Integrative 'omic' approach towards understanding the nature of human diseases

The combination of improving technologies for molecular interrogation of global molecular alterations in human diseases along with increases in computational capacity, have enabled unprecedented insight into disease etiology, pathogenesis and have enabled new possibilities for biomarker development. A large body of data has accumulated over recent years, with a most prominent increase in information originating from genomic, transcriptomic and proteomic profiling levels. However, the complexity of the data made discovery of high-order disease mechanisms involving various biological layers, difficult, and therefore required new approaches toward integration of such data into a complete representation of molecular events occurring on cellular level. For this reason, we developed a new mode of integration of results coming from heterogeneous origins, using rank statistics of results from each profiling level. Due to the increased use of next-generation sequencing technology, experimental information is becoming increasingly more associated to sequence information, for which reason we have decided to synthesize the heterogeneous results using the information of their genomic position. We therefore propose a novel positional integratomic approach toward studying 'omic' information in human disease.