Computational Biology & Genomics Core
IRP Core: Computational Biology & Genomics Core
The Computational Biology & Genomics Core (CBGC) is involved in interdisciplinary research and institute-wide training by collaborating mainly with the laboratories in the NIA IRP, NIH.
The goals of the core are to (1) carry out genomic, epigenomic, functional genomic (CRISPR screens), transcriptomic, single-cell data analysis and data sharing using NIH data-sharing guidelines; (2) coordinate the activities of data scientists at NIA IRP by maintaining a Biomedical Data Science Network (BDSN); (3) acquire and maintain novel high-throughput instruments and high-performance computer systems to achieve the mission of the NIA; (4) have a fully functional, aging-centered core research program which fosters collaborations mainly within NIA IRP, NIH.
The Computational Biology & Genomics Core (CBGC) provides advice on experimental design and sample preparation, quality control of samples, data analysis, data sharing, and training. Apart from the primary focus on genomic and transcriptomic research, CBGC is highly interested in applying Artificial Intelligence (AI) for image analysis and novel genomic data analysis algorithms. The core utilizes gene expression microarrays, Illumina and Oxford Nanopore sequencers and 10x Genomics single-cell sample preparation instruments to meet the needs of NIA IRP researchers. Moreover, CBGC maintains several high-performance computer systems and is involved in designing and maintaining NIA IRP cloud computing for use by all interested NIA IRP researchers.
CBGC is involved in fostering wide variety of collaborative research and providing training on diverse data science topics beyond the core research areas by maintaining the Biomedical Data Science Network (BDSN) comprising of all the data scientists in the NIA IRP. The BDSN website (internal to NIA IRP, sign-in required) provides description of the expertise available at NIA IRP, Baltimore and their contact information.
Core Research Areas
- Role of microbiome in Aging and age-related diseases (e.g., Alzheimer’s Disease)
- Detection of DNA and RNA base modifications
- Applications of Artificial Intelligence in data analysis
- Functional Genomics (CRISPR) Screens
Elin Lehrmann, PhD, Scientist
Gabriel Lam, PhD, Staff Scientist
Selected Recent Publications:
- Pandey PR, Yang JH, Tsitsipatis D, Panda AC, Noh JH, Kim KM, Munk R, Nicholson T, Hanniford D, Argibay D, Yang X, Martindale JL, Chang MW, Jones SW, Hernando E, Sen P, De S, Abdelmohsen K, Gorospe M (2020) circSamd4 represses myogenic transcriptional activity of PUR proteins Nucleic Acids Research 48 (7): 3789-3805.
- Qiu X, Ma F, Zhao M, Cao Y, Shipp L, Liu A, Dutta A, Singh A, Braikia FZ, De S, Wood WH, Becker KG, Zhou W, Ji H, Zhao K, Atchison ML, Sen R (2020) Altered 3D chromatin structure permits inversional recombination at the IgH locus Science Advances 6 (33): eaaz8850.
- Myrum C, Kittleson J, De S, Fletcher BR, Castellano J, Kundu G, Becker KG, Rapp PR (2020) Survey of the Arc Epigenetic Landscape in Normal Cognitive Aging Molecular neurobiology 57, 2727–2740.
- Chaudhary R, Muys BR, Grammatikakis I, De S, Abdelmohsen K, Li XL, Zhu Y, Daulatabad SV, Tsitsipatis D, Meltzer PS, Gorospe M, Janga SC, Lal A (2020) A circular RNA from the MDM2 locus controls cell cycle progression by suppressing p53 levels Molecular and Cellular Biology 40 (9).
- Casella G, Munk R, Kim KM, Piao Y, De S, Abdelmohsen K, and Gorospe M. (2019) Transcriptome Signature of Cellular Senescence. Nucleic Acids Research 47 (14): 7294-7305.
- Lee JH, Demarest T, Babbar M, Kim E, Okur M, De S, Croteau D, Bohr VA (2019) Cockayne syndrome group B deficiency reduces H3K9me3 chromatin remodeler SETDB1 and exacerbates cellular aging. Nucleic Acids Research 47 (16), 8548-8562.
- Noh JH, Kim KM, Pandey PR, Hooten NN, Munk R, Kundu G, De S, Martindale JL, Yang X, Evans MK, Abdelmohsen K, Gorospe M (2019) Loss of RNA-binding protein GRSF1 activates mTOR to elicit a proinflammatory transcriptional program. Nucleic Acids Research 47(5):2472-2486.
- Chatterjee B, Roy P, Sarkar UA, Zhao M, Ratra Y, Singh A, Chawla M, De S, Gomes J, Sen R, Basak S (2019) Immune differentiation regulator p100 tunes NF-κB responses to TNF. Frontiers in immunology 10:997.