Alternatives to animal experiments, based on in vitro methodologies, have been suggested and adopted in the last decades in order to completely substitute or to reduce animal numbers in in vivo assays. In this chapter we describe methods for establishment, maintenance, and characterization of primary goat mammary epithelial cell cultures (pgMECs) and possible applications for which the derived primary cell model can be used instead of in vivo experiments. The established cell lines were grown in vitro for several passages and remained hormone and immune responsive and capable of milk protein synthesis. Knowledge on goat mammary cells and their manipulation is applicable to different fields of research; for example, it could be used in basic research to study mammary development and lactation biology, in agriculture to enhance lactation yield and persistency or to produce milk with special characteristics, in biopharma to express recombinant proteins in goat milk, or in biomedicine to study lactation, mammary development, and pathology, including neoplasia. The established cells represent an adequate surrogate for mammary gland; were successfully used to study mammary gland immunity, lactation, and mammary stem/progenitor cells; and have a potential to be used for other purposes.
Part of the book: Goat Science
Genomic structural variations (SVs) are genetic alterations that result in duplications, insertions, deletions, inversions, and translocations of segments of DNA covering 50 or more base pairs. By changing the organization of DNA, SVs can contribute to phenotypic variation or cause pathological consequences as neurobehavioral disorders, autoimmune diseases, obesity, and cancers. SVs were first examined using classic cytogenetic methods, revealing changes down to 3 Mb. Later techniques for SV detection were based on array comparative genome hybridization (aCGH) and single-nucleotide polymorphism (SNP) arrays. Next-generation sequencing (NGS) approaches enabled precise characterization of breakpoints of SVs of various types and sizes at a genome-wide scale. Dissecting SVs from NGS presents substantial challenge due to the relatively short sequence reads and the large volume of the data. Benign variants and reference errors in the genome present another dimension of problem complexity. Even though a wide range of tools is available, the usage of SV callers in routine molecular diagnostic is still limited. SV detection algorithms relay on different properties of the underlying data and vary in accuracy and sensitivity; therefore, SV detection process usually utilizes multiple variant callers. This chapter summarizes strengths and limitations of different tools in effective NGS SV calling.
Part of the book: Bioinformatics in the Era of Post Genomics and Big Data