize the operational principle and development history of CRISPR/Cas9 technology. Additionally, we highlight the research advancements utilizing this technology in muscle growth and development, fiber growth, milk quality composition, disease resistance breeding, and animal welfare within the livestock and poultry sectors. Our aim is to provide a more comprehensive understanding of the application of CRISPR/Cas9 technology in gene editing for livestock and poultry.