Scientific School on Cloud-based Metabolomics Data Analysis and Collaboration
Metabolomics is a well established -omics science whose growth is bringing about new challenges. Systematic studies and integration with other data sources are resulting in ever larger dataset sizes; production applications require superior computational scalability of analysis techniques; complex, multi-step workflows make study reproducibility more challenging.
At the same time, cloud computing technologies are extending their functionality and provide practical solutions for many of these problems.
In this School students will have the opportunity to learn about current topics in metabolomics, with a slant on the integration of cloud computing technologies where they are beneficial to the effectiveness and efficiency of research and analysis work. Top-level lecturers in the field will provide their insight and will be available for the entire duration of the school, with ample opportunity for interaction with the students. Importantly, the School will also include practical sessions where students can put their new knowledge into practice under the guidance of tutors and run analyses using the new PhenoMeNal cloud-based metabolomics platform.
Participants will learn about:
- Tools and techniques to solve computational metabolomics problems
- Standard data processing tools; statistical data analysis; metabolic network analysis
- How to assemble tools into your own workflows
- Galaxy for metabolomics; main data formats; understanding well-known workflows
- How to share your results and data with the research community
- Reproducibility and sharing of analyses and results; MetaboLights; Workflow4Metabolomics
- Using the cloud to perform these tasks
- What is Cloud computing; low-barrier access to computing infrastructure; how to use the cloud to tackle bigger experiments; cloud-based collaboration
Who should attend
The School is targeted at graduate students and early-stage researchers in metabolomics and bioinformatics. Participants should have some metabolomics experience and an interest in computational data analysis. Some scripting experience will be helpful, but not strictly required.
Registration deadline extended to July 15th 2017!
More info at http://cloudmet2017.crs4.it/