Multi-omics

Multi-Omics is an emerging technology in the field of clinical science in India.

About Multi-Omics

• Multi-omics (multiple omics) is a new approach in biological sciences  where the data sets of different omic groups are combined to power discovery across multiple levels of biology. 
  • By combining data researchers can achieve a more comprehensive understanding of molecular changes contributing to normal development, cellular response, and disease.
• Omic Groups: 
  • Genomics: It focuses on the structure, function, evolution, mapping, and editing of information coded within an organism’s DNA.
  • Transcriptomics: Study the complete set of RNA transcripts produced by the genome.
  • Proteomics: Evaluate protein expression for a better understanding of cellular function and prediction of therapeutic responses.
  • Epigenomics: Examine heritable changes in gene expression activity caused by factors other than DNA changes.
  • Metabolomics: Metabolome comprises all the metabolites present in a cell, tissue, or organism, including small molecules, carbohydrates, peptides, lipids, nucleosides, and catabolic products.
  • Microbiomics: It consists of all the microorganisms of a community. Microbes have been found in human skin, mucosal surfaces, and gut.
    • The microbiome is analyzed by sequencing the 16S rRNA genes or metagenomics quantification.
• Omics datasets: They  are libraries characterizing a specific biological feature,  such as the genes, lipids, proteins, metabolites, or microbes of an organelle, cell, tissue, or organism. 
  • Individual analysis of an omics dataset can identify links between the characterized feature and a specific biological phenomena. 
• Multi Omics Stratergy: Multiomics is the integrated analysis of multiple omics datasets through which one  can identify mechanisms of action, biomarkers, networks, pathways, and other relationships present in physiological and pathological processes.
  • The  field encompasses all the omics fields and aims  to understand the native and altered state of an organism by the analysis of the data from different omics experiments.
• Purpose: Multi Omics approach is critical to find strategies to characterize, monitor, predict and find treatment to many diseases

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India’s approach to Multi Omics

• India is creating disease-specific datasets with many disease specific consortia emerging  around the country creating datasets to address individual health problems, ranging from tuberculosis to cancers, rare genetic disorders in children, and even antimicrobial resistance. 
• The Indian Gene Map: 
• • The Genome India project: The project is a national initiative which  aims to develop a reference genome for Indian people, which will help design genome-wide and disease-specific ‘genetic chips’ for low-cost diagnostics and research.
    • The Department of Biotechnology had completed sequencing 10,000 genomes from 99 ethnic groups. 
  • Mission IndiGen: The Council for Scientific and Industrial Research (CSIR) had sequenced the entire genomes of 1,008 individuals in India representing diverse ethnic groups.
    • The Mission aims to create a pilot dataset with which researchers could analyse the epidemiology of genetic diseases and help develop affordable screening approaches, optimise treatment, and minimise adverse events for them.
• Role of AI: More value is being attracted by combining these individual datasets using artificial intelligence and machine learning with other extensive datasets on proteins (proteomics), gene expression in cells (transcriptomics), and epigenomics to develop a ‘multi-omics’ approach to tackle diseases.
  • Usage: AI and ML based approaches are used in in-house bioinformatic pipelines as well as part of commercial tools for analysis of the sequencing data to identify disease-causing variants
  • Ease of Analyses: AI and Machine Learning can help predict an individual’s risk of developing cancer, develop diagnostic tools to detect some cancers early, classify them, and develop treatment strategies

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