Dr. Indira Ghosh (SCIS,JNU),
Dr. Punit Kaur (AIIMS),
Dr. Marylyn D. Ritchie (PSU, USA)
Recent advances and improvements in the DNA sequencing technologies coupled with the drop in sequencing costs have led to the explosive growth of unexplored biological and medical knowledge or ‘Big Data’. With more and more patients submitting their genome for clinical studies the size of 'omics dataset’ and 'clinical dataset' are exploding. The enormity of biological data is transforming research from hypothesis-driven to data-driven. The big medical data is thus opening up newer avenues to investigate hitherto unexplored information embedded in the genomes and proteomes.
The central objective of medical sciences is to mine the information at molecular level (genes and proteins) and study the interaction between these genes and environmental factors, in order to relate them to the phenotypes (diseases) of the clinical implications. Monogenic diseases like thalassemia result from a mutation in a single gene but complex diseases like diabetes are affected by a number of genes, their mutations and various interactions through biological pathways. The data deluge in biological domain comes with an opportunity to extract a complete picture of gene-disease association for multifactorial diseases and to study varied patient responses for infectious diseases.
Yet, this potential cannot be unlocked without addressing issues related to integration and analysis of data from different sources. 'Big Data' management and analysis call for development of new tools, software and methodologies. As a result, translational bioinformatics has emerged to provide a platform for the integration of different biological techniques, statistical analysis methods and information technologies to translate basic molecular, genetic, cellular, and clinical data into clinical applications in order to improve human health and cure diseases. Efficient analysis of big medical data is required for a better understanding of diseases especially complex diseases and development of predictive, preventive and better personalized diagnostics and therapeutics. In short, the knowledge gained can provide an insight into factors that influence individual and public health and disease. In an endeavor to enable newer biological discoveries, the aim here is to translate the basic information obtained from patients into an end product for the benefit of human health and disease.
Emerged Translational Pipeline is shown below :
Patients → Clinical and molecular data → Statistical/Computational methods → Drug or Biomarker development → Patients
Purpose: The symposium aims to bring together clinicians and researchers from both US and India to discuss the advances and challenges in the analysis of the huge amount of biomedical data presently available. The symposium will focus on GWAS, PheWAS, epigenetics and PPI, technologies generating big medical data, big data storage and maintenance, data integration, analysis and visualization and big data sharing tools and policies and provide hands on training on the basic practical knowledge on management, interpretation and application of big data. The focal point will be on the genetic and genomic analysis of diseases with specific reference to cancer, genetic disorders and infectious diseases and explore the challenges and roadblocks for moving beyond into health.