Data Analysis Strategies for Multiomics and Integromics Datasets.

• Grant number: 24/04860-9
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: September 01, 2024
• End date: December 31, 2024
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Animal Production
• Principal Investigator: Nara Regina Brandão Cônsolo
• Grantee: Eduardo Solano Pina dos Santos
• Supervisor: Ranjith Ramanathan
• Host Institution: Faculdade de Medicina Veterinária e Zootecnia (FMVZ). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: Oklahoma State University, United States
• Associated to the scholarship: 23/14600-1 - Statistical Modeling Strategies in the Integrated Evaluation of Analyses Metabolomics, Genomics and Lipidomics., BP.IC

Abstract

The advancement of omics methodologies has revolutionized our capacity to innovate through specialized approaches. Genomics delves into the layers of specific genes, while metabolomics explores the metabolites generated as products of organismal reactions. The latest contribution, lipidomics, delves into the universe of non-polar molecules. However, such progress necessitates innovative data processing and interpretation techniques due to the immense complexity of datasets and their biological implications. In the Brazilian context, omics data analysis has emerged as a growing yet underexplored demand. Ongoing efforts in Brazil are focused on exploring statistical models to integrate genomics, metabolomics, and lipidomics data. Through the utilization of software such as R and JASP, the aim is to identify the most effective statistical methodologies for datasets collected from calves at birth and during the weaning phase. In this process, understanding the development of tenderness in these animals is sought, with a specific focus on Nelore cattle meat tenderness. Through the BEPE program, the objective is to acquire comprehensive knowledge of essential software and programs to comprehend the biological significance of statistical results, particularly focusing on network analysis. This stage is crucial for building a solid foundation of knowledge within the field of omics research. These skills are essential for work within this domain and represent a vital element in the education and professional development of individuals in this area.