Systems Biology, Bioinformatics and Livestock Science

By Anupam Nath Jha, Sandeep Swargam and Indu Kumari

This book explores the intricate world of livestock sciences and production through the lens of systems biology. Offering a comprehensive exploration of both fundamental and advanced aspects, it unearths the potential of systems biology in the realm of livestock. The book presents 13 edited chapters on cutting-edge knowledge about systems biology and omics technology, showcasing genomics, transcriptomics, proteomics, metabolomics, and more. It illuminates the role of systems biology in livestock and disease management. Readers will learn about power of technologies that merge computational biology, nanobiotechnology, artificial intelligence, and single-cell sequencing. Each chapter is written by scientific experts and includes references for further reading.
The book covers 4 key themes:
Introduction to Systems Biology in Livestock Science: Uncover the foundation of integrating systems biology with omics data for animal scientists.
Multi-scale Modeling Techniques: Explore how multi-scale modeling is shaping the future of system biology.
Livestock Viral Diseases: Gain insights into how systems biology is revolutionizing our understanding of livestock viral diseases.
Single Cell RNA-Sequencing: Understand the potential of this advanced technique in studying livestock animals at a cellular level.
This book is a timely resource for students and researchers, offering a pathway to comprehend the crucial role systems biology plays in sustainable livestock production and management.

• Language: English
• Publisher: Bentham Science Publishers
• Release date: Nov 20, 2000
• ISBN: 9789815165616

Book preview

An Introduction to the Integration of Systems Biology and OMICS data for Animal Scientists

Sandeep Swargam¹, *, #, Indu Kumari², *, #

¹ Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Shahpur and Dharamshala, Kangra, Himachal Pradesh, India

² Indian Biological Data Centre, Regional Centre for Biotechnology, Faridabad, Haryana, India

Abstract

Systems biology integrates the data of all the omics studies and provides the avenues to understand the biology of an organism at higher levels like at tissue, organ or organism level. In the last decade, studies of genomics, transcriptomics, proteomics and metabolomics have been carried out. Only a limited amount of this big data has been analyzed, which is mainly focused on the genotype (single nucleotide polymorphism) level like minor allele frequency, copy number variation and structural variants. The analysis in transcriptomics is limited to differentially expressed genes and their ontology. Proteomics is focused on virulent factors, proteins involved in the disease progression and immunomodulation. However, in the case of livestock animals, there is a need to develop pipelines for the analysis of the omics data. With the integration of omics data into systems biology studies, there is a need to develop algorithms to carry out gene interaction and protein interaction studies and to build interaction networks. The pathway analysis of a system requires the well-defined interacting hub and edges of the protein system of an organism. Developing AI-ML models for drug discovery is required to target the pathogens of livestock animals. In the present era, the research is moving towards single-cell sequencing of the cells and tissues to explore the genetic heterogeneity in the micro-environment of the tissue and spatial biology of the tissue. This chapter will introduce the reader to different aspects of omics technology and its role in systems biology for better livestock management.

Keywords: Database, Genomics, Omics, Proteomics, System biology, Transcriptomics.

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* Corresponding Authors Sandeep Swargam and Indu Kumari: Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Shahpur and Dharamshala, Kangra, Himachal Pradesh, India; and Indian Biological Data Centre, Regional Centre for Biotechnology, Faridabad, Haryana, India; Emails: [e-mail]; [e-mail]

# Sandeep Swargam and Indu Kumari have equal contribution

INTRODUCTION

The ongoing era is the advancing next generation sequencing (NGS) which started from Sanger sequencing that was taken over by targeted and whole genome sequencing (WGS). At present, the scientific community has shifted to single-cell sequencing and spatial transcriptomics. This ever-increasing big data is obtained from the reductionist approach. Therefore, there is a need to integrate the latest information on biological systems which is present in several databases and metadatabases like NCBI, ENA, DDBJ and China National GeneBank DataBase (CNGBdb). There are several genome databases available for animals. And these available open data sources provide details of genomics, transcriptomics, proteomics, metabolomics, translatomics, function of enzyme and protein, interactome (protein-ligand, protein-protein, DNA-protein and RNA-protein interactions), drug-associated single nucleotide variants (SNVs) and gene ontologies. The integration of the available data of livestock is required, which is possible through the application of mathematical modelling and system biology approaches. It is the necessity of the present era as the human population is estimated to rise to 11.2 billion by 2100, as given by the Food and Agriculture Organisation of the United Nations (FAO) (https://population.un.org/wpp /Publications/Files/Key_Findings_WPP_2015.pdf). Population growth along with global climate change causes pressure on the global food system. This can be achieved by integrating different fields of omics to bring improved and better food from the lab to common people. To achieve this objective, we need to improve animal health and welfare through sustainable practices. The enhanced food quality can be achieved by improving breeding strategies by selecting the accurate genotype to access the phenotype in farmed terrestrial and aquatic animal species. There is a Functional Annotation of ANimal Genomes (FAANG) project ongoing to fulfil these objectives [1]. There is a need to start such projects in different parts of the world so that the quality of food and the reproductive ability of the livestock can be improved. There are studies that were carried out by single technique like WGS, targeted or whole exome sequencing (WES), targeted/whole transcriptomic sequencing, targeted/whole methylome sequencing (epigenetic changes), proteome profiling, and metabolomics. In addition to this, there are integrated studies which have tried to improve the meat, milk quality and health of the livestock. However, the integration of the genome into the phenome will help to develop some working models that can be applied by system biology for better livestock production.

The book has been divided into four sections which introduce the readers to system biology, its application in livestock and diseases, the amalgamation of various techniques with system biology and its future perspectives as well. In the present era, the experimental work is moving from genome-level sequencing to single-cell sequencing. In experimental biology, we are trying to move towards a holistic approach by shifting towards single-cell sequencing and spatial tissue analysis. We will discuss all the chapters in brief in this chapter.

We will discuss briefly the contents of the book chapters, which include:

Modeling Approaches in Computational Biology, Including Silicon Cell Models, Physiome Project: A Global System Biology Network Initiative, Genome to Systems Biology in Livestock Management, Host-food-Gut Microbiome Interactions by Systematic approaches, Multi trait genetic evaluation of economic traits in ruminants, Role and Importance of Nano-biotechnology and System Biology for Livestock Science, Animal–Pathogen Interactions, Proteomics in animal health and diseases: An update, Machine learning-based AI approaches to veterinary drug discovery for system-wide prediction of the drugable proteome, System biology-based understanding of gut microbiome role in cattle production and health, Application and Development of CRISPR-cas9 based genome engineering in farm animals.

SECTION A: INTRODUCTION TO SYSTEMS BIOLOGY AND ITS PERSPECTIVES

Chapter 1. Systems Biology and Bioinformatics for Animal Scientists

System biology is a holistic approach to correlating the individual livestock’s omics levels of different units involved in the central dogma process of the cell. This integration of genome to phenome and functional annotation of the genetic units may help the scientific community to bring better quality features of the livestock to the farm end. This integration process starts from genome-epigenome (methylome)- transcriptome- metabolome- proteome- phenotype or disease variaome [2]. Different omics approaches employed in the current research are listed in Table 1.

Table 1 Different ‘omic’ levels used in systems biology analyses.

Systems biology integrates the big data of all these omic techniques which help the veterinary scientist to identify the causal genes, pathways and networks, regulatory genes, predictive markers for complex traits and diagnostic markers for several diseases of livestock. The integrated analysis comprising of the metabolome, metagenomic analysis of rumen microbiome and transcriptome analysis has unraveled the biological mechanism regulating the milk production at different biofluids and tissues under the influence of low-quality forage [3]. Such studies may provide novel strategies to improve the future products utilization and sustainable livestock yield. The implications of multi-omics approach in the livestock field may lead to the development of new breeds of milk and meat-producing animals which can be obtained by genome-wide association studies (GWAS) by focussing on the phenotypic traits of interest. These approaches and different studies have been discussed in the following chapters of the book.

Chapter 2. Application of Multi-Scale Modeling Techniques in System Biology

Mathematical modeling has been used to integrate the available omics data to develop nutrition models and to predict the adaptability of tissue in realistic settings. There are different types of models developed by system biology to study the pathways, gene interactions and networks. The biological reactions in system biology are usually modeled by chemical reaction networks (CRNs). BioCRNpyler is a recently developed tool whose script is in Python [4]. It can be used to build, manage, and explore different biochemical models based on biochemical study requirements and modeling questions. There is a mathematical model developed which can predict the feed intake and efficiency of cows in terms of beef quality. Cattle Value Discovery System beef cow (CVDSbc) tool id developed to predict these parameters in cows [5]. A 3D model of Mycoplasma genitalium (MG) cell has been created by the CellPACK suite. Mathematical modeling tools are advancing with time, and in the future, we will be able to integrate the available data to obtain the required outcome [6]. The mathematical models are already developed for some spectrum of research areas like host-pathogen interactions and immune system paradigms. Mechanistic models (MM) use quantitative data to define how a biological system will work under conditions and how its relationship will and yield change under some environmental conditions. Currently employed data-driven modeling methods namely, machine learning (ML) and deep learning (DL), use the features of the data to predict the outcome accurately, and the integration of MM and ML/DL parameters can be utilized for individual animals towards a knowledge-based precision livestock management system [7]. There is a platform to submit mathematical models, i.e., BioModels (https://www.ebi.ac.uk/biomodels/), established in 2005. A meeting regarding the mathematical modeling of biological entities was conducted in 2019 named COMBINE http://co.mbine.org/) 2019 meeting (http://co.mbine.org/events/COMBINE 2019) which has initiated the development of ModeleXchange. ModeleXchange is based on metadata for the discovery of system biology models. The components of these models will be obtained from several independent repositories like SynBioHub, Physiome Model Repository, JWS Online, ModelDB, BiGG, Open Source Brain, Center for Reproducible Biomedical Modeling (https://reproduciblebiomodels.org/) and V-Cell [8]. Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking, has been developed, which can be utilized to understand animal interactions [9]. The development of such platforms will be beneficial to devise strategies for better livestock management. The integration of computational data with mathematical modeling will be discussed in detail in this chapter with emphasis on the silicon cell model.

Chapter 3. The Perspective of Physiome Modelling in Systems Biology: New Horizon

The International Union of Physiological Sciences (IUPS) Physiome Project and the Virtual Physiological Human (VPH) initiative were established on the computational models that are developed by functional systems of mathematics and physics. It expresses the human body and its components in computable form and employs the gained knowledge in the clinical setup to bring it to the bedside [10]. The computational models have been developed for many organ systems like cardiovascular, respiratory, musculoskeletal, digestive and reproductive system. CellML and FieldML standards have been developed to ensure the generation of accurate and reproducible models that can be available to the modeling community. There is a freely available simulation software programme (OpenCOR) for running the computational models. There are various models developed which are available in the form of a webtool that can be utilized to discover the relevant models from the Physiome Model Repository (PMR) according to their scientific endeavors (Sarwar et al., 2019). In addition to the PMR, efforts are going on to develop cell physiome from the available big data in different cellular aspects like a number of components, in time, scale and characterization of physical properties of the cell [11]. There is a need to start an initiative towards the livestock physiome, which may improve the practices of livestock management.

SECTION B: SYSTEMS BIOLOGY TO STUDY LIVESTOCK AND RELATED DISEASES

Chapter 4. Systems Biology Approach in Fisheries Science

There are several omics studies that deal with different aspects of fish nutrition, reproduction and yield. There are some databases developed based on the genomic and transcriptomic data of diverse fish species. There is a single dedicated database for Danio rerio (zebra fish), i.e., Zebrafish Information Network (ZFIN) [12]. The catfish genome database, cBARBEL (catfish Breeder And Researcher Bioinformatics Entry Location), is the genomic database of Ictalurus spp. (http://catfishgenome.org) [13]. It provides BLAST search, EST contig visualization along with single nucleotide polymorphism (SNP) information, etc. FishOmics is a database based on barcode sequences, mitogenome sequences, microsatellite markers, hypoxia-responsive genes and karyology of fishes (http://mail.nbfgr.res.in/FisOmics/) [14]. The multi-omics data of Pisces is available at FishDB (http://fishdb.ihb.ac.cn). It encompasses the genomic data of 223 fish, transcriptome data of 201 fish, mitochondrial genome data of 5841 fish, gene sets of 88 fish and in addition to this miRNAs, piRNAs, lnc RNAs, untranslated regions (UTRs) and coding sequences (CDS) of fish [15]. The RNA-seq data of fish has been compiled in FishExp, which is a database as well as an analysis platform for expression data. The database has expression and alternative splicing data from 44 fishes. RNA-seq analysis output is visualized in gene, transcript and splicing levels (https://bioinfo.njau.edu.cn/fishExp) [16]. The first proteomic repository has been established for Labeo rohita. The ‘Rohu PeptideAtlas’ is a comprehensive database of the proteome of L. rohita, which has 6015 high confidence canonical proteins, 2.9M peptide spectrum match and ~150K peptides present in it, and it is accessible at the web address (https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/buildDetails?atlas_build _id=500) [17]. The studies are taking a plunge from WGS and gene expression studies to single-cell sequencing and single-cell transcriptome studies. There are studies that involved transcriptomic analysis at the single cell level of the immune system to unravel the evolution of the vertebrate immune cell types [18]. This is a glimpse of the omics studies that are going on in fisheries science; the details of this topic are discussed in the chapter.

Chapter 5. Systems Biology and Livestock Gut Microbiome

The Gut microbiome of livestock plays a vital role in its growth and product yield. It affects many phenotypic traits like feed efficiency, quality and yield of meat/milk, health and immunity. Current omics approaches are employed for in-depth study of the ruminal and gastrointestinal tract. The integrated omics studies have led to system biology that helps to understand the organism-level interactions within the cell and between the cells. However, in the present time, gut microbiome studies are mainly focusing on the identification and characterization of microbial diversity. However, there is a need to understand the functional aspect of these microbes and their interactions with the host. The currently existing models used in system biology can be employed to understand the functional aspect of these symbiotic relationships and may help to build a significant biological network [19]. Nutrigenomics is one of the fields which integrates the interactions between nutrition and metabolic changes in the cell and tissue under changing physiological conditions and varying life stages of livestock like pregnancy, lactation or postnatal growth. The integrative application of omics in the nutritional aspect of the livestock may help to decipher the link between feed and the immune system of the livestock yield [20]. Such studies which involve omics approaches along with system biology will be discussed in detail in this chapter.

Chapter 6. Omics in Livestock Animals: Improving Health, Well-Being, and Production

The omics data generated from livestock animals have been used to improve the health, nutrition, metabolism, physiology, production, quality of the products and interactions with the symbiotic microbes. Different fields are emerging in the research and development of livestock, like feedomics and nutrigenomics. Feedomics is the integration of all omics approaches, i.e., livestock genomics, epigenomics (methylome), transcriptomics, metabolomics, proteomics, translatomics, metagenomics, phenome, metatranscriptomics and single cell transcriptomics) for better yield in livestock [21]. PhenomeXcan is a tool which integrates genome and transcriptome to decipher the phenotype. This is developed for human data, however, there is a need to develop such platforms for livestock data as well [22] (Long et al., 2020). There are enough studies about genetic variations, but the selection of specific traits to improve the genetic constitution of the livestock is required. Epigenomic studies can be powerful tools to decipher the practices which produce benefits and may translate the developmental programming in the livestock [23]. The interactions between nutrients, bovine genotype and enteric biome in acidic conditions identified various quantitative trait loci for many metabolites like lactate, butyrate and host-microbe interactions [24]. In this way, system biology along with omics approaches has an impact to devise new dietary and production guidelines by the identification of key molecular markers for personalized decision-making in livestock [25]. The appli-

cation of omics approaches for the improvement of the health and product quality of livestock will be discussed in detail in this chapter.

Chapter 7. Livestock Viral Diseases and Insights into Systems Biology

A study has shown that the sequenced data of livestock contain some viral DNA derived from the virus at some stage of infection. 1471 WGS data of livestock (pigs, cattle, chickens and rabbits) was mined. From this data, different viruses were identified that have infected the sequenced livestock [26]. There was one database developed for one livestock viral disease, i.e., CIDA: A Proposed Data Base for Foot-and-Mouth Disease Virus (FMDV) that consists of WGS and internal ribosome entry site-related records [27]. There are Infectious Diseases of East African Livestock (IDEAL) project that has calf health-related records from 2007-2010, which contain clinical and epidemiological data, blood and tissue samples [28]. The metagenomic NGS analysis has been implemented to identify and characterize the existing and novel viral diversity in livestock animals. There is a need to understand the livestock virome and its interactions with the host and the virulence factors of the virus, which immunomodulate the immune system of the host [29]. These gaps in existing knowledge can be removed by the integration of the omics data of the virus and its analysis to understand the gene and protein network interactions at the organism levels. The United States Swine Pathogen Database has been developed by the integration of private clinical data and publicly available data of porcine reproductive and respiratory syndrome virus (PRRSV) [30]. Currently, there is a need to understand the survival strategy of livestock viruses, which can be deciphered by system biology. This chapter will discuss the various livestock viral diseases and how omics approches can be the savior against these disease causing organisms.

SECTION C: INTEGRATION OF RECENT AND DEVELOPING TECHNIQUES WITH SYSTEMS BIOLOGY FOR BETTER OUTCOME

Chapter 8. Proteomics in Livestock Health and Diseases

Proteomics studies include one-dimensional and two-dimensional gel electrophoresis, chromatography (liquid and gas) methods to sophisticated mass spectrometry like MALDI-MS, ESI-MS, LC-MS/MS, MALDI-TOF MS, etc. [31]. It provides an extensive characterization of whole or partially expressed proteins present in a tissue sample or livestock animal that may interact with the matrix or fluid of the organism. The proteomics analysis of postpartum dairy cows by liquid chromatography-MS showed the association of negative energy balance with the liver proteome in early lactation dairy cows [32]. There is the first study that has analyzed the exosomes obtained from the blood plasma of high tick resistance and low tick resistance cattle by MS technique to differentiate the effects of tick burden [33]. There were some initiatives a decade back at the international level for proteomic studies on livestock like COST (Cooperation in Science and Technology) action FA1002 – Farm Animal Proteomics (https://www.cost.eu/actions/FA1002/#tabs|Name:overview) in early 2011 that was at European level. Recently, there has been a European Joint Doctorate in Molecular Animal Nutrition (the Marie Curie MANNA PhD program MANNA, www.phd4manna.eu, 2018–2022) that is also a European initiative to encourage omics-based studies applied to animal nutrition [34]. There is a need to adopt proteomic tools in animal and veterinary sciences to develop a database for access to proteomic platforms and funding. This chapter will discuss proteomics techniques in detail and their application in livestock animals.

Chapter 9. Importance and Potential Applications of Nanobiotechnology and Systems Biology for Livestock Science

The ongoing advancement of nanotechnology has opened countless applications in the field of livestock production. It has paved unconventional and innovative ways to solve the present reproductive challenges of farm animals. There is an emergence of microbial antibiotic resistance in cattle and the increased use of antibiotics as growth promoters has alarmed the livestock sector. There is a need to devise novel, sensitive and effective antibiotics to fulfill the production demand of the livestock sector. To solve this problem, nanoparticles are one of the potential candidates for growth enhancement and antimicrobials [35]. There are several studies which have shown promising results of the application of nanoparticles in poultry and other areas. However, it is still in the advanced stage of its development, which may impact the environment and organism’s health. Therefore, it is necessary to assess the hazardous impact of the nanoproducts before implementing these on livestock animals [36]. The molecules obtained after the integration of nanoparticles with drugs (hormone/antibiotics), nutrients and biomolecules may exhibit novel physiochemical properties with better bioavailability, enhanced cellular uptake, sustained release and decreased toxicity with conventional forms [37]. Such applications of nanoparticles in the field of livestock management will be discussed in this chapter.

Chapter 10. Single Cell Sequencing in Livestock

The single cell sequencing involves the analysis of different cell populations at the gene expression level to understand the genetic heterogeneity within/between the cell and tissue microenvironment of the organism. It helps to understand the gene expression between normal, tumour, stress-induced cells and other conditions. Single-cell sequencing has been applied to unravel the microbes involved in fibre digestion, volatile fatty acid uptake and metabolism in the rumen [38]. RNAseq analysis has been implemented to understand spermatogenesis in fish. It was reported that various pathways of gamete synthesis, cell cycle, protein processing and mRNA surveillance were enriched in testicular germ cell types. The ribosomal pathway was enriched in testicular somatic cell types [39]. RNAseq analysis help to reveal the genetic signatures which are responsible for immune function and tissue remodeling in cow affected with endometritis [40]. RNAseq analysis can be applied in different aspects of livestock management. The cross tissue single cell transcriptomics has helped to understand the vital cell types and their importance in the nutrient absorption and metabolism of cattle [41]. A recent RNAseq study focused on ACE2 and TMPRSS2 on 11 non-model species, namely, dog, cat, lizard and hamster, which are pets; goat and rabbit that belong to the livestock category; pigeon and duck that belong to poultry; and tiger, pangolin and deer that belong to wildlife category. This study was carried out to know the host range of SARS-CoV-2 that needs further validation by experimental work. Different types of single-cell techniques and their application, as described above, will be discussed in detail in this chapter.

SECTION D: FUTURE ASPECTS OF SYSTEMS BIOLOGY IN LIVESTOCK SCIENCES

Chapter 11. AI-ML and Systems Biology for Drug Discovery in Livestock: its Applications and Future Prospect

It has been observed that studies related to the application of artificial intelligence (AI) and machine learning (ML) in farm animals have increased since 2016. These studies are mostly focused on the detection of animal behavior and recognition, which are mainly focused on pigs, cattle and poultry. However, most of the studies involving AI have been carried out on data collection, its processing, analysis and interpretation of the results in animal behavior, growth estimation, disease surveillance and environment monitoring [42]. Recently, a deep learning method was employed to monitor and classify the behavioural pattern of cow. This method was integrated with C3D (Convolutional 3D) network and ConvLSTM (Convolutional Long Short-Term Memory) to decipher the five activities of cows, i.e., walking, standing, feeding, exploring and grooming [43]. There is a possibility of the development of AI-based sensors which can be used to detect the quality of milk, poultry products, quality of water and quality of meat (fish/cattle). AI and ML can be applied to detect the disease at an early stage in cattle. However, there are limitations to this approach, like the accuracy and cost-effectiveness of the technology before it is brought into commercialization. These different aspects of AI-ML in the current time of livestock management will be discussed in this chapter.

Chapter 12. Genomics to Systems Biology in Livestock Management: its Applications and Future Prospect

There is a huge amount of genomic data for different livestock animals, which can be integrated to understand the gene interactions by system biology. There are different platforms and databases available for the genomic data of livestock, which are discussed in the below text. Functional annotation of animal genome (FAANG) consortium is developed for genome annotation and to understand the genotype-phenotype link in farm animals [44]. Animal-ImputeDB (http://gong lab.hzau.edu.cn/ Animal ImputeDB/) contains genomic data of 13 animal species for single nucleotide polymorphisms (SNP) analysis like genetic imputation and SNP search [45]. The estimation of missing genotypes from a reference panel is a genetic imputation process. It is used in genome-wide association studies. However, in the case of livestock animals, there is a lack of reference genome for most animals. The Animal Transcription Factor DataBase (AnimalTFDB) provides collated information about transcription factors and cofactors [46]. There is a comprehensive database for the variations of bovine named the Bovine Genome Variation Database (BGVD) (http://BovineGenome.org), which provides tools like gene search, genomics signature search, variation search, alignment search tool, genome browser and coordinate conversion tool [47]. The Animal QTLdb has evolved over time (://www.animalgenome.org/ QTLdb), which now contains 220401 QTL, eQTL and SNP data [48]. There is a database developed for expressed sequence tags of sheep and goat, namely, GoSh: a goat and sheep ESTs database: The GoSh database (http://www.itb.cnr.it/gosh/) from Ovis aries and Capra hircus [49]. The guiding document to conduct research in the field of animal genomics, Blueprint for USDA Efforts in Agricultural Animal Genomics 2008–2017, was published. Further, to develop an updated blueprint, the Agricultural Research Service (ARS) and the National Institute for Food and Agriculture (NIFA), in collaboration with the scientist of animal genomics, conducted a workshop titled Genome to Phenome: A USDA Blueprint for Improving Animal Production in November 2017. It helped to modulate the goals into broad categories in the previous blueprint [50]. Nematode.net (http://nematode.net) is a web portal which provides data, including NGS data of 56 nematode species and HelmCoP (Helminth Control and Prevention) provides data on helminth genomes [51]. Ruminant Genome Database (RGD; http://animal.nwsuaf.edu.cn/RGD) provides a tool for functional annotation and comparative genomics. It also has 78 genomes of ruminants, synteny alignment of 110 species (sheep, goat, cattle) and wild ungulates [52]. Rat Genome Database (RGD, https://rgd.mcw.edu) was developed in 1999, and now it has standardized genetic, genomic, phenotypic and disease-related data of 8 species [53]. The integrated omics data of domestic dog (Canis lupus familiaris) and wild canids have been compiled in iDog (https://ngdc.cncb.ac.cn/idog) and its recent version also includes 27 ancient dog’s data as well [54]. A comprehensive genetic database of sheep Ovis species that contains 82689498 variants is iSheep (https://ngdc.cncb.ac.cn/isheep) (CNCB-NGDC Members and Partners, 2022). We have compiled some of the genomic database resources of livestock animals. Further, the application of genomics and its correlation with system biology has been discussed in detail in this chapter.

Chapter 13. Applications and Future Perspectives of Computational Approaches in Livestock Animals

The chapters in this book have dealt with different aspects of omics technologies. The application of omics studies and its future perspectives need to be discussed in detail. There are many limitations in the current omics technologies, e.g., there is a lack of reference panels to carry out genomic or GWAS studies in livestock animals. When we discuss single cell transcriptomics, it is in its initial phase. There are databases for human gut microbiota like gut MEtaGenome Atlas (gutMEGA), gutMDisorder, GMrepo, and HumanMetagenomeDB. Recently, ADDAGMA: A database for domestic animal gut microbiome atlas has been released, which contains data for pig, horse, cattle, and chicken [55]. However, there are no well-defined cell or tissue atlas based on which different cell or tissue population analyses can be carried out. The application of AI-ML approaches is still in the infant stage, although there are studies on livestock data and behavior. However, there are no well-defined features to carry out AI-ML studies on the reproductive aspect of livestock animals. The integration of omics data with system biology is a promising tool, however there is a need to develop algorithms to analyze and integrate this data into significant outcomes. There is still a need for more studies in livestock Animal Sciences considering the product and disease based studies like human diseases. Standard protocols need to be developed for future perspective to tackle the targeted genes for the well being of livestock animals. The AI-ML models need to be advanced to know the exact relation of livestock-animal-microbial network biology to understand the proper dependent and independent biological systems and vice-versa.

CONCLUSION

This chapter has given a glimpse of the past, present and future perspectives of omics data. We have tried to give an account of the existing knowledge of the tools and algorithms employed for systems biology studies. There are no standardized reference data available to study all the domestic and commercial livestock. The clinical research has switched to single cell sequencing/transcriptomic studies for better treatment of the diseases. However, in the case of livestock, we are still struggling at the genomic, transcriptomic, and proteomic levels. There is a need to accelerate the research in livestock genomics. We have baseline data which can be integrated to carry out the integrated systems biology studies on the livestock animals. The gene interactions networks have elaborated the key genes which regulate the cellular pathways. By keeping the future perspectives in focus, we need to integrate omics data to unravel the important events that play a major role in the genome to phenome translation in livestock animals. In the future, if we are able to achieve this, then we can control specific quantitative traits in the livestock to enhance their products’ quality.

REFERENCES