Developing bioinformatic tools for Multi-omic data integration
Project Details
Program
BioScience
Field of Study
Biosciences, bioinformatics
Division
Biological and Environmental Sciences and Engineering
Faculty Lab Link
Project Description
The past two decades have provided unprecedented growth in data modalities and data availability within the biomedical research. As a result, methodologies for the multi-omic analysis of bulk (average profiles derived from hundreds to millions of cells), single-cell data or both are in continuous development.
Our team has been involved in such developments working on multi-omic frameworks (STATegra framework + STATEgRa Bioconductor package), multi-omic DeepLearning based analysis (LIBRA - BioarXiv) and specific tools such as GeneSetCluster to summarize information derived from gene-set enrichment analysis.
We are looking for highly motivated and skilled visiting students to work on one or several of the following challenges sub-projects:
- Upgrading STATegRA Bioconductor package to account for the novel developments, including the single-cell analysis applications and the integration with GeneSetCluster analysis tool.
- Novel Deep Learning-based methodologies for multi-omic analysis.
- Implementing Cell-to-cell interaction analysis as part of a multi-omic integrative framework. We have existing proprietary data to work in the context of the Bone Marrow.
For any selected student, the project to be conducted will be decided based on the student's interest, technical proficiency, and level of study.
We expect for any participant (a) to bring motivation, enthusiasm, creativity, and hard work, (b) give lab seminars on your work, (c) collaborate with other lab members, and (d) produce a final written report.
References of interest:
- STATegra framework: https://www.frontiersin.org/articles/10.3389/fgene.2021.620453/abstract
- STATegRa package: https://www.bioconductor.org/packages/release/bioc/html/STATegRa.html
- LIBRA single-cell multi-omic framework: https://www.biorxiv.org/content/10.1101/2021.01.27.428400v1
Link to recent publications of the team: http://www.lunacab.org/publications/
About the Researcher
David Gomez-Cabrero
Associate Professor, Bioscience
Affiliations
Education Profile
- Postdoctoral Fellow, Karolinska Institutet, 2009-2014.
- Ph.D. Mathematics, Universitat de ValA¨ncia, 2005-2009.
- Advances Ph.D. Courses in Operations Research and Statistics, Universitat de ValA¨ncia, 2000-2003.
- MSc Statistics, 2000-2004.MSc and BSc Mathematics, 1996-2000.
Research Interests
a€‹Professor Gomez-Cabrero is a bioinformatician and computational biologist. His research focuses on the development of system-based methodologies for understanding biological systems and its translational applications for understanding health and disease.A Upon completing his Ph.D., he moved to Stockholm as postdoctoral research. Next, he was appointed Assistant Professor at Karolinska Institutet between 2014 and 2018. Next, he was appointed as Senior Lecturer at King's College London (2016-2018). During 2017 he became the head of the Translational Bioinformatics Unit at Navarrabiomed (Pamplona, Spain). His current goals are the developing of robust statistically-based methodologies and frameworks to uncover robust hypothesis in (possibly under-powered) research studies. Tools for multi-omic integration are being implemented and shared in the Bioconductor STATegRa package. Nowadays, the aim is to develop strategies for current integrative challenges such as ""integrating bulk and single-cell data"" and ""integrating multi-omic single-cell data"". Most of Professor Gomez-Cabrero's research is conducted in a translational context, especially working in Multiple Sclerosis, Rheumatoid Arthritis, rare diseases and Myelodysplastic syndromes, among others.Selected Publications
- Carr VR, Witherden E, Lee S, Shoaie S, Mullany P, Proctor GB, et al. Abundance and diversity of resistomes differ between healthy human oral cavities and gut. Nature Communications; 2020; 11:693. link
- Gomez-Cabrero D, Tarazona S, FerreirA³s-Vidal I, Ramirez RN, Company C, Schmidt A, et al. STATegra: a comprehensive multi-omics dataset of B-cell differentiation in mouse. Scientific Data; 2019, to appear; 587477. doi:10.1101/587477
- International Multiple Sclerosis Genetics Consortium, Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility, Science, 2019, 365:6460.
- FerreirA³s-Vidal I, Carroll T, Zhang T, Lagani V, Ramirez RN, Ing-Simmons E et al. Feedforward regulation of Myc coordinates lineage-specific with housekeeping gene expression during B cell progenitor cell differentiation. PLOS Biol. 2019;17.
- Ewing E, Kular L, Fernandes SJ, Karathanasis N, Lagani V, Ruhrmann S, et al. Combining evidence from four immune cell types identifies DNA methylation patterns that i
Desired Project Deliverables
Enhancing critical thinking, presentations skills, and scientific writing.
The research, in collaboration and with support of team members, may lead to scientific publications.
Obtaining a hands-on perspective at the frontier of bioinformatics, multi-omic analysis, and its applications in an interdisciplinary research group and environment.
Recommended Student Background
Completed a BSc on Biological or Quantitative background.
Conducting or completed MSc Biotechnology, MSc Bioinformatics, MSc Computer Sciences or similar.
Any student interested in the project with high motivation and skills and most welcome to apply.
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Be part of the journey with VSRP
3-6 months
Internship period
100+
Research Projects
3.5/4
Cumulative GPA
310
Interns a Year