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Supriyo DE

Supriyo De
Title: Head, Computational Biology and Genomics Core
Office(s): RNA Regulation Section (RRS)
Phone Number: 410-558-8152
Email Address: desu@nih.gov

Biography

Dr. De received his M.B.B.S. (M.D.-equivalent) degree from Medical College, Kolkata, India in 1998 and his Ph.D. degree in Biology (Molecular Genetics) from Howard University, Washington DC in 2006, where he transitioned to genomics research. After postdoctoral training under Dr. Ranjan Sen and Dr. Kevin G. Becker at the National Institute on Aging (NIA), NIH, he became a Staff Scientist under Dr. Myriam Gorospe, Chief, Laboratory of Genetics and Genomics in 2013.  He was promoted to his present position as Head, Computational Biology and Genomics Core, NIA, NIH in 2017.

 

Research Interests/Portfolio

Interdisciplinary research is fascinating and is continuously evolving.  The Computational Biology & Genomics Core (CBGC) aims to advance interdisciplinary aging research through the analysis of complex biological systems by using high-throughput genomic technologies such as Next Generation Sequencing, Single Cell Sequencing, and microarrays.

CBGC’s portfolio includes (1) genomic, epigenomic, functional genomic (CRISPR screens), transcriptomic, single-cell data analyses; (2) coordinating the activities of data scientists at NIA IRP by maintaining a Biomedical Data Science Network (BDSN); (3) providing novel high-throughput instruments and high-performance computer systems to achieve the mission of the NIA; and (4) a fully functional, aging-centered core research program which fosters collaborations mainly within NIA IRP, NIH

The research interests of the CBGC include the role of microbiome in Aging and age-related diseases (e.g., Alzheimer’s Disease), detection of DNA and RNA base modifications and its role in aging, functional genomic analysis by CRISPR screens, and applications of Artificial Intelligence in genomic data analysis and in medical imaging informatics.  CBGC also provides advice on experimental design, sample preparation, quality control of samples, data analysis, data sharing, and training.

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