Centre for Functional Genomics & Bioinformatics | The University of Trans-Disciplinary Health Sciences and Technology (TDU)
  • Centre for Functional Genomics & Bio-informatics

  • Centre In-Charge

    Dr. Pavithra N

  • About the Centre

    Recent advances in DNA sequencing, molecular cloning, and gene editing helped remarkably in understanding the biology of life forms in our nature. Reduced cost and increased speed of DNA sequencing have led to rapid growth of genomics. Functional significance of genomic elements (genes, RNA, proteins, small molecules and phenotype) are still being unravelled in many organisms. The data coming out from large-scale functional genomics studies need to be stored, analyzed and interpreted. This requires a high-end computing facility and data center to cater towards the scientific community. India being a country having the richest biodiversity hotspots in the world, there is huge scope to decode genomes, understand and harness functionality of biodiversity through functional genomics and bio-informatics approaches. Our center aim to carryout large-scale studies in relation to conservation and traditional medicine.

  • Our Expertise

    1. Functional Genomics (Clinical, Medicinal Plants, Pharma, Medical etc)
    2. Microbiome (Host-pathogen interactions, Culturomics,)
    3. Ayurgenomics (Clinical, Ayurbiology)
    4. Plant genomics (Horticulture, Agriculture)
    5. Conservation and Forestry
    6. Computational Biology (Big Data Analyses, Omic Database and software Development)
  • Mission

    • Apply functional genomic technologies to understand the concepts of traditional life science practice
    • Analyze big data generated from lagerer studies by transforming using Bio-informatics tools to draw inferences and conclusive comprehensive discovery
    • Education and training to capitalize human resource to carry out big bio-data research
  • Objectives

    • Applications of functional genomics in conservation science
    • Apply genomic technologies to understand the concepts of traditional life science practice
    • Analyze big bio-informatics data from lager studies through data transformation towards useful inferences and conclusive concepts
    • Evolving national network projects
    • Train and capitalize human resources to carry out big-data research

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