Project
Project #2936
Step by step one goes very far
Organisms :
Group : Name of Applicant : Milena Hasan Date of application : 15-12-2015 Unit : Department of Immunology Location : Francois Jacob (27) and Metchnikoff (64) Phone : 3027@ Mail : milena.hasan@pasteur.fr
Project context and summary :
The Center for Human Immunology (CIH) supports researchers involved in translational research projects by providing access to 16 different cutting edge technologies. Currently, the CIH hosts over 60 scientific projects coming from 8 departments of the Institut Pastuer and 5 external teams. In order to respond to the growing needs of these projects in the area of single cell analysis, the CIH has introduced a significant number of single-cell/single-molecule technologies over the past 2-3 years. These new technologies, such as the Personal Genome Machine (PGM) and Ion Proton sequencers, iSCAN microarray scanner, Nanostring technology for transcriptomics profiling and real-time PCR machine BioMark, give rise to large datasets with high dimensionality. Such trend, in terms of data complexity, is also true for flow cytometry technologies (currently reaching over 20 parameters per cell). The exploration of this data is generally beyond the scope of scientists involved in translational research projects. In order to maximize the research outcomes obtained from the analysis of these rich datasets, and to ensure that the full potential of our technologies can be served to the users of the CIH, we would require a proximity bioinformatics support. A CIH-embedded bioinformatician would: 1) design and implement standard analysis pipelines for each of the data-rich technologies of the CIH; 2) provide regular ‘bioinformatics clinics’ to allow scientists the possibility to customize standard pipelines to their specific needs; 3) run trainings on the ‘R software’ platform and other data analysis tools (such as Qlucore) of interest for the CIH users. The objective would be to empower the users to run exploratory analysis by themselves, and to teach good practices in terms of data management and data analysis.
Related team publications :