The paper presents an approach to the holistic analysis of transcriptomic data which integrates two state-of-the-art methodologies into a coherent framework. The aim of the proposed approach is to give insight into the discovered patterns, help explaining the observed phenomena, enable the creation of new research hypotheses and assist in design of new experiments. We have integrated a methodology for semantic analysis of transcriptomic data, a system for automated extraction of biological relations from the literature, and a number of supporting components. The approach is demonstrated and evaluated on a publicly available dataset from a clinical trial in acute lymphoblastic leukaemia and a document corpus of full-text articles from the PubMed Open Access Subset.