Integrative bioinformatics analysis of combined epigenome, transcriptome and proteome data.

Rapid evolution in analytical technologies including next generation sequencing and mass spectrometry recently boosted the systematic analysis of molecular layers such as the transcriptome, epigenome and proteome. Datasets of enormous size and diversity are now routinely generated in Systems Biology research projects. The advances in the acquisition of these diverse data are followed by the development of new bioinformatics approaches, typically each dedicated to the analysis and interpretation of a specific data type. The separate analysis of each data type does not suffice anymore to satisfy the need for a multi-perspective understanding of biological processes and diseases, which is imperative in modern Systems Biology. Different 'omics datasets should not only be analysed separately, but also be integrated and compared, in order to reveal patterns that encompass multiple 'omics layers. This is an underexplored research area in the bioinformatics field. At the PPES lab of Proteomics & Epigenetic Signaling, parallel transcriptomic (Illumina Array, miRNA QPCR array), epigenomic (MBD2seq, Illumina CpG array) and chemoproteomic (SILAC/iTrAQ) assays have been performed on different cancer cell types treated with the very potent tumor selective anticancer drug Withaferin A, to get a comprehensive view of cellular networks targeted during chemosensitisation. By an integrated analysis of our available datasets, we want to identify key proteins/nodes/pathways responsible for the potent chemosensitizing anti-cancer effects of Withaferin A. In this doctoral project, novel bioinformatic methodologies will be developed and studied which enable the integrative analysis of these three different quantitative omics data types (transcriptome, epigenome and proteome) with high relevance for research ongoing in and outside the University.

Keywords:SYSTEMS BIOLOGY, BIOINFORMATICS, DATA MINING

Disciplines:Scientific computing, Biochemistry and metabolism, Bioinformatics and computational biology, Medical biochemistry and metabolism, Public health care, Public health services