Bioinformatics

The Bioinformatics Image Core (BIONIC) links biology with mathematics, image processing, computer science and statistics to develop computational tools for image analysis.

We use PerkinElmer Opera and Phenix high-content screening microscopes, ImageJ analysis software and the statistical program R with Bioconductor packages such as CellHTS2,  in monolayer and 3D tissue culture. 

Key methodologies include:

  • Applied image segmentation
  • Thresholding
  • Morphological operations
  • Filtering
  • Feature extractions
  • High-throughput statistical analysis
  • Algorithm development

Wilson GA et al (2023). Active growth signaling promotes senescence and cancer cell sensitivity to CDK7 inhibition. 
Molecular Cell 83(22):4078-4092.e6 doi: 10.1016/j.molcel.2023.10.017

Papandreou A et al (2023). Automated High-Content Imaging in iPSC-derived Neuronal Progenitors. 
SLAS Discovery doi: 10.1016/j.slasd.2022.12.002

Ketteler R, Kriston-Vizi J (2022). High-Content Screening in Cell Biology 
in: Encyclopedia of Cell Biology: Volume 1-6, Second Edition: 472-483 doi: 10.1016/B978-0-12-821618-7.00032-8

Kriston-Vizi J et al (2022). Salmonella Exhibit Altered Cellular Localization in the Presence of HLA-B27 and Codistribute with Endo-Reticular Membrane. 
Journal of Immunology Research 2022:1-9 
doi: 10.1155/2022/9493019

Janos Kriston-Vizi

BIONIC is potentially accessible to UCL researchers, as well as external users, on a collaboration basis. Please contact Janos Kriston-Vizi for details

Tyan FT48-B8812 High Performance Barebone System

SuperServer 4048B-TR4FT

The Royal Society - International Exchanges award