Environmental Genomics 2018

This training initiative in Environmental Genomics at the MDI Biological Laboratory seeks to guide research towards understanding how gene function is influenced by environmental conditions while accounting for variation that exists within and among natural populations. Two significant advances create opportunities to finally link gene-environment interactions to the fitness of individuals and to population-level outcomes affecting biodiversity and ecosystem functioning:

• Genome and transcriptome sequences are now available for a growing number of species whose ecology and physiology are well understood.
• Sophisticated tools for high-throughput biology, statistical analysis and informatics are rapidly becoming more accessible to single investigators.

This course is built on the paradigm that the research field will most effectively grow by training early career environmental scientists to properly design comprehensive, large-scale, experiments enabled by drastically increased sample-throughput and lower costs. Most importantly, the challenges of manipulating and analysing population-level genomics (big) data must be addressed.

Topics covered in seminar and laboratory formats:

• bioinformatics, including sequence analysis workflows and use of R statistical analysis tools
• experimental design for environmental genomics research and laboratory training
• robotic platforms for sample preparation as well as preparing RNA-sequencing libraries.

The course provides a significant introduction and some hands-on real-world project training experience for PhD students and early career scientists, who will acquire sufficient knowledge, and build an important network of experts and peer investigators, to initiate their own environmental genomics study and launch Environmental Scientist careers in academia and industry.

Applicants may be interested in taking the Applied Bioinformatics course immediately prior to this course as introduction and preparation for the Environmental Genomics experience.