Strategies for molecular diagnosis of Rare Disease patients through integrated analysis of multi-omics data
Supervision
Sergi Beltran
CNAG-CRG, 1st Supervisor
Jürgen Cox
MPI, 2nd Supervisor
Objectives
Development and implementation of strategies for the integration of proteomics/metabolomics, genomics and phenomics data from Rare Disease patients, and diagnosis of Rare Disease patients through integrated analysis of multi-omics data.
Methodology
The student will join the Solve-RD Data Analysis Task Force, co-led by S. Beltran and A. Hoischen (Radboud University Medical Center, Nijmegen, NL), and will evaluate existing methods and strategies on the Solve-RD data. The student will design and develop a multi-omics workflow applicable to several diseases and the analysis of the metabolomics and proteomics data will be carried out in collaboration with J. Cox (MPI). Finally, the student will collaborate with the RD-Connect GPAP software developers to integrate the approach in the GPAP, including the development of a module to visualize the multi-omics data with metadata, and network information (Reactome and Wiki Pathways).
Required Skills
Required skills:
Good understanding of genetics and genomics
Computer programming basics
Strong command of English
Desirable skills:
Advanced computer programming,
Experience with human genomics data analysis
Understanding of metabolomics
Training in bioinformatics
Knowledge of databases
Expected Results
We expect to obtain strategies evaluated, developed, and implemented for the integration of proteomics/metabolomics, genomics and phenomics data from Rare Disease patients, and the identification of pathogenic genes or molecules proposed for undiagnosed Rare Disease patients through the application of the implemented strategies.
Planned Secondments
Host: MPI (J. Cox), Duration: 1 Month; When: Year 2, Goal: Proteomics data analysis workflows.
Host: NOVO (Mads Grønborg), Duration: 1 Month; When: Year 3, Goal: Multi-omics databases in rare-diseases.
Enrolment in doctoral programs
Ph.D. in Biomedicine from Universitat de Barcelona (UB).
References
1. Matalonga, L., Laurie, S., Papakonstantinou, A., Piscia, D., Mereu, E., Bullich, G., Thompson, R., Horvath, R., Pérez-Jurado, L., Riess, O., Gut, I., van Ommen, G.-J., Lochmüller, H., Beltran, S., Renieri, A., Dursun, A., Matilla-Duenas, A., Cormand, B., Rivolta, C., … Sabater, M. (2020). Improved Diagnosis of Rare Disease Patients through Systematic Detection of Runs of Homozygosity. The Journal of Molecular Diagnostics, 22(9), 1205–1215. https://doi.org/10.1016/j.jmoldx.2020.06.008
2. Tort, F., Ugarteburu, O., Texidó, L., Gea‐Sorlí, S., García‐Villoria, J., Ferrer‐Cortès, X., Arias, Á., Matalonga, L., Gort, L., Ferrer, I., Guitart‐Mampel, M., Garrabou, G., Vaz, F. M., Pristoupilova, A., Rodríguez, M. I. E., Beltran, S., Cardellach, F., Wanders, R. J. A., Fillat, C., … Ribes, A. (2019). Mutations in TIMM50 cause severe mitochondrial dysfunction by targeting key aspects of mitochondrial physiology. Human Mutation, 40(10), 1700–1712. https://doi.org/10.1002/humu.23779
3. Graham, E., Lee, J., Price, M., Tarailo-Graovac, M., Matthews, A., Engelke, U., Tang, J., Kluijtmans, L. A. J., Wevers, R. A., Wasserman, W. W., van Karnebeek, C. D. M., & Mostafavi, S. (2018). Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review. Journal of Inherited Metabolic Disease, 41(3), 435–445. https://doi.org/10.1007/s10545-018-0139-6