APPLICATION OF NETWORK BIOLOGY METHODS FOR THE ANALYSIS OF THE BIOLOGICAL BASIS OF THE IMMUNITY OF FARM ANIMALS
The object of the study of immunity is not only the genome, but the animal organism as a whole. The study is carried out at the level of the metabolome and phenome with a deepening into genetic processes, incl. transcriptional regulation. In order to efficiently and informatively investigate the influence of harmful factors of the industrial environment on the body of an animal, it is necessary to comprehensively study the genetic effects of immunity and evaluate the preliminary results of our own and previously published studies of the immune status in biological databases. The research methods are statistical calculations in the structural and population genetics offarm animals, as well as molecular genetics, such as highly efficient parallel sequencing of complete DNA-seq genomes (or their target fragments related to the target and regulatory RNAseq genes), as well as scanning expression profiles using chips (heat maps). The resulting data is huge and complex for interpretation and visualization. The data processing is carried out by methods of statistical analysis, namely neural networks, multivariate and Bayesian analysis, as well as using linear models. To realize mathematical calculations, since we are talking about working with large amounts of data (“big data”), there are need to buy the time of yandex.cloud or azure.cloud (oracle.cloud computation) calculation servers, or construct own clustered server networks. The tasks of building national operating systems from software computing environments are also actual. The storage of statistical data requires the system development of systems and standards for the classification and codification of genomic data, for example, in Microsoft server or RedDB, PostreSQL, MySQL class environments. The data visualization is conveniently carried out by a number of systems based on the SAS and R software environments, but this may already be beyond the scope of this review.