Bioinformatics: Genomes and Algorithms

Description

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.

Pre-Requisites

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

Course Summary

Part 1 : Genomic texts

Part 2 : Genes and proteins

Part 3 : Gene prediction

Part 4 : Sequences comparison

Part 5 : Phylogenetic trees

The material of this course come from a MOOC delivered on France Université Numérique : https://www.fun-mooc.fr/courses/inria/41007/session01/about

  

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5.1. The tree of life
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7 vidéos

5. Phylogenetic trees

  • RECHENMANN François
Table of contents 5.1. The tree of life 5.2. The tree, an abstract object 5.3. Building an array of distances 5.4. The UPGMA algorithm 5.5. Differences are not always what they look like 5.6. The diversity of bioinformatics algorithms 5.7. The application domains in microbiology
05.02.2015
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4.1. How to predict gene/protein functions?
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10 vidéos

4. Sequences comparison

  • RECHENMANN François
Table of contents 4.1. How to predict gene/protein functions? 4.2. Why gene/protein sequences may be similar? 4.3. Measuring sequence similarity 4.4. Aligning sequences is an optimization problem 4.5. A sequence alignment as a path 4.6. A path is optimal if all its sub-paths are optimal 4.7. Alignment costs 4.8. A recursive algorithm 4.9. Recursion can be avoided: an iterative version 4.10. How efficient is this algorithm?
05.02.2015
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3.1. All genes end on a stop codon
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10 vidéos

3. Gene prediction

  • RECHENMANN François
Table of contents 3.1. All genes end on a stop codon 3.2. A simple algorithm for gene prediction 3.3. Searching for start and stop codons 3.4. Predicting all the genes in a sequence 3.5. Making the predictions more reliable 3.6. Boyer-Moore algorithm 3.7. Index and suffix trees 3.8. Probabilistic methods 3.9. Benchmarking the prediction methods 3.10. Gene prediction in eukaryotic genomes
05.02.2015
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2.1. The sequence as a model of DNA
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10 vidéos

2. Genes and proteins

  • RECHENMANN François
Table of contents 2.1. Genes: from Mendel to molecular biology 2.2. The genetic code 2.3. A translation algorithm 2.4. Implementing the genetic code 2.5. Algorithms + data structures = programs 2.6. The algorithm design trade-off 2.7. DNA sequencing 2.8. Whole genome sequencing 2.9. How to find genes?
05.02.2015
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