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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.
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:
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- 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.
- Approved by the government in 2020, to create a comprehensive catalogue of genetic variations in the Indian population.
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- The Department of Biotechnology completed sequencing 10,000 genomes in January 2024 under the ‘Genome India’ project.
- Mission IndiGen:
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- 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:
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- 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:
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- 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:
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- 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:
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- Analysis Scope: Use genomics and metagenomics to analyse antimicrobial resistance in slow-growing microbes such as tuberculosis.