Bioinformatics for Evolution and Ecology

- First, to provide students with a foundation of computer science knowledge and skills, thereby giving them a solid basis for learning and using the bioinformatics tools specifically applied in evolution and ecology.

- Second, to raise their awareness of the need to produce reproducible results and introduce them to the key concepts and tools for doing so.

- Third, have students work on concrete examples that they can draw upon during their master's internship and in their future careers.

"Knowledge:

- Key Linux/Bash commands used in bioinformatics (ls, cp, mv, grep, cut, sort, uniq, sed, …) ;

- Key concepts of imperative programming (variables, loops, functions, basic data structures);

- Basics of data flow (pipes in Bash and R ggplot2);

- Key concepts related to computing clusters (organizing a cluster, using a cluster);

- Best practices in bioinformatics (comments, indentation, variable/function naming, FAIR data, basic use of Git and MD, "test-first approach");

- Key concepts for testing and validating programs and models ("training/validation/test sets," measuring the value of a model or program compared to existing solutions);

- Limitations inherent in bioinformatics tools (objective function, heuristics).

Expertise:

- Computing cluster: manipulate and search for content in numerous files and folders (bash/R);

- Computing cluster: connect to a cluster, transfer data to and from the cluster, run "jobs";

- Programming: running repetitive tasks (for loop in Bash), defining a function and calling it on multiple datasets (R, Bash);

- Programming: chaining commands (bash/R-ggplot2);

- Reproducibility: knowing how to create a Git repository and use it to manage multiple versions of an R/bash script;

- Reproducibility: Use Markdown to document scripts."

Comprehensive ongoing assessment: 100%

Hourly volumes:

CM: 12:00 a.m.

TD: 9 a.m.

Practical Training: 0 hours

Land: 0 h

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SPS: 12:00 a.m.

Seminars: 0 hours

Outside UM: 6 hours