Bioinformatics, Genomes and Algorithms

Course description

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. 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.

Course objectives

• Understand the fundamental concepts involved in genomics
• Acquire basic algorithmic skills related to genetic information processing

Who is this course for?

This course is intended for anyone wishing to learn about computer analysis of genetic information and acquire basic knowledge at the interface between genomics and algorithmics.

Course outline

• Week 1: Genomic texts
• Week 2: Genes and proteines
• Week 3: Gene prediction
• Week 4: Sequences comparison
• Week 5: Phylogenetic trees

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.

Pedagogical team

Authors:

• François Rechenmann, bioinformatics researcher, Inria.
• Thierry Parmentelat, research engineer, Inria.

Pedagogical support:

• Christelle Mariais, learning engineer, Inria Learning Lab.
• Isabelle Rey, learning engineer, Inria Learning Lab.

Partners

This MOOC was produced by Inria, in partnership with the UNIT foundation, as part of the IDEFI uTOP project - Multi-partner Open Technology University - contract PIA ANR-11-IDEFI-0037.