Omics Data and Personalized Medicine

Omics data refers to large-scale data generated from high-throughput techniques that study various biological components on a comprehensive scale.​

The term “omics” is derived from disciplines such as genomics, transcriptomics, proteomics, metabolomics, and others, each focusing on different types of biological molecules.​

Genomics: Genomics involves the study of an organism’s entire genome, including its genes and their functions, as well as interactions between genes and other elements within the genome.​

Transcriptomics: Transcriptomics focuses on the study of all RNA molecules present in a cell or tissue at a given time, providing insights into gene expression patterns and regulation.​

Proteomics: Proteomics involves the study of all proteins present in a cell, tissue, or organism, including their structures, functions, and interactions.​

Metabolomics: Metabolomics aims to identify and quantify all small-molecule metabolites present in a biological sample, providing insights into cellular processes and metabolic pathways.​

Integrating multiple omics datasets, allows for a holistic characterization of individual patients and their unique molecular profiles. By integrating omics data with clinical data, electronic health records, and other relevant information, healthcare providers can develop personalized treatment plans tailored to each patient’s specific needs, preferences, and genetic makeup.​

Currently, our study incorporates genomics and proteomics data and in the near future, transcriptomics and metabolomics will also be covered.​