This course will provide theoretical and practical aspects to generate and use ‘-omics’ data as well as remote sensing for plant breeding - from sampling to interpreting the results and finding biologically relevant conclusions.
The last years’ rapid technological advancements have enabled genome-scale capturing of biological processes. Analysis of genetic variance and gene expression by Next-generation Sequencing (NGS) as well as protein and metabolite identification by mass spectrometry are today common techniques used in many laboratories. However, combining different types of data and making biological sense out of large datasets remains challenging. The generation of such large datasets - often referred to as ‘-omics’ data - demands partly new considerations for experimental set-ups, sampling, data analysis and visualization. Simultaneously, rapid advancement is undergone within remote sensing and satellite analysis enabling new ways to phenotype plants, which need to be linked to ‘-omics’ and other data-. Applying artificial intelligence (AI) to interpret large datasets another possibility which is rapidly advancing. These methods will have importance in the future development plant breeding and sustainable agriculture and forestry.
This course will provide theoretical and practical aspects to generate and use ‘-omics’ data as well as remote sensing for plant breeding - from sampling to interpreting the results and finding biologically relevant conclusions. The emphasis will be on plant phenomics, genomics, proteomics, metabolomics and microbiomics. Strategies both in outdoor and controlled environments will be covered. Suitable statistical and visualization methods to deal with the variation in this type of data will be presented. Ways of applying AI for data interpretation will be demonstrated.
The overall goal is to point out the possibilities in using ‘-omics’ and AI for plant breeding but also to highlight possible pitfalls.
Download the preliminary course syllabus here.
Download the preliminary schedule here.
Lecturers: Kristina Gruden (NIB Slovenia), Dan Jacobson (ORNL), Antoine Harfouche (UNITUS), Ian Dodd (Lancaster University), Aakash Chawade (SLU), Rodomiro Ortiz (SLU), Annabel Large (ORNL/SLU), Svante Resjö (SLU), Therese Bengtsson (SLU) and Erik Alexandersson (SLU, course leader)
This course is a joint PhD-student course by the SLU Platform Plant Breeding and the Industry Research School of Forest Genetics and Biotechnology.
Pre-requisites: PhD students interested in handling, interpreting and visualizing large-scale data in the context of relatively uncontrolled environments, e.g. field trials, ecosystems. Some experience of handling and analyzing large datasets in R or similar applications is advantageous but not necessary.
What is required to pass: Active participation during lectures, demonstrations and computer practical as well as approved presentation of subject-related article in journal club both orally and in text. Every missed lesson by the student has to be compensated a written summary addressing the subject area and answering one key question based on the lecture slides and related assigned reading (minimum half an A4 page).
Technical requirements: Own laptop.
PhD-course: Accelerating climate resilient plant breeding by applying –omics and artificial intelligence (3 ECTS)
Course syllabus (preliminary)
Course leader: Erik Alexandersson
Lectures: Kristina Gruden (NIB Slovenia), Dan Jacobson (ORNL), Antoine Harfouche (UNITUS), Ian Dodd (Lancaster University), Aakash Chawade (SLU), Rodomiro Ortiz (SLU), Annabel Large (ORNL/SLU), Svane Resjö (SLU), Therese Bengtsson (SLU), Erik Alexandersson (SLU)
Date: 20-24 April 2020
Place: SLU Alnarp
Important to know
The course is mainly for PhD students from SLU and from the Industry Research School of Forest Genetics, Biotechnology and Breeding (UPSC). If there are spare spaces (total 30), both postdocs and young researchers are also welcome. Travel and accommodation for PhD students from SLU will be funded by Platform Plant Breeding.
Last day to register: 2nd of April 2020 (first come first served)