26-27 October 2016.
Course duration: 2 days (approximately 11 hours of lectures and 3 hours of practicals).
Registration will start at 9.00am on the first day, the course will finish by 4.15pm on the final day.
Professor Mika Ala-Korpela (course organiser), Professor Debbie Lawlor, Dr Peter Würtz, Dr Pasi Soininen, Antti Kangas, Qin Wang.
This course is an introductory course on omics sciences, particularly quantitative metabolomics, and their current and future role in epidemiology and medicine. It is aimed at familiarising participants with the basis and applications and integration of various omics disciplines. The focus will be in quantitative metabolomics, but a brief introduction, e.g., to genomics and transcriptomics will be given in the light of integration of multiple omics data sets and domains in systems epidemiology research. An overview on experimental issues as well as data characterisation and analyses is given with respect to key quantitative NMR and mass spec metabolomics methodologies. The focus is on the role of metabolomics and its applications, but in general systems epidemiology thinking remains on the background with an emphasis on understanding the relation between various omics spaces and the importance of their integration in modern epidemiology. The concept of “big data” will be introduced in relation to systems epidemiology and medicine. The role and concept of biobanking will be introduced and linked with electronic health records. Several exemplars of the above mentioned concepts are illustrated in real epidemiological research applications, including genetic background of systemic metabolism (so-called genome-wide association study approach) and how genetics, metabolomics and big data can enhance drug development.
By the end of the course participants should be able to:
Invited University of Bristol staff, postgraduate students and selected collaborators who are interested in applying omics sciences in epidemiology. Participants are required to have a basic background in medical statistics and epidemiology, and knowledge of the basics of R software.
Overview on metabolomics in epidemiology.
Metabolomics among the other omics’.
Metabolic profiling in epidemiology.
Key quantitative metabolomics methodologies in epidemiology.
The role of data visualisation for understanding complex data.
Recent metabolomics highlights from scientific literature.
Basics of using R in epidemiological metabolomics.
Recommended reading: It would be helpful for attendees to familiarise themselves with the following papers in advance of the course:
Please note: The following software will be used on the course - R (https://www.r-project.org); version 3.3.1 or newer. This course will be held in a computer lab, you will not need to bring a laptop.
Please note bookings for this course have now closed.
Very good practical tips - great insights. I REALLY enjoyed the visualization and the R sessions, as they provided some hands-on materials.
Course feedback, October 2016
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