• End of Registration
  • -
  • Classes Start
  • may 15 2017
  • Classes End
  • jul 02 2017
  • Estimated Effort
  • 2-3 h/week
  • Language
  • English
« This course is available in “Archived Open” mode ": there is no animation of the teaching team (no forum or exercise noted like the quizzes) and the course does not issue any certificate of successful completion or certificate. However, you can access videos and text resources without limitation. This broadcasting mode therefore allows you, while waiting for the opening of a future "session animated ”, to train you by having access to the main content. »

About the Course

In this course, you will discover how computer science supports the interpretation of the text of genomes. Running the adequate programs, a computer may produce predictions on the location of the thousands of genes in a living organism and the functions of the proteins these genes code for.

You are not a biologist? Attending this course, you will be introduced to several entities and processes involved in the interpretation of the genomic texts: cell, chromosome, DNA, genome, genes, transcription, translation, proteins and many more.

You are not a computer scientist? This course is also an introduction to algorithms on character strings: pattern searching, sequence similarity, Markov chain models, or phylogenetic tree reconstruction are some basic algorithms which are implied in genome sequence analysis and will be explained.

You are neither a biologist nor a computer scientist? This course is a great opportunity to a joint approach to genomics and algorithmics, or if you prefer, to algorithmics and genomics.

NEW in this session: thanks to a specific tool (iPython notebooks), you will be able to execute the algorithms presented in the course and evaluate their use on real data sets. If you wish to go further, the iPython notebooks allow you to modify the programs written in Python and you can even code new ones and test them.


A scientific culture will make easier the understanding of the notions studied.

Course Summary

Week 1

Genomic texts

Week 2

Genes and proteins

Week 3

Gene prediction

Week 4

Sequences comparison

Week 5

Phylogenetic trees


Course Staff Image #1


François Rechenmann is a bioinformatics researcher. He was a Research Director at Inria during more than 30 years. He worked in particular in the Ibis team whose research projects focus on computational biology.

Thierry Parmentelat

Thierry Parmentelat's career has been conducted both in the academic and the industrial area. His topics of interest are programming languages, networks and algebra. Currently research engineer at Inria, Thierry uses Python since more than 10 years for his research projects as well as for the development of the platforms he is in charge of.

Course Organisation and Evaluation

The course is structured in 5 weeks.

Each week, new material will be available: around 10 course sequences with a 6 minutes video, quizzes associated to each sequence, course documents and practical exercices at the end of each week.

The algorithms shown in the course will be also presented in Python and executable via the iPython notebooks integrated to the course. Thanks to this tool, you will be able to modify the algorithms and even to write new ones.


logo inria recherche logo investissement d'avenir

This course was produced by Inria as part of the IDEFI uTOP project - Université de Technologie Ouverte Pluri-partenaires - contract PIA ANR-11-IDFI-0037.

Social Network

Twitter : @MoocLabInria #moocbioinfo

Terms of use

Terms of use of the course content
Creative Commons Licence BY-NC-ND : the user must give appropriate credit, he may not use the material for commercial purposes and may not distribute a modified material.

Terms of use of the contents produced by users
Creative Commons Licence BY-NC-ND : the user must give appropriate credit, he may not use the material for commercial purposes and may not distribute a modified material.

Pictures credits:
Video thumbnail: © rolffimages - Fotolia.com
F. Rechenmann picture: © Inria - Vanessa Peregrin