Table Of Contents

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Introduction
	Course Presentation
	Guide: Learning Platform
	Documentation
Week 1: Genomic texts
	1.1. The cell, atom of the living world
	1.2. At the heart of the cell: the DNA macromolecule
	1.3. DNA codes for genetic information
	1.4. What is an algorithm? + Python
	1.5. Counting nucleotides + Python
	1.6. GC and AT contents of DNA sequence
	1.7. DNA walk + Python
	1.8. Compressing the DNA walk
	1.9. Predicting the origin of DNA replication? + Python
	1.10. Overlapping sliding window + Python
	Exercises Week 1 + Python
	Course Documents
Week 2: Genes and proteins
	2.1. The sequence as a model of DNA
	2.2. Genes: from Mendel to molecular biology + Python
	2.3. The genetic code
	2.4. A translation algorithm
	2.5. Implementing the genetic code
	2.6. Algorithms + data structures = programs
	2.7. The algorithm design trade-off
	2.8. DNA sequencing + Python
	2.9. Whole genome sequencing
	2.10. How to find genes?
	Exercises Week 2
	Course Documents
Week 3: Gene prediction
	3.1. All genes end on a stop codon
	3.2. A simple algorithm for gene prediction + Python
	3.3. Searching for start and stop codons + Python
	3.4. Predicting all the genes in a sequence + Python
	3.5. Making the predictions more reliable + Python
	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
	Exercises Week 3 + Python
	Course Documents
Week 4: Sequence comparison
	4.1. How to predict gene/protein functions?
	4.2. Why gene/protein sequences may be similar?
	4.3. Measuring sequence similarity + Python
	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 + Python
	4.9. Recursion can be avoided: an iterative version + Python
	4.10. How efficient is this algorithm?
	Exercises Week 4
	Course Documents
Week 5: Phylogenetic trees
	5.1. The tree of life
	5.2. The tree, an abstract object
	5.3. Building an array of distances + Python
	5.4. The UPGMA algorithm + Python
	5.5. Differences are not always what they look like
	5.6. The diversity of bioinformatics algorithms
	5.7. The application domains in microbiology
	Exercises Week 5
	Course Documents