Tharparkar, an indigenous milch breed from India, is known for producing high-quality milk and being able to thrive in dry and desert settings. This breed is very important for the livelihoods of rural communities. This work aimed to assess genetic diversity using double digest Restriction site-Associated DNA (ddRAD) sequencing and to identify breed-specific single nucleotide polymorphisms (SNPs) for the creation of an ultra-low-density SNP panel derived from five significant indigenous breeds of India. We genotyped 48 animals from the Livestock Research Station (LRS) in Beechwal under RAJUVAS, Bikaner (Rajasthan). After careful filtering, we got 121,554 high-quality biallelic SNPs. The population exhibited an average minor allele frequency of 0.249 ± 0.117, with the mean expected and observed heterozygosity values of 0.347 ± 0.103 and 0.291 ± 0.078, respectively. The genomic inbreeding coefficients calculated by several methodologies (FHOM, FUNI, FGRM and FROH) varied from 0.106 to 0.125, signifying a low to moderate degree of inbreeding. The prevalence of short runs of homozygosity (0–2 Mb) indicates minimal recent inbreeding, with inbreeding mostly resulting from ancient common ancestry, perhaps due to genetic drift or founder effects. The effective population size in recent generations was approximated at 82.6. To find informative SNPs that are distinctive to each breed, we used pre-selection statistics using the TRES (Toolbox for Ranking and Evaluation of SNPs) tool. Then, we used the MAF-LD approach on a reference dataset of 260 individuals from five different Indian cow breeds. This method found 309 SNPs in 260 individuals from the reference set and 77 unique SNPs that were only found in Tharparkar. These SNPs could differentiate Tharparkar apart from other breeds in both the reference and validation datasets. These results give us important information about the genetic diversity of Tharparkar cattle, show how useful ddRAD sequencing is for detecting breed-specific SNPs and stress the necessity for targeted conservation initiatives to ensure the breed’s sustainability.

Telomeres are protective DNA–protein structures at chromosome ends that play a crucial role in maintaining genome stability and regulating cellular aging. Their progressive shortening during cell division is counteracted by telomerase, a ribonucleoprotein enzyme composed of a catalytic subunit namely, telomerase reverse transcriptase catalytic subunit (TERT) and an RNA template i.e., telomerase RNA component (TERC), which restores telomeric repeats. Telomerase activity varies across species and is closely associated with differences in longevity. This review synthesizes current knowledge on telomere biology across diverse taxa, focusing on evolutionary selection patterns, age-related gene expression, and key regulatory mechanisms. By integrating insights from comparative genomics, functional studies, and computational approaches, it highlights both conserved and species-specific factors influencing lifespan and age-related processes. The review also discusses emerging computational frameworks for predicting telomere dynamics and outlines their potential applications in telomere-based therapies, livestock improvement, and conservation biology.

Rathi cattle, an indigenous Bos indicus breed of north-western India, represent a valuable genetic resource due to their adaptation to arid environments, heat tolerance and dairy potential. However, genomic information on this breed remains limited. This study provides the first double-digest restriction-site associated DNA sequencing (ddRAD) based genome-wide assessment of Rathi cattle using a large sample size. A total of 96 animals were genotyped, generating 78,193 high-quality SNPs with 96.52

Red Sindhi cattle, a distinguished dairy breed from India, are famous for their resilience in tropical climates and exceptional milk yield. This study utilized double digest restriction site-associated DNA sequencing (ddRADseq) across 96 individuals to explore genome-wide diversity and uncover signatures of selection. The analysis revealed a high proportion of polymorphic SNPs (0.956), moderate nucleotide diversity (π = 0.215 ± 0.114), and a low minor allele frequency (MAF = 0.149 ± 0.128). The analysis of Red Sindhi data showed a steep decline in effective population size (Ne) from 2387 to 125.9 over 13 generations, implying potential bottlenecks and underscoring the urgency of conservation efforts. Employing Tajima’s D, composite likelihood ratio (CLR), integrated haplotype score (iHS), and runs of homozygosity (ROH) methods, we identified 490 genomic regions under positive selection, encompassing 1282 genes and aligning with 574 quantitative trait loci (QTLs). Functional annotations highlighted several genes linked to reproduction (RHOU, MND1), production (DOK6, NPFFR2), immune response (BOLA-DYA and BOLA-DMB), and environmental adaptation (HSPA14, NOD2, GCLC, and RPS19BP1). Several MHC class II genes under selection pressure indicate robust immune competence, while stress-response genes supported Red Sindhi’s remarkable tolerance to extreme heat. These findings show the breed’s strong adaptability and disease resilience, underlining its importance as a valuable genetic resource for improving livestock in challenging environments.

Animal breeding has undergone profound transformations from its origins in phenotypic observation to the integration of genomic and machine learning techniques. This review paper explores the progression of livestock breeding, tracing its roots to the domestication of animals during the Neolithic Revolution. Gregor Mendel's foundational work with pea plants established key principles of Mendelian genetics, which initially focused on discrete qualitative traits. However, the advancement of quantitative genetics has shifted the focus to continuous traits, such as body weight and milk yield, which are influenced by multiple genes. QTL mapping revolutionized breeding by shifting from phenotype- to genotype-based selection, enhancing accuracy through genomic predictions like GEBV under GBLUP. The strongest QTL associations on chromosome 18 linked local GEBV with FUK and DDX19B expression. In recent years, machine learning and artificial intelligence have transformed genomic prediction into livestock breeding by efficiently handling high-dimensional data and capturing complex genetic relationships. Notably, a deployed deep learning model achieved an average correlation of up to 0.643 between actual and predicted values. This review highlights the integration of machine learning approaches in animal breeding, showcasing advancements in milk and meat production, and the improvement of disease management through multi-omics strategies. The paper underscores the shift towards innovative methods and their impact on advancing animal breeding practices, offering insights into prospects for enhancing productivity, health, and welfare in livestock.

Goats are vital to the rural economy of India, contributing significantly to livelihoods, nutrition, and agricultural sustainability. With a population of 148.88 million, India holds the world’s largest goat population, comprising 41 recognized indigenous breeds. These goats provide milk, meat, and fiber, particularly in marginal environments. The genomic advancements in goat research have revolutionized the understanding of genetic diversity, adaptation, and trait improvement. Whole-genome sequencing (WGS), single nucleotide polymorphism (SNP) arrays and transcriptomics have unveiled genetic markers associated with production, disease resistance, and reproductive traits. Genomic tools such as the Illumina Goat SNP50K BeadChip and high-throughput sequencing technologies have facilitated the identification of selection signatures and quantitative trait loci (QTL), influencing economically important traits like milk yield, meat quality, and prolificacy. Notably, genes such as DGAT1, GHR, BMPR1B, and HSP70 have been linked to production efficiency, reproductive performance, and climate resilience. Genome-wide association studies (GWAS) and genomic selection (GS) have enabled precision breeding, enhancing genetic gains and reducing inbreeding risks. The application of RNA sequencing has provided insights into gene expression patterns governing lactation, growth, and reproductive efficiency. Epigenomic studies, focusing on DNA methylation and histone modifications, have highlighted regulatory mechanisms underpinning prolificacy and muscle development. Conservation genomics has played a pivotal role in safeguarding native breeds by assessing genetic diversity and mitigating inbreeding depression. Indicine goat breeds, such as Jamunapari, Beetal, Barbari, and Black Bengal, exhibit unique genetic adaptations to diverse agro-climatic conditions, emphasizing the need for their conservation. Emerging technologies, including CRISPR-Cas9 gene editing, hold promise for precision breeding to enhance productivity and disease resistance. Integrating genomics with artificial intelligence (AI) and big data analytics is poised to revolutionize goat breeding and management. Future efforts should focus on expanding genomic databases, developing breed-specific reference genomes, and promoting genomic literacy among farmers to ensure sustainable goat production and improve rural livelihoods in India.

This study aims to comprehensively explore genome-wide selective processes influencing reproductive traits across six cattle breeds by employing different statistical methods. Reproductive efficiency is crucial for livestock productivity, as it directly influences the number of offspring and, consequently, the availability of animals for production. Early reproductive development and high fertility in herds boost selection intensity, driving faster genetic gains. This efficiency underpins the sustainability and profitability of livestock systems. To identify genomic signatures related to these traits, this study utilises genotyping technologies, including the Illumina BovineSNP50 Bead Chip and GGP Bos indicus 70k array. For this work, we used four summary statistics, including two intra-population statistics (Tajima's D and iHS), and two inter-population statistics (Rsb and XP-EHH). After identifying the key locations for selection, the NCBI database and the Cattle QTL database were utilised for annotation. The genes CACNA1H, KCNIP4, GDF9, SLC4A4, DHX57, EIF2AK3 and ME3 have been demonstrated to be under positive selection in Gir cattle. These are associated with characteristics such as udder cleft, age at puberty, sperm counts, sperm motility, sperm acrosome integrity rate, sperm motility per conception, sperm counts, conception rate, etc. Two genes, ENTHD1 and PRDM16 found on chromosomes 5 and 16, respectively, have been shared by Tharparkar and Gir which were undergoing positive selection. The ENTHD1 gene is linked to reproductive traits such as calving ease and stillbirth. Meanwhile, the PRDM16 gene is associated with characteristics like udder cleft, udder attachment, udder depth and udder height. The genes RXFP2, FRY, ENTHD1, SREBF2, RNF10, NYAP2, VWF, PPP1R8, EYA3, BBX, and TRPM3 were consistently identified across multiple selection signature methods, highlighting them as strong candidates under intense selection pressure. This approach offers valuable insights into the genetic basis of variations in reproductive traits, facilitating informed selection processes and enhancing our understanding of evolutionary and domestication in diverse cattle breeds.

The Y-chromosome, a hallmark of male-specific inheritance, holds immense importance in domestic animal genetics. It harbors the Sex-determining Region Y (SRY) gene, which initiates testis development and male differentiation. Due to its strict paternal inheritance and minimal recombination outside the pseudoautosomal regions (PARs), the Y-chromosome is a stable and reliable marker for studying male lineage, phylogeography, and patrilineal evolution in domestic species. Despite these constraints, advances in sequencing technologies and molecular tools (like Y-STRs and Y-SNPs) have enabled in-depth exploration of Y-chromosomal diversity. A comparative overview of the Y-chromosome architecture is presented for major domestic species, including cattle, sheep, goats, pigs, horses, dogs, and poultry. The genes present on the Y-chromosome are of growing interest in breeding programs to enhance male reproductive performance and disease resistance. Understanding Y-chromosome diversity is also critical for conservation genetics, especially in preserving indigenous breeds and managing genetic bottlenecks. This review also highlights the importance of Y-chromosome analysis in breeding strategies, particularly when combined with advanced tools. Studying Y-chromosome diversity provides valuable insights into male-driven domestication events. With the ongoing development of high-resolution molecular techniques, the scope for Y-chromosome-based research in domestic animals is expanding rapidly, promising to improve both scientific understanding and practical outcomes in animal breeding and conservation.

Mastitis is a multi-etiological disease that significantly impacts milk production and reproductive efficiency. It is highly prevalent in dairy populations subjected to intensive selection for higher milk yield and where inbreeding is common. The issue is amplified by climate change and poor hygiene management, making disease control challenging. Key obstacles include antibiotic resistance, maximum residue levels, horizontal gene transfer, and limited success in breeding for resistance. Predictive genomics offers a promising solution for mastitis prevention by identifying genetic traits linked with susceptibility to mastitis. This review compiles the research and findings on genomics and its allied approaches, such as pan-genomics, epigenetics, proteomics, and transcriptomics, for diagnosing, understanding, and treating mastitis. In dairy production, artificial intelligence (AI), particularly deep learning (DL) techniques like convolutional neural networks (CNNs), has demonstrated significant potential to enhance milk production and improve farm profitability. It highlights the integration of advanced technologies like machine learning (ML), CRISPR, and pan-genomics to improve our knowledge of mastitis epidemiology, pathogen evolution, and the development of more effective diagnostic, preventive and therapeutic strategies for dairy herds. Genomic advancements provide critical insights into the complexities of mastitis, offering new avenues for understanding its dynamics. Integrating these findings with key predisposing factors can drive targeted prevention and more effective disease management.

The signature of selection is a crucial concept in evolutionary biology that refers to the pattern of genetic variation which arises in a population due to natural selection. In the context of climate adaptation, the signature of selection can reveal the genetic basis of adaptive traits that enable organisms to survive and thrive in changing environmental conditions. Breeds living in diverse agroecological zones exhibit genetic “footprints” within their genomes that mirror the influence of climate-induced selective pressures, subsequently impacting phenotypic variance. It is assumed that the genomes of animals residing in these regions have been altered through selection for various climatic adaptations. These regions are known as signatures of selection and can be identified using various summary statistics. We examined genotypic data from eight different cattle breeds (Gir, Hariana, Kankrej, Nelore, Ongole, Red Sindhi, Sahiwal, and Tharparkar) that are adapted to diverse regional climates. To identify selection signature regions in this investigation, we used four intra-population statistics: Tajima’s D , CLR, iHS, and ROH. In this study, we utilized Bovine 50 K chip data and four genome scan techniques to assess the genetic regions of positive selection for high-temperature adaptation. We have also performed a genome-wide investigation of genetic diversity, inbreeding, and effective population size in our target dataset. We identified potential regions for selection that are likely to be caused by adverse climatic conditions. We observed many adaptation genes in several potential selection signature areas. These include genes like HSPB2 , HSPB3 , HSP20 , HSP90AB1 , HSF4 , HSPA1B , CLPB , GAP43 , MITF , and MCHR1 which have been reported in the cattle populations that live in varied climatic regions. The findings demonstrated that genes involved in disease resistance and thermotolerance were subjected to intense selection. The findings have implications for marker-assisted breeding, understanding the genetic landscape of climate-induced adaptation, putting breeding and conservation programs into action.

From an economic standpoint, reproductive characteristics are fundamental for sustainable production, particularly for monotocous livestock like cattle. A longer inter-calving interval is indicative of low reproductive capacity. This issue changes the dynamics of current and future lactations since it necessitates more inseminations, veterinary care, and hormone interventions. Various reproductive phenotypes, including ovulation, mating, fertility, pregnancy, embryonic growth, and calving-related traits, are observed in dairy cattle, and these traits have been associated with several QTLs. Calving ease, age at puberty, scrotal circumference, and inseminations per conception have been associated with 4437, 10623, 10498, and 2476 Quantitative Trait Loci (QTLs), respectively. This data offers valuable insights into enhancing and comprehending reproductive traits in livestock breeding. Studying QTLs associated with reproductive traits has far-reaching implications across various fields, from agriculture and animal husbandry to human health, evolutionary biology, and conservation. It provides the foundation for informed breeding practices, advances in biotechnology, and a deeper understanding of the genetic underpinnings of reproduction.

Mammalian X and Y chromosomes independently evolved from various autosomes approximately 300 million years ago (MYA). To fully understand the relationship between genomic composition and phenotypic diversity arising due to the course of evolution, we have scanned regions of selection signatures on the X chromosome in different cattle breeds. In this study, we have prepared the datasets of 184 individuals of different cattle breeds and explored the complete X chromosome by utilizing four within-population and two between-population methods. There were 23, 25, 30, 17, 17, and 12 outlier regions identified in Tajima’s D, CLR, iHS, ROH, F ST , and XP-EHH. Bioinformatics analysis showed that these regions harbor important candidate genes like AKAP4 for reproduction in Brown Swiss, MBTS2 for production traits in Brown Swiss and Guernsey, CXCR3 and CITED1 for health traits in Jersey and Nelore, and BMX and CD40LG for regulation of X chromosome inactivation in Nelore and Gir. We identified genes shared among multiple methods, such as TRNAC-GCA and IL1RAPL1 , which appeared in Tajima’s D, ROH, and iHS analyses. The gene TRNAW-CCA was found in ROH, CLR and iHS analyses. The X chromosome exhibits a distinctive interaction between demographic factors and genetic variations, and these findings may provide new insight into the X-linked selection in different cattle breeds.

This study seeks a comprehensive exploration of genome-wide selective processes impacting morphometric traits across diverse cattle breeds, utilizing an array of statistical methods. Morphometric traits, encompassing both qualitative and quantitative variables, play a pivotal role in characterizing and selecting livestock breeds based on their external appearance, size, and physical attributes. While qualitative traits, such as color, horn structure, and coat type, contribute to adaptive features and breed identification, quantitative traits like body weight and conformation measurements bear a closer correlation with production characteristics. This study employs advanced genotyping technologies, including the Illumina BovineSNP50 Bead Chip and next-generation sequencing methods like Reduced Representation sequencing, to identify genomic signatures associated with these traits. We applied four intra-population methods to find evidence of selection, such as Tajima’s D, CLR, iHS, and ROH. We found a total of 40 genes under the selection signature, that were associated with morphometric traits in five cattle breeds (Kankrej, Tharparkar, Nelore, Sahiwal, and Gir). Crucial genes such as ADIPDQ, DPP6, INSIG1, SLC35D2 in Kankrej, LPL, ATP6V1B2, CDC14B in Tharparkar, HPSE2, PLAG1 in Nelore, PCSK1, PRKD1 in Sahiwal, and GNAQ, HPCAL1 in Gir were identified in our study. This approach provides valuable insights into the genetic basis of variations in body weight and conformation traits, facilitating informed selection processes and offering a deeper understanding of the evolutionary and domestication processes in diverse cattle breeds.

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.

In the present study, the population genomic data of different cattle breeds were explored to decipher the genomic regions affected due to selective events and reflected in the productive, reproductive, thermo-tolerance, and health-related traits. To find out these genomic deviations due to selective sweeps, four statistical tools (Tajima’s D, CLR, ROH, and iHS) were used on four indigenous cattle breeds. Several candidate genes were found to be related to milk production (ADARB, WDR70, and CA8), reproductive (PARN, FAM134B2, and ZBTB20), and health-related traits (SP110, CXCL2, CLXCL3, CXCL5, IRF8, and MYOM1). Our findings serve as a foundation for identifying selective sweeps responsible for the genetic variation of traits. These sweeps could hold functional significance for multiple cattle breeds across various subcontinents. However, to enhance the robustness of the results, further studies with high-density arrays or whole-genome sequencing, employing greater resolution and larger sample sizes, are warranted.

Balancing selection is the process of selection that preserves various alleles within a population. Studying the areas undergoing balancing selection is essential, because it preserves genetic diversity in a population. Finding genes that exhibit signs of balancing selection during the domestication of cattle is the goal of this study. To identify regions where polymorphism has persisted in the cattle population for millions of years, we examined the genome of cattle. In this study, we used bovine SNP 50 k data to conduct a detailed genome-wide assessment of selection signatures for balancing selection. We have included the genotyped data from 427 animals, including five taurines, two crossbreds, and eight Indian cattle breeds. For this study, we employed Tajima's D approach to identify signature regions undergoing balancing selection. Using the NCBI database, PANTHER 17.0, and CattleQTL database, the annotation was carried out after finding the relevant areas under balancing selection. The number of genomic regions undergoing balancing selection in Ayrshire, Brown-Swiss, Frieswal, Gir, Guernsey, Hariana, Holstein Friesian, Jersey, Kankrej, Nelore, Ongole, Red Sindhi, Sahiwal, Tharparkar, and Vrindavani was 11, 13, 13, 19, 18, 11, 17, 14, 14, 12, 10, 12, 13, 13, and 11, respectively. We have observed multiple immune system-related genes going through balancing selection, including KIT, NFATC2, GBP4, LRRC32, SYT7, RAG1, RAG2, LOC513659, and ZBTB17. In our study, we found that the majority of the immune-related genes and a few genes associated with growth, reproduction, production, and adaptation are undergoing balancing selection.

The Sahiwal cattle breed is the best indigenous dairy cattle breed, and it plays a pivotal role in the Indian dairy industry. This is due to its exceptional milk-producing potential, adaptability to local tropical conditions, and its resilience to ticks and diseases. The study aimed to identify selective sweeps and estimate intrapopulation genetic diversity parameters in Sahiwal cattle using ddRAD sequencing-based genotyping data from 82 individuals. After applying filtering criteria, 78,193 high-quality SNPs remained for further analysis. The population exhibited an average minor allele frequency of 0.221 +/- 0.119. Genetic diversity metrics, including observed (0.597 +/- 0.196) and expected heterozygosity (0.433 +/- 0.096), nucleotide diversity (0.327 +/- 0.114), the proportion of polymorphic SNPs (0.726), and allelic richness (1.323 +/- 0.134), indicated ample genomic diversity within the breed. Furthermore, an effective population size of 74 was observed in the most recent generation. The overall mean linkage disequilibrium (r2) for pairwise SNPs was 0.269 +/- 0.057. Moreover, a greater proportion of short Runs of Homozygosity (ROH) segments were observed suggesting that there may be low levels of recent inbreeding in this population. The genomic inbreeding coefficients, computed using different inbreeding estimates (FHOM, FUNI, FROH, and FGROM), ranged from -0.0289 to 0.0725. Subsequently, we found 146 regions undergoing selective sweeps using five distinct statistical tests: Tajima's D, CLR, |iHS|, |iHH12|, and ROH. These regions, located in non-overlapping 500 kb windows, were mapped and revealed various protein-coding genes associated with enhanced immune systems and disease resistance (IFNL3, IRF8, BLK), as well as production traits (NRXN1, PLCE1, GHR). Notably, we identified interleukin 2 (IL2) on Chr17: 35217075-35223276 as a gene linked to tick resistance and uncovered a cluster of genes (HSPA8, UBASH3B, ADAMTS18, CRTAM) associated with heat stress. These findings indicate the evolutionary impact of natural and artificial selection on the environmental adaptation of the Sahiwal cattle population.

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.

Oestrus is defined as a period when a female animal exhibits characteristic sexual behaviour in the presence of a mature male. Oestrous manifestation in dairy animals is due to the oestrogen (E2) effect on the central nervous system (CNS). It is a critical issue to be considered on a priority basis. Inefficient oestrous detection reduces the fertility status of the herd. The primary and most reliable indicator of oestrus is standing to be mounted by a bull or another female herd mate, signalling receptivity and the pre-ovulatory state in dairy cattle. Oestrous detection is primarily a management challenge requiring skill and vigilance. To improve the efficiency of oestrous detection in dairy cattle, visual observation is one of the best methods if done three times a day; however, heat detection aids, if combined, give better results. However, techniques like using teaser bulls, tail painting, chin ball markers, ultrasound (USG) examination, hormonal analysis and examination of cervicovaginal mucus (CVM) improve oestrous detection efficiency. Moreover, the changes in production systems have reduced the expression of oestrous behaviour among cows, due to higher oestrogen (E2) metabolism. Therefore, automated systems, such as pedometers, accelerometers and acoustic sensors like infrared thermography (IRT) and image processing, have significantly enhanced reproductive performance by facilitating oestrous detection and optimizing insemination schedules. From this review, we would conclude that oestrous detection alone contributes considerably to the reproductive status of the herd; therefore, applying different methods of oestrous detection reduces the incidence of missed oestrus and improves the fertility status of the herd.

Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.