Biodiversity analysis can be broadly defined as the study and interpretation of patterns of species diversity and
ecological functioning as structured by history and geography. In this field, data of a variety of different
types and dimensions need to be integrated and analyzed statistically to be able to meaningfully address relevant
research questions. This course reviews these different types of data and the current methodologies for processing and
analyzing them. The review starts with one-dimensional, sequential data from which at least some relevant insights can
be gained by analyzing its diversity per se: DNA. While we are introducing ourselves to this in plenary form in the
mornings, we will take two afternoons individually to work our way through an e-book about open science, open data and
open source practices. We will then do a hands-on practical in metabarcoding data analysis and conclude the week with
a journal club where we discuss a relevant publication with each other in brief stand-up presentations.
2019-25-11, Sylvius 1.5.03
- Lecture I: Introduction to the course
- Lecture II: Introduction to biodiversity and data science
- Lecture III: Introduction to molecular biodiversity
- Self study / software installs / reading
2019-26-11, Sylvius 1.5.03
- Lecture I: High-throughput DNA sequencing data
- Lecture II: HTS data types and file formats
- Lecture III: Cases of HTS in biodiversity research
- Homework: Open Science, Open Data, Open Source (pdf)
2019-27-11, Sylvius 1.5.03
- Lecture I: Data science tools: UNIX
- Lecture II: The barcoding workflow and MSA data
- Lecture III: Species delimitation
- Homework: Open Science, Open Data, Open Source (cont.) (pdf)
2019-28-11, Sylvius 1.5.03
- Lecture I: Metabarcoding
- Lecture II: environmental quality assessments: DNA WaterScan - Van der Hoorn / Beentjes
- Lecture III: Guest lecture metabarcoding: Kinabalu mycorrhizal diversity - Morgado
- Practical: Hands-on practical step by step data analysis of mycorrhizal diversity - Nuytinck
2019-29-11, Sylvius 1.5.03
- Lecture I: Phylogenetic data
- Lecture II: Megatrees (e.g. algorithms, SQL)
- Lecture III: Wrap-up Open Science, Open Data, Open Source
- Journal club: All students present their selected paper to the class (5 min. + questions)