The Stategra Initiative Develops Novel Methodologies for the Integrative Analysis of Multiple Types of NGS & Omics Data

Next generation sequencing has speed up genome analysis and brought omics research closer to many organisms and biological scenarios. Today an increasing number of research projects propose the combined use of different omics platforms to investigate diverse aspects of genome functioning. These proposals ideally seek to provide complementary sources of molecular information that eventually can be put together to obtain systems biology models of biological processes. Hence, it is not rare anymore to find experimental designs involving the collection of genome, transcriptome, epigenome and even metabolome data on a particular system. However, standard methodologies for the integration of diverse omics data types are not yet ready and researchers frequently face post-experiment question on how to combine data of different nature, variability, and significance into an analysis routine that sheds more light than the analysis of individual datasets separately. The STATegra (http://stategra.eu) is a FP7 funded project that has been conceived to address these problems and provide the genomics community with user-friendly tools for the integration of different omics data types. STATegra uses a mouse B- cell differentiation system to measure up to seven different omics technolgies: RNA-seq, ChIP-seq, DNase-seq, Methyl-seq, smallRNA-seq, proteomics and metabolomics, on the same set of samples and develop tools for data integration. In this presentation I will present current results of the project that include:

* Experimental design in integrative multiomics experiments

* Data fusion methods to analyze the relative information content of different omics

* Machine learning methods to dissect RNA regulatory programmes.

* Integrative Pathway-based approaches to leverage gene expression, metabolomics and proteomics data for pathway analysis.

* The STATegraEMS, an Experiment Management System for storage and annotation of complex omics experiments.

These methodologies will be made available to the community through the STATegRa R package and other user-friendly software tools.