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Muhlberger I.,Emergentec Biodevelopment GmbH
Methods in molecular biology (Clifton, N.J.) | Year: 2011

Progress in experimental procedures has led to rapid availability of Omics profiles. Various open-access as well as commercial tools have been developed for storage, analysis, and interpretation of transcriptomics, proteomics, and metabolomics data. Generally, major analysis steps include data storage, retrieval, preprocessing, and normalization, followed by identification of differentially expressed features, functional annotation on the level of biological processes and molecular pathways, as well as interpretation of gene lists in the context of protein-protein interaction networks. In this chapter, we discuss a sequential transcriptomics data analysis workflow utilizing open-source tools, specifically exemplified on a gene expression dataset on familial hypercholesterolemia. Source

Mayer B.,Emergentec Biodevelopment GmbH
Diabetologia | Year: 2016

Medications approved for diabetes-associated renal and cardiovascular morbidities and candidate drugs currently in development are subject to substantial variability in drug response. Heterogeneity on a molecular phenotype level is not apparent at clinical presentation, which means that inter-individual differences in drug effect at the molecular level are masked. These findings identify the need for optimising patient phenotyping via use of molecular biomarkers for a personalised therapy approach. Molecular diversity may, on the one hand, result from the effect of genetic polymorphisms on drug transport, metabolism and effective target modulation. Equally relevant, differences may be due to molecular pathologies. The presence of distinct molecular phenotypes is suggested by classifiers aimed at modelling progressive disease. Such functions for prognosis incorporate a complex set of clinical variables or a multitude of molecular markers reflecting a diverse set of molecular disease mechanisms. This information on disease pathology and the mechanism of action of the drug needs to be systematically integrated with data on molecular biomarkers to develop an experimental tool for personalising medicine. The large amount of molecular data available for characterising diabetes-associated morbidities allows for elucidation of molecular process model representations of disease pathologies. Selecting biomarker candidates on such grounds and, in turn identifying their association with progressive disease allows for the identification of molecular processes associated with disease progression. The molecular effect of a drug can also be modelled at a molecular process level, and the integration of disease pathology and drug effect molecular models reveals candidate biomarkers for assessing drug response. Such tools serve as enrichment strategies aimed at adding precision to drug development and use. © 2016 Springer-Verlag Berlin Heidelberg Source

Wiesinger M.,Emergentec Biodevelopment GmbH
Methods in molecular biology (Clifton, N.J.) | Year: 2011

Cross-Omics studies aimed at characterizing a specific phenotype on multiple levels are entering the -scientific literature, and merging e.g. transcriptomics and proteomics data clearly promises to improve Omics data interpretation. Also for Systems Biology the integration of multi-level Omics profiles (also across species) is considered as central element. Due to the complexity of each specific Omics technique, specialization of experimental and bioinformatics research groups have become necessary, in turn demanding collaborative efforts for effectively implementing cross-Omics. This setting imposes specific emphasis on data sharing platforms for Omics data integration and cross-Omics data analysis and interpretation. Here we describe a software concept and methodology fostering Omics data sharing in a distributed team setting which next to the data management component also provides hypothesis generation via inference, semantic search, and community functions. Investigators are supported in data workflow management and interpretation, supporting the transition from a collection of heterogeneous Omics profiles into an integrated body of knowledge. Source

Perco P.,Emergentec Biodevelopment GmbH | Perco P.,Medical University of Vienna | Oberbauer R.,Medical University of Vienna
Seminars in Nephrology | Year: 2010

The histologic scoring of renal biopsies is still the gold standard for renal disease classification. The Banff classification scheme and the chronic allograft damage index are histopathologic scoring schemes widely used in renal transplantation. The determination of genome-wide gene expression profiles in human renal biopsies has the potential to serve as independent validation data sets and also provide a more precise evaluation of the functional status behind the visible morphologic alterations. It is expected that results from high-throughput -omics experiments will lead to improved classification schemes in the near future as also discussed at recent Banff meetings. In this review we give an overview on -omics studies, focusing on the association of molecular changes on the transcript as well as on the protein level and morphologic scoring schemes in renal disease and transplantation. © 2010 Elsevier Inc. Source

Agency: Cordis | Branch: H2020 | Program: MSCA-RISE | Phase: MSCA-RISE-2015 | Award Amount: 387.00K | Year: 2016

Cardiovascular disease (VD) is the leading cause of mortality and morbidity in Europe and worldwide. The objective of the PRETREAT consortium is to generate a joint SME/academic European preclinical platform for providing services for detection of VD and drug development. This platform will combine the use of urinary and/or blood peptidomics in humans and in preclinical animal models of VD, together with bioinformatics and systems biology, in order to better detect, stratify and decipher the molecular mechanisms of VD, develop new animal models with high similarity to human disease, and provide new tools for obtaining information on novel drug targets. PRETREAT builds on the FP7 project Sysvasc (systems biology to identify molecular targets for vascular disease treatment, 2014-2018) and the combined expertise of the PRETREAT partners in clinical proteomics, proteomics, animal models and system medicine as the pillars to implement this platform. The work will be carried out in an extensive exchange program totaling 27 secondments. Main objectives during the secondments include establishment of the link between urinary markers and the pathophysiology of VD, identification of additional animal VD models with similarity to human VD, development of humanized body fluid readouts in VD models, establishment of a VD protein-centric database in order to automatically link urinary peptides to in situ changes and provide information on drug targets, and provide proof-of-concept of the utility of the proposed platform. These secondments will in parallel serve to keep information flowing within the project, increase individual research efficiency and create a multi-disciplinary working chain, train personnel, and prepare the sustainability of the results after the project. To complete training, 3 monthly webinars and four workshops are planned.

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