Bioinformatics for evolution and ecology

- Firstly, to provide students with a solid foundation of computer knowledge and skills, enabling them to learn and use the bioinformatics tools used more specifically in evolution and ecology.

- Secondly, to make them aware of the need to produce reproducible results, and to introduce them to the key concepts and tools for doing so.

- Thirdly, to get students to work on concrete examples that can be used during their Master's internship and future professional life.

"Knowledge :

- Key linux/bash commands in bioinfo (ls, cp, mv, grep, cut, sort, uniq, sed, ...) ;

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

- Notions of data flow (pipes in bash and R ggplot2) ;

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

- Good bioinformatics practices (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 contribution of a model/program against the existing one);

- Inherent limitations of bioinfo tools (objective function, heuristics).

Know-how :

- Compute cluster: manipulate and search content across multiple files/folders (bash/R) ;

- Computing clusters: connect to a cluster, transfer data to/from the cluster, run jobs;

- Programming: launch repetitive tasks (for loop in bash), define a function and call it on several data sets (R, bash);

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

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

- Reproducibility: use markdown to document scripts."

100% continuous assessment

Hourly volumes :

CM : 0 h

TD: 9 h

TP : 0 h

Field : 0 h

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SPS: 0 h

Seminars: 0 h

Outside UM: 6 h