“Multi-omics” is revolutionising the detection and treatment of tuberculosis and cancers in India

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“Multi-omics” is revolutionising the detection and treatment of tuberculosis and cancers in India

Context:

Multi-omics, a rapidly emerging technology in India’s clinical science, is integrating with AI and machine learning to create  valuable datasets for tuberculosis, cancers, rare genetic disorders, and antimicrobial resistance.

 

What is Multi-Omics?

  • Multi-Omics is a new approach where the data sets of different omic groups are combined during analysis. 
    • Omics is a new term that has emerged to describe the field of large-scale data-rich biology.
  • The different omic groups employed are genome, proteome, transcriptome, epigenome, and microbiome. 
    • This integrated approach enhances researchers’ ability to explore molecular changes that influence normal development, cellular responses, and disease processes comprehensively.
  • Genomics is a field which involves identification of genes and genetic variants associated with a disease or in response to certain drugs and medication.

"Multi-omics" is revolutionising the detection and treatment of tuberculosis and cancers in India

India’s Approach in Multi-Omics: India is adopting this approach by forming disease-specific consortia across the country to address various health challenges.

  • from tuberculosis to cancers, rare genetic disorders in children, and antimicrobial resistance.
  • India’s genomics advancements have transformed disease diagnosis, management, and treatment.
  • Genome India Project:
    • Approved by the government in 2020, to create a comprehensive catalogue of genetic variations in the Indian population.
      • Aims to develop future therapies tailored to the genetic diversity of Indian populations.
    • Essential for understanding the evolution history and genetic basis of diseases specific to India.
    • The Department of Biotechnology completed sequencing 10,000 genomes in January 2024 under the ‘Genome India’ project.
  • Mission IndiGen:
    • Led by the Council of Scientific and Industrial Research (CSIR) sequence entire genomes of 1,008 individuals from diverse ethnic groups in India.
    • Aims to develop cost-effective screening methods and optimise treatments for genetic disorders.
    • Goal to establish a pilot dataset for epidemiological analysis of genetic diseases.
  • Role of Artificial Intelligence (AI) and Machine Learning (ML) in Genomics:
    • Crucial for analysing large datasets, predicting cancer risks, developing early detection tools, and formulating treatment strategies.
    • Applied in analysing genome-sequencing data for rare genetic disorders, managing exome and whole-genome datasets.

Disease-Specific Consortia:

  • Tuberculosis Genomics Consortium: Address tuberculosis eradication challenges through the Indian Tuberculosis Genomic Surveillance Consortium (InTGS).
    • Goals: Develop a centralised repository for tuberculosis strains, map genetic diversity, correlate mutations with drug resistance, and improve treatment outcomes.
  • Rare Genetic Disorders Initiative:
    • Mission PRaGeD: National initiative for paediatric rare genetic disorders.
    • Objectives: Raise awareness, conduct genetic diagnosis, identify new genes or variants, provide counselling, and develop therapies.
  • Cancer Genomics Efforts:
    • Indian Cancer Genome Consortium (ICGC-India): Characterises genomic abnormalities and genetic variations in Indian cancer patients.
    • Initiatives: Establish genomic data repositories like the Indian Cancer Genome Atlas project to advance cancer research and precision medicine.
  • Antimicrobial Resistance and Genomics:
    • Analysis Scope: Use genomics and metagenomics to analyse antimicrobial resistance in slow-growing microbes such as tuberculosis.

 

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