Project R-14396

Integromics – Towards integration of big bio-molecular data to enable systems biology (Research)

Rapid advances in high-throughput technologies have enabled the fast and efficient generation of a large amount of omics data. This data can yield knowledge about underlying effect of molecules in biological systems [1]. Over the past few decades, extensive single-level analysis of omics data such as gene expression analysis, RNA sequence analysis has been the common standard approach. However, combining omics data into a comprehensive multi-omics dataset is rapidly creating a paradigm shift in biomedical research, as it offers an effective way to leverage the strength of multi-level omics data, by providing even more powerful insights into the systems biology interpretation. However, integrative analysis is not straightforward, particularly due to the high dimensionality and heterogeneity of the data and by the lack of universal analysis framework [2]. Several methods, such as matrix factorization methods, network-based methods, correlation-based integrations, have been develop to facilitate integrative analysis [3, 4], But these methods do not completely address issues surrounding multiomics integration such as un equal variability due to the different scale of the measurements and the lack of robust solution for the data analysis. In addition although methods for integrated data analysis were developed in recent years, one unified framework is not exist yet (not the methodology not the software). The aim of this joint research project between SCK-CEN and DSI in UHasselt is to develop statistical frameworks and software tools for integrating and analyzing multi-omics data, utilizing various types of - omic datasets generated at SCK•CEN and other open-source databases. We focus on two main research objectives: (1) Methodology development for integrative analysis. This objective is to develop a methodology for integrative analysis focusing on two primary level of analysis, that is, a global analysis and local analysis. For both research lines, our goal is to find common patterns (of features and samples) across multiple omics data available for the experiment of interest. (2) Software development. The software development for the methodologies outlined in Objective (1) will be conducted in R, and/or Python platform. This objective will run concurrently with Objective (1).

01 August 2023 - 31 July 2027