Maria Ulmer
(née Wörheide)
PhD candidate
Research Interests
- Exploration of complex diseases, such as Alzheimers disease, through multi-omics data
- Cross-omics results integration and visualization accessible through interactive web interfaces:
- AD Atlas (adatlas.org)
- Omicscience (omicscience.org)
- Development of multi-omics- and graph-based machine learning and deep learning approaches for multi-omics-guided candidate target identification/prioritization and in silico drug repositioning
Skills and Expertise
BioinformaticsMulti-omicsData IntegrationBig DataWebtool development
Network analysisData visualization
Professional Background
PhD in Computational Biology
Computational Health Center / ICB, Helmholtz Zentrum München and TUM School of Life Sciences, Technical University Munich
M.Sc. Bioinformatics
Thesis: "NetPrio - an approach for network integration and pathway prioritization with applications to multi-omics leukemia data"
Supervisor: Dr. Jan Krumsiek, Institute of Computational Biology at the Helmholtz Zentrum München
Technical University Munich and Ludwig-Maximilians-Universität Munich
B.Sc. Bioinformatics
Thesis: "Epigenetic signatures of environmental factors in children with familial risk for type 1 diabetes"
Supervisor: Dr. Alida Kindt, Institute of Computational Biology at the Helmholtz Zentrum München
Technical University Munich and Ludwig-Maximilians-Universität Munich
Selected Publications
Multi-omics integration in biomedical research - A metabolomics-centric review
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
2021 medRxiv
An Integrated Molecular Atlas of Alzheimer’s Disease.
INTRODUCTION Embedding single-omics disease associations into the wider context of multi-level molecular changes in Alzheimer’s disease (AD) remains one central challenge in AD research. METHODS Results from numerous AD-specific omics studies from AMP-AD, NIAGADS, and other initiatives were integrated into a comprehensive network resource and complemented with molecular associations from large-scale population-based studies to provide a global view on AD. RESULTS We present the AD Atlas, an online resource (www.adatlas.org) integrating over 20 large studies providing disease-relevant information on 20,353 protein-coding genes, 8,615 proteins, 997 metabolites and 31 AD-related phenotypes. Multiple showcases demonstrate the utility of this resource for contextualization of AD research results and subsequent downstream analyses, such as drug repositioning approaches. DISCUSSION By providing a global view on multi-omics results through a user-friendly interface, the AD Atlas enables the formulation of molecular hypotheses and retrieval of clinically relevant insights that can be validated in follow-up analyses or experiments.