Jens Hoefkens’s research while affiliated with Genedata and other places

The integration and analysis of large datasets in translational research has become an increasingly challenging problem. We propose a collaborative approach to integrate established data management platforms with existing analytical systems to fill the hole in the value chain between data collection and data exploitation. Our proposal in particular ensures data security and provides support for widely distributed teams of researchers. As a successful example for such an approach, we describe the implementation of a unified single platform that combines capabilities of the knowledge management platform tranSMART and the data analysis system Genedata Analyst™. The combined end-to-end platform helps to quickly find, enter, integrate, analyze, extract, and share patient- and drug-related data in the context of translational R&D projects.

Breast and ovarian cancers pose huge and unsolved challenges to the medical profession. Breast cancer is the most common cancer in women in the EU: more than 332,000 women are diagnosed with breast cancer each year and a woman dies every 6 minutes from this disease. Ovarian cancer, whilst far less common than breast cancer, is often diagnosed when the disease is at an advanced stage and has spread to other areas of the body. More than 60% of ovarian cancer patients die within the first 5 years after diagnosis. Implementation of successful screening programs has dramatically reduced the number of women dying from cervical cancer. Similarly, the EU FP7 consortium EpiFemCare aims to reduce the number of women diagnosed with late stage breast or ovarian cancer by 50%, reduce the number of women who receive unnecessary long-term chemotherapy by 50%, and reduce the number of women dying from these cancers by 20%. EpiFemCare will establish and clinically validate a series of blood tests based upon DNA methylation technology that will facilitate both early detection and prediction of therapeutic outcome. The project consists of three phases: (1) Epigenome-wide discovery of ovarian/breast cancer specific DNA methylation markers. (2) Development of serum based assays for cancer specific markers. (3) Validation of the serum test performance in thousands of serial samples from prospective clinical trials. In phase 1 Illumina Infinium Human Methylation450 BeadChip Array technology is used to assess the methylation status of ~485’000 sites in cancer and control tissues. In parallel Reduced Representation Bisulfite Sequencing (RRBS) is used to identify & confirm cancer specific methylated circulating DNA in matching serum samples. Using Genedata Expressionist® for Genomic Profiling, we have established an automated bioinformatics pipeline for the detection of cancer specific differentially methylated regions (DMRs) that are most likely to fulfill the strict specificity criteria of a serum based test. The most promising DMRs are taken forward for the serum based clinical assay development and validation.

Commercial software packages including Genedata Expressionist®, from Genedata USA, Lexington, MA, are available that support the management and integrated analysis of large and diverse biological data. The tool is a perfect answer to In addition to the inherent data complexity and strict divisions among research, development, and clinical trials, of various organizations. Genedata scientists were part of the data analysis team that demonstrated that the combination of omics and conventional toxicology does indeed prove to be a useful tool for mechanistic investigations and the identification of putative biomarkers. Similar consortia are under way and their results support the thesis of a true benefit in the integration of transcriptome, proteome, metabolome, and genome profiling data for, among others, toxicity prediction and patient stratification.

The interrogation of genome, transcriptome, and epigenome information is key to understanding genetic regulatory networks in crop species. While applications such as RNA-seq, ChIP-seq, and bisulfite sequencing can yield valuable information about such networks, it can be challenging for a scientist, or even a biostatistician, to bring all of this information together in a meaningful way. Many-to-one relationships across data types and variability in how data are distributed can make interpretation of the data difficult. Here we present a workflow-based approach for analyzing and integrating data across these diverse data types. Our approach has been designed not only to overcome these challenges, but also give researchers easier access to their data. By “mapping” data types to a common context, diverse sequence data, as well as raw data from other platforms such as microarray and quantitative PCR, can be efficiently integrated into an analysis. To demonstrate this, we created a workflow to analyze a previously published Oryza sativa data set containing mRNA, siRNA, and bisulfite sequencing reads (Chodavarapu et al, 2012). This information was used to build a correlation network of genetic interactions, which is informed by information across all of the data types. Despite broad variation between the individual strains, an integrated analysis utilizing all three data types revealed important differences between the groups. The method and results presented here highlight the power of integrated analysis across data types, and the importance of algorithms that can efficiently place large volumes of diverse data types into a shared context.