I am a veterinary scientist specializing in Animal Reproduction, with a research focus on applying transcriptomics and epigenomics to understand endometrial biology and embryonic and foetal development in ruminants. My approach to analysing and interpreting these complex data sets combines both traditional methodologies and innovative techniques, such as machine learning-based predictive models, tailored to extract valuable insights within the experimental context.

In essence, my goal is “to find the biological meaning within the sea of biological numbers.”

Repromics: Reproductive omics data science

The advent of high-throughput technologies, known as omics, has revolutionized biological research by enabling the large-scale measurement of molecular data. For instance, sequencing mRNA transcripts from a biological sample provides a comprehensive quantification of the protein-coding transcriptome. 

Analysing the vast datasets generated by omics requires the application of mathematical algorithms to interpret numbers derived from biological events. Indeed, bioinformatics tools identify patterns that can objectively describe –and even predict– physiological events or their disruptions, which is fascinating considering the deep complexity of biological organisms. 

Pregnancy is one of the most intricate processes in mammals, requiring precise communication between the developing embryo (or conceptus) and the maternal environment. Data generated from omics technologies applied to the various components involved in pregnancy—sperm, oocytes, the endometrium, embryo, and foetus—can be analysed using molecular data science tools, such as statistical analyses, multi-omics data integration, and machine learning algorithms. 

By leveraging these approaches, we can gain a deeper understanding of the interactions among these components, which are crucial for successful pregnancy establishment, progression, and the birth of healthy offspring.