Bioinformatics for evolution and ecology

- First, give students a foundation of computer knowledge and skills, providing them with a solid basis for learning and using bioinformatics tools used more specifically in evolution and ecology.

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

- Thirdly, have students work on concrete examples that they can reuse during their master's internship and in their future professional life.

Knowledge:

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

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

- Data flow concepts (pipes in bash and R ggplot2);

- Key concepts related to computing clusters (organization of a cluster, use of a cluster);

- Best practices in bioinformatics (comments, indentation, naming variables/functions, 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 contribution of a model/program compared to the existing one);

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

Expertise:

- Computing cluster: manipulating and searching content in numerous files/folders (bash/R);

- Computing cluster: connect to a cluster, transfer data to/from the cluster, launch "jobs";

- Programming: launching repetitive tasks (for loop in bash), defining a function and calling it on multiple data sets (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.

Continuous assessment: 100%

Hourly volumes:

CM: 0 hours

Tutorial: 9 a.m.

Practical work: 0 hours

Field: 0 hours

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SPS: 0 hours

Seminars: 0 hours

Outside UM: 6 hours