Marine metagenomics is the study of genetic material recovered directly from the sea. It is a new and rapidly expanding area of research, and there is a danger that data is produced faster than users are able to share, analyze and interpret it. This workshop will provide marine microbiologists, who are embarking upon biodiversity and metagenomics research projects, training to use publicly available resources to manage, share, analyse and interpret amplicon based and metagenomics data and conduct metagenomics studies at large. Delegates will gain hands-on practice of using a range of data resources and tools, interspersed with lectures. The course will provide an opportunity for discussion of the major challenges in the field.
This course is aimed at marine biologists embarking upon new projects in the field of metagenomics research. The course assumes basic experience in sequence data analysis. Some of the tools to be introduced on the course will require the use of Linux/Unix commands. Although a pre-workshop session will be dedicated to basic Unix, we strongly recommend that you familiarize yourself with these before the workshop.
During this course you will learn about:
- Data generation: Next Generation Sequencing, amplicon-based approaches (ribosomal RNA)
- Data submission: ENA, SRA
- Data analysis: The Metagenomics pipelines such asQIIME
- Data retrieval, analysis and interpretation
After this course you should be able to:
- Identify marker gene and metagenomics data with the minimum standards required for submission to comply with the Genomic Standards Consortium (GSC)
- Discuss the merits and drawbacks of using a range of amplicon based (marker genes) and metagenomics tools including the EBI metagenomics portal
- Submit marker genes metagenomics data to public repositories
- Use marine metagenomics pipelines
- Interpret results and compare them with other metagenomics datasets
- Use databases specific to marine metagenomics.
- Evaluates tools and pipelines for metagenomics analyses
- Interrogate marine metagenomics datasets