Topic: Omics

For our next CDSL webinar we will have a guest lecture by Dr. Julio Saez-Rodriguez from the Institute for Computational Biomedicine, Heidelberg.

Bio: Julio Saez-Rodriguez is Professor of Medical Bioinformatics and Data Analysis at the Faculty of Medicine of Heidelberg University, director of the Institute for Computational Biomedicine, group leader of the EMBL-Heidelberg University Molecular Medicine Partnership Unit and a co-director of the DREAM challenges. He holds a PhD (2007) in Chemical Engineering. He was a postdoctoral fellow at Harvard Medical School and M.I.T (2007- 2010), group leader at EMBL-EBI, Cambridge (2010-2015), and professor of Computational Biomedicine at RWTH Aachen (2015-2018). His research focuses on computational methods to understand and treat the deregulation of cellular networks in disease (www.saezlab.org).

Abstract: Multi-omics technologies, and in particular those with single-cell and spatial resolution, provide unique opportunities to study deregulation of intra- and inter-cellular processes in cancer and other diseases. In this talk I will present recent methods and applications from our group towards this aim, with a  focus is on computational approaches that combine data with biological knowledge.

Metabolomic and multi-omic data are increasingly being collected in basic, preclinical, and clinical research studies. Interpretation of these data though remains challenging.  Common challenges include the difficulty in identifying metabolites and assigning unique identifiers, and the scarcity of resources that provide up-to-data comprehensive annotations and analysis tools on integrated genes/proteins and metabolites. To aid in interpreting these complex data, we developed RaMP-DB 2.0, a public resource that contains comprehensive biological, structural/chemical, disease, and ontology annotations for human metabolites and metabolic genes/proteins. The associated RaMP-DB 2.0 framework provides the ability to query those annotations and to perform pathway and chemical enrichment analysis on input multi-omic datasets. Since our first release, RaMP-DB 2.0 has been substantially upgraded and now includes an expanded breadth and depth of functional and chemical annotations, and a reproducible and semi-automated method for entity resolution of analytes across the different source databases pulled.  The usability of the RaMP-DB 2.0 has also been improved through updates of pathway and chemical enrichment analysis methods, and a completely revamped web interface and associated public API for programmatic access. RaMP-DB 2.0 currently pulls information from HMDB, KEGG (through HMDB), Reactome, WikiPathways, Lipid-MAPS, and ChEBI and includes 254,860 chemical structures, of which 43,338 are lipids, 15,389 genes, 53,745 pathways, 807,362 metabolic enzyme/metabolite reactions, and 699 functional ontologies (biofluid, health condition, etc.).  RaMP-DB 2.0 is available at https://rampdb.nih.gov/.

Speaker:

Ewy Mathé, Ph.D., Director of Informatics, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH