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1.1. The cell, atom of the living world
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Welcome to this introduction to bioinformatics. We will speak of genomes and algorithms. More specifically, we will see how genetic information can be analysed by algorithms. In these five weeks to come, we will see first, what are these genomic texts, we will try to analyse using algorithms and programs. We will then speak of genes and proteins. Proteins being coded by genes. We will study and design algorithms to predict genes on the DNA sequences or genomic texts. We will study, more deeply, an algorithm to compare genomic sequences. And we will use this algorithm to reconstruct phylogenetic trees that is to say the evolution of species over time. During this first week, we will speak of genomic texts and we will see how algorithms can deal with these texts. First, we will see what most often is called "the atom of the living world" that is the cell. What is a cell? The first scientist who spoke of the cells was Robert Hooke, in 1667. Robert Hooke saw the walls of the cells, not the cell itself because he studied with a microscope of his own design, a very thin slice of cork. And within this slice, what he did see is this very tiny space.Like this or this. And he decided that this tiny space looked like monk cells and then the term cell remained.
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1.7. DNA walk
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1.2. At the heart of the cell: the DNA macromolecule
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1.10. Overlapping sliding window
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1.5. Counting nucleotides
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1.8. Compressing the DNA walk
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1.3. DNA codes for genetic information
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1.6. GC and AT contents of DNA sequence
RechenmannFrançoisWe have designed our first algorithmfor counting nucleotides. Remember, what we have writtenin pseudo code is first declaration of variables. We have several integer variables that are variables which
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1.9. Predicting the origin of DNA replication?
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1.4. What is an algorithm?
RechenmannFrançoisWe have seen that a genomic textcan be indeed a very long sequence of characters. And to interpret this sequence of characters, we will need to use computers. Using computers means writing program.
Avec les mêmes intervenants et intervenantes
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1.7. DNA walk
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2.6. Algorithms + data structures = programs
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3.3. Searching for start and stop codons
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4.1. How to predict gene/protein functions?
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4.10. How efficient is this algorithm?
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5.7. The application domains in microbiology
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1.2. At the heart of the cell: the DNA macromolecule
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1.10. Overlapping sliding window
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2.3. The genetic code
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3.6. Boyer-Moore algorithm
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4.5. A sequence alignment as a path
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5.5. Differences are not always what they look like
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