Bioinformatics for evolution and ecology

- Firstly, to give students a base of knowledge and computer skills and thus provide them with a solid foundation for learning and using 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 this.

- Thirdly, to make the students work on concrete examples that can be used during their master's course and their 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 computing 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);

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

Know-How :

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

- Computing cluster: 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: know how to create a git repository and use it to manage several versions of an R/bash script;

- Reproducibility: use markdown to document scripts."

Continuous assessment : 100%.

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