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Single-cell and bulk RNA-seq transcriptomics methods and applications in dairy cattle

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Next generation sequencing (NGS) technologies have enabled the generation of huge quantities of transcriptomics data using bulk tissue RNA sequencing (RNA-seq) technologies, which has recently developed further to capture gene expression at single cell resolution...
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  • 08 November 2026
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Next generation sequencing (NGS) technologies have enabled the generation of huge quantities of transcriptomics data using bulk tissue RNA sequencing (RNA-seq) technologies, which has recently developed further to capture gene expression at single cell resolution (scRNA-seq). This has led to detection of cell types and communications, novel gene transcripts, novel regulatory mechanisms, allele specific gene expression and numerous non-coding RNAs (ncRNAs) generating major implications for unravelling biology of cattle health, welfare and production. In this chapter, an overview of the bulk and scRNA-seq technologies and related bioinformatics tools from processing raw reads to final analyses are described. Differential expressions, co-expression networks, machine learning and systems genetics approaches are also elaborated. Case studies in dairy cattle feed-efficiency and Invitro embryo production and transfer technologies are provided, followed by challenges and future trends in transcriptomics, namely multi-omics integration and use of artificial intelligence methods for precision dairy cattle management and breeding.

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Price: £25.00
Publisher: Burleigh Dodds Science Publishing
Imprint: Burleigh Dodds Science Publishing
Series: Burleigh Dodds Series in Agricultural Science
Publication Date: 08 November 2026
ISBN: 9781835458914
Format: eBook
BISACs:

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  • 1 Introduction
  • 2 Transcriptomics
  • 3 Processing of bulk and single cell RNA-sequencing data
  • 4 Network analyses
  • 5 Machine learning approaches
  • 6 Transcriptomics of dairy cattle feed efficiency and reproduction
  • 7 Challenges and opportunities in using transcriptomics in selective breeding
  • 8 Future trend: integration of multi-omics and computational approaches
  • 9 Conclusion and future trends in research
  • 10 Where to look for further information
  • 11 Acknowledgments
  • 12 References