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Anglais
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François Rechenmann (Intervention)
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Ces ressources de cours sont, sauf mention contraire, diffusées sous Licence Creative Commons. L’utilisateur doit mentionner le nom de l’auteur, il peut exploiter l’œuvre sauf dans un contexte commercial et il ne peut apporter de modifications à l’œuvre originale.
DOI : 10.60527/5zpe-q744
Citer cette ressource :
François Rechenmann. Inria. (2015, 5 février). 1.10. Overlapping sliding window , in 1. Genomic texts. [Vidéo]. Canal-U. https://doi.org/10.60527/5zpe-q744. (Consultée le 13 juillet 2024)

# 1.10. Overlapping sliding window

Réalisation : 5 février 2015 - Mise en ligne : 9 mai 2017
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Descriptif

We 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 produce an interesting result that is a prediction of the origin of replication of bacterial genomes. We have seen also that it may work for a large part of bacterial genomes but for some of them it doesn't work and this is the real life of bio informaticians. We have to deal with that. But, our algorithm was very visual. Now, we want to have a more quantitative approach to make apparent the bias in G, C or A, T and so on. Not only on the visual basis buton a more quantitative basis. Let's see how we can do that. We will change the algorithm a little bit. We are already familiar withthe notion of window. So, we know how to compute nucleotidefrequencies in a sliding window. Frequencies or number ofoccurrences, it's the same thing. So, what we will do is this newversion of the algorithm is we have a window onthe genomic sequence. On this window, we are able to compute the number of G and C. This should be very easy for you right now.

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Documentation

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### 1.7. DNA walk

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We will now design a more graphical algorithm which is called "the DNA walk". We shall see what does it mean "DNA walk". Walk on to DNA. Something like that, yes. But first, just have a look again at

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### 1.2. At the heart of the cell: the DNA macromolecule

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During 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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### 1.5. Counting nucleotides

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In 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

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We 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

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Remember 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

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We 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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### 1.1. 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

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### 1.9. Predicting the origin of DNA replication?

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We have seen a nice algorithm to draw, let's say, a DNA sequence. We will see that first, we have to correct a little bit this algorithm. And then we will see how such as imple algorithm can provide

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### 1.4. What is an algorithm?

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We 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

• Vidéo pédagogique
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### 1.4. What is an algorithm?

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We 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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### 2.4. A translation algorithm

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We have seen that the genetic codeis a correspondence between the DNA or RNA sequences and aminoacid sequences that is proteins. Our aim here is to design atranslation algorithm, we make the

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### 3.1. All genes end on a stop codon

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Last week we studied genes and proteins and so how genes, portions of DNA, are translated into proteins. We also saw the very fast evolutionof the sequencing technology which allows for producing

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### 3.9. Benchmarking the prediction methods

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It is necessary to underline that gene predictors produce predictions. Predictions mean that you have no guarantees that the coding sequences, the coding regions,the genes you get when applying your

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### 4.2. Why gene/protein sequences may be similar?

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Before measuring the similaritybetween the sequences, it's interesting to answer the question: why gene or protein sequences may be similar? It is indeed veryinteresting because the answer is related

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### 5.4. The UPGMA algorithm

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We know how to fill an array with the values of the distances between sequences, pairs of sequences which are available in the file. This array of distances will be the input of our algorithm for

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### 1.7. DNA walk

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We will now design a more graphical algorithm which is called "the DNA walk". We shall see what does it mean "DNA walk". Walk on to DNA. Something like that, yes. But first, just have a look again at

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### 2.7. The algorithm design trade-off

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We saw how to increase the efficiencyof our algorithm through the introduction of a data structure. Now let's see if we can do even better. We had a table of index and weexplain how the use of these

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### 3.4. Predicting all the genes in a sequence

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We have written an algorithm whichis able to locate potential genes on a sequence but only on one phase because we are looking triplets after triplets. Now remember that the genes maybe located on

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### 4.7. Alignment costs

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We have seen how we can compute the cost of the path ending on the last node of our grid if we know the cost of the sub-path ending on the three adjacent nodes. It is time now to see more deeply why

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### 4.9. Recursion can be avoided: an iterative version

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We have written a recursive function to compute the optimal path that is an optimal alignment between two sequences. Here all the examples I gave were onDNA sequences, four letter alphabet. OK. The

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00:04:52

### 1.2. At the heart of the cell: the DNA macromolecule

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François

During 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