The Interdisciplinary MultiOMIC Project to Advance Crossanalysis in Translational Research (IMPACT) is the essence of our professional experience.
As two experienced data scientists who focus on biomedicine for more than 15 years and while working on numerous clinical trials we identified the high demand in Translational Research and Precision Medicine for affordable cross-analyses of OMICs and clinical data.
Our preliminary research indicated cytome as the common factor among the heterogeneous data and working with the European Society for Clinical Cell Analysis (ESCCA) we validated this hypothesis. Building upon the preliminary research
IMPACT was born, a project which is already producing impressive results and can significantly improve biomarker discovery across all OMIC fields.
For the platform to implement our prototype, we chose i2b2 – tranSMART. This decision was based on the platform’s open-source model, its constantly increasing popularity and the top score it received in the published benchmarks.
What is i2b2 – tranSMART
the most advanced & widely used open-source translational research cloud platform
State of the art
The preferred data sharing and analytics platform for TR and PM, linking academic, non-profit and corporate research communities for collaborative research
An ever growing community
i2b2 – tranSMART community includes most of the industry leaders and the highest ranked academic institutions
Deployed in more than 10.000 TR and PM projects around the world to standardize management, integration, sharing and cross-analysis of OMICs, clinical and biological data
A success story
Projects based on i2b2 – tranSMART already attracted more than 1.000.000.000€ in EU and US funding.
Built with clinical trials in mind, i2b2 – tranSMART complies with FDA and EMA directives for drug discovery
i2b2 – tranSMART
An open-source project
developed by scientists and the industry
to be used by scientists and the industry
Easy to be used
by biologists, medical doctors, chemists and anyone else with TR or PM background without specific requirements for bioinformatics or statistical knowledge
all the necessary modules for standardized/automated cell population description and a functional prototype which boosts biomarker discovery across all OMICs fields
A high efficiency, published, database schema for managing and analyzing/cross-analyzing data.
A published methodology for cytome data curation, harmonization, normalization and integration
An improved method to identify cell populations and their properties in ungated FCS files
An improved method to perform quantitative analysis on cytome data
A fully automated and standardized pipeline from raw Cytome data files to data analysis
A Functional Prototype
Our project is not just an innovative idea. We already have a fully fictional prototype that can significantly improve OMICs biomarker discovery
having already a functional prototype, which can significantly improve biomarker discovery, we are looking for an industrial partner to co-develop a high quality multi-OMICs/clinical cross-analysis cloud service
Elevate the impact of cross-analysis in translational research and precision medicine. Increase the demand for corresponding products and services
Accelerate drug discovery, improve prognosis and diagnosis, make it accessible to a bigger audience at a reduced cost
Provide advanced collaboration and data management solutions as a cloud service to both academia and the industry
Collaborate with industry and academic leaders to shape the future of translational research and precision medicine
The Interdisciplinary MultiOMIC Project to Advance Crossanalysis in Translational Research (IMPACT), is an innovative effort to increase the available cross-analysis options in translational research and precision medicine.
By developing a standardized and automated pipeline for cytome (flow and mass cytometry) feature extraction and introducing it in one of the most popular open-source translational research platforms, we unlocked the ability to use the cellular properties to accurately target specific data-sets across any OMIC field and boost biomarker discovery.
Cytome features, like cellular populations distribution and immunophenotype, are a common factor affecting and being affected by various conditions in the rest of the data fields (clinical, low dimensional biological, genome, transcriptome, exome, SNPs, comparative genomic hybridization, proteome, metabolome, and epigenome) and can be used to smooth the heterogeneity among the different data production techniques (e.g. imaging, next generation sequencing, microarrays and mass spectrometry).
Cross-analysis has multiple benefits at an increased data integration cost. With the use of cytome, IMPACT can enhance the cross-analysis benefits while eliminating the extra cost.