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1.1. The cell, atom of the living world
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Descriptif
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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1.9. 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.
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1.7. DNA walk
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1.2. At the heart of the cell: the DNA macromolecule
RechenmannFrançoisDuring the last session, we saw how at the heart of the cell there's DNA in the nucleus, sometimes of cells, or directly in the cytoplasm of the bacteria. The DNA is what we call a macromolecule, that
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1.10. Overlapping sliding window
RechenmannFrançoisWe have made some drawings along a genomic sequence. And we have seen that although the algorithm is quite simple, even if some points of the algorithmare bit trickier than the others, we were able to
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1.5. Counting nucleotides
RechenmannFrançoisIn this session, don't panic. We will design our first algorithm. This algorithm is forcounting nucleotides. The idea here is that as an input,you have a sequence of nucleotides, of bases, of letters,
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1.8. Compressing the DNA walk
RechenmannFrançoisWe have written the algorithm for the circle DNA walk. Just a precision here: the kind of drawing we get has nothing to do with the physical drawing of the DNA molecule. It is a symbolic
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1.3. DNA codes for genetic information
RechenmannFrançoisRemember at the heart of any cell,there is this very long molecule which is called a macromolecule for this reason, which is the DNA molecule. Now we will see that DNA molecules support what is called
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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
Avec les mêmes intervenants et intervenantes
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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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2.5. Implementing the genetic code
RechenmannFrançoisRemember we were designing our translation algorithm and since we are a bit lazy, we decided to make the hypothesis that there was the adequate function forimplementing the genetic code. It's now time
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3.2. A simple algorithm for gene prediction
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3.10. Gene prediction in eukaryotic genomes
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4.8. A recursive algorithm
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5.6. The diversity of bioinformatics algorithms
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1.9. Predicting the origin of DNA replication?
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2.8. DNA sequencing
RechenmannFrançoisDuring the last session, I explained several times how it was important to increase the efficiency of sequences processing algorithm because sequences arevery long and there are large volumes of
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3.5. Making the predictions more reliable
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4.6. A path is optimal if all its sub-paths are optimal
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5.1. The tree of life
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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.