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le (5m51s)

2.5. Implementing the genetic code

Remember 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 to see this lookupfunction but just before that come back on this condition herewhich is a bit more complex than the first attempt in writing the algorithm. Here you see the keyword OR, itmeans that this condition is true if this one is true or thisone is true or this one is true. Why do we need this morecomplex condition? Imagine our sequence and there washere the last triplet we translated. Now we increase our index ...
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le (5m54s)

2.3. The genetic code

Genes code for proteins. What is the correspondence betweenthe genes, DNA sequences, and the structure of proteins? The correspondence isthe genetic code. Proteins have indeedsequences of amino acids. There are 20 amino acidsin the living world. They can be named by a single letter,3 letters or their full name. It means that a protein can berepresented by a sequence of letters in a 20 letter alphabet. Let's come back again on thiscorrespondence between gene and protein. Genes are regions of DNA. These regions are first transcribedinto RNA and then RNA into proteins. And proteins’ sequences of aminoacids fold into 3D structures. Like here, some helixes. Translation is the process whichgoes from RNA to ...
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le (5m59s)

3.6. Boyer-Moore algorithm

We have seen how we can make gene predictions more reliable through searching for all the patterns,all the occurrences of patterns. We have seen, for example, howif we locate the RBS, Ribosome Binding Site, upstream gene we can make the prediction of the coding sequence more reliable. So it is clear that pattern searching isa central topic in sequence analysis. So let's have a look at searching algorithms for strings or patterns and their performance. First,what we call the naive algorithm. What does it mean? The naive algorithm consists in comparing every letter of the pattern toevery letter of the text, so if N is the length of the ...
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le (6m7s)

1.7. DNA walk

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 the typical, also quite short sequence of DNA, a long text offour letters: A, C, G, T, T and so on. When the first sequence of DNA were obtained, the idea of using computers very quickly emerged but people didn't know exactly what to do with this sequence of characters. Again, there is a meaning behind the sequence because it is genetic information. It means it is the information ...
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le (6m10s)

2.4. A translation algorithm

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 thehypothesis that the genetic codehas been implemented as an array as presented in the lastslide of the previous session. We have seen transcriptions and translationsfrom DNA to RNA and proteins. An important thing to notice hereis that most of the time computer scientists and bioinformaticiansjust forget about RNA. When they speak about translating,they say translating from DNA to proteins directly becausethe differences between the DNA and RNA is only T and U sowhat they do is this ...
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le (6m10s)

3.8. Probabilistic methods

Up to now, to predict our gene,we only rely on the process of searching certain strings or patterns. In order to further improve our gene predictor, the idea is to use, to rely onprobabilistic methods. What does it mean? I will firsttake an example, which is not related to genomic but I think it'sgood to understand the idea. Imagine you have a very long text which is known to be written in some human understandable language but you don't know which one but you know that some passages of this text only are written in a human understandable language,maybe English, maybe French and so on, whatever. You don't know. How ...
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Documentaires

le (6m21s)

La passion du dessin et de la préhistoire

Les archéologues découvrent des objets abandonnés par les Hommes préhistoriques dans les sites qu’ils ont habités ou visités. La plupart de ces objets sont ceux du quotidien : des ustensiles, des armes, des outils en pierre, en os, en bois de cervidés ou en ivoire. Certains d’entre eux sont décorés. D’autres traces ont été laissées par ces Hommes ; elles se sont surtout conservées dans les grottes, protégées des assauts du temps. Il s’agit de dessins, gravures ou peintures exécutés sur les parois et les sols. L’art tel qu’il nous est parvenu aujourd’hui n’apparait qu’avec l’Homme moderne (l’Homo sapiens). Les objets décorés ...
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le (6m23s)

3.4. Predicting all the genes in a sequence

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 different phases and on the two strands. It means that to retrieve all the genes on a genome we have to look on six different sequences, three phases on each strand. Let's looknow how we can deal with this kind of search. First we have to modify a little bit our algorithm so that instead of starting at position One, I want to introduce a variable, a parameter which could be One or Two ...
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le (6m25s)

1.8. Compressing the DNA walk

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 representation. It is a way of representing the information content of the sequence as a drawing. Remember that the problem of the algorithm we designed is that it supposes the capacity of drawing several millions or billions of segments on the screen. This is not feasible. No screen will be large enough for that. So, how can we deal with this hardware constraint? Compression is the answer. Let's see that in more details. Remember, for each ...
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le (6m39s)

4.7. Alignment costs

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 these costs are used to compute the cost in the last node. So again, we saw how we can compute the cost here of the path ending on that node if we know the cost of the sub-path ending on these three red nodes. Indeed, if we come from that node, the cost on that node will be the cost of that node plus the ...
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