iBRAIN was conceived in the lab in 2006 to effectively handle large-scale image datasets and to make efficient use of super-computing facilities in the Zurich area such as the Brutus cluster of the ETH Zurich, and is continuously further developed. It is now the main interface for dealing with large-scale datasets, computational analysis of images, multivariate statistics, machine learning, and data-driven modelling. It incorporates an earlier version of CellProfiler, which has been extensively modified and further developed, a version of CellClassifier, and a large amount of meta-information on genes and proteins retrieved from STRING, DAVID, PathwayCommons and other sources. iBRAIN has become very robust, able to handle most occurring errors autonomously, and is essential for achieving reasonable turn around times for experiments based on quantitative large-scale imaging. This all with the aim to make such experiments common-practice and routine, like running a gel and doing a western-blot. On March 2013, iBRAIN had performed more than 150 processor years of computation.

iBRAIN_screenshot

                         The web browser-based user interface of iBRAIN for continuous monitoring of a large number of projects simultaneously.

 

 

Publications in which iBRAIN was used:

  1. Liberali, P. et al. A hierarchical interaction map of signaling and membrane trafficking genes in human cells. Cell In press (2014)
  2. Battich, N. et al. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nature Methods 10, 1127-33 (2013)
  3. Snijder B., et al. Predicting functional gene interactions with the hierarchical interaction score. Nature Methods 10, 1089-92 (2013)
  4. Wippich, F. et al. Dual Specificity Kinase DYRK3 Couples Stress Granule Condensation/Dissolution to mTORC1 Signaling. Cell 152, 791–805 (2013)
  5. Mercer, J. et al. RNAi Screening Reveals Proteasome- and Cullin3-Dependent Stages in Vaccinia Virus Infection. Cell Reports 2, 1036–1047 (2012)
  6. Snijder, B. et al. Single-cell analysis of population context advances RNAi screening at multiple levels. Molecular Systems Biology 8, (2012)
  7. Misselwitz, B. et al. RNAi screen of Salmonella invasion shows role of COPI in membrane targeting of cholesterol and Cdc42. Molecular Systems Biology 7, 474 (2011)
  8. Rämö, P., Sacher, R., Snijder, B., Begemann, B. & Pelkmans, L. CellClassifier: supervised learning of cellular phenotypes. Bioinformatics 25, 3028–3030 (2009)
  9. Snijder, B. et al. Population context determines cell-to-cell variability in endocytosis and virus infection. Nature 461, 520–523 (2009)