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le (4m29s)

1.6. GC and AT contents of DNA sequence

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 cantake as a value an integer. One, two, three minus five and so on. We have the sequence of characters we want to interpret, declare as a character string oflengths and define. Then we have the initializationof our different variables. This symbol is a symbol for assignment, it means that zero becomes the value of total nb, nbT and soon and so on and here we say: index takes the value one. It means that we position at the beginning of ...
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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 (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 (9m8s)

1.9. Predicting the origin of DNA replication?

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 biological results. Again, this is the aim of bioinformatics: analysing the genomic texts and providing biological results. So, you remember that we had to deal with the problem of the screen size and for that, we decided to change the first version of the algorithm and we introduced a window of fixed length. And we get this algorithm. So, in this algorithm, we repeat the analysis within the window and ...
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le (7m30s)

1.10. Overlapping sliding window

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 ...
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le (5m42s)

2.1. The sequence as a model of DNA

Welcome back to our course on genomes and algorithms that is a computer analysis ofgenetic information.Last week we introduced the very basic concept in biology that is cell, DNA, genome, genes,proteins and this week we will concentrate on these last two concepts, that is genes and proteins and we will see how proteins are coded by genes and what is the process of translating genes into proteins. Then we will design some algorithms for this translation. But first let's come back on the idea that the sequence, that is the string of characters returning these four letters of the alphabet A, C, G and T is a model ...
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le (4m59s)

2.2. Genes: from Mendel to molecular biology

The notion of gene emerged withthe works of Gregor Mendel. Mendel studied the inheritance on some traits like the shape of pea plant seeds,through generations. He stated the famous laws of inheritance which, by the way, were rediscovered 50 years later. The important thing here tounderline is that these concepts of inheritance of genes and so on were very abstract. No physical supports ofthese genes were clarified. So, it's something which appearedlater through molecular biology. We now know that genes are thoseregions of DNA which code the information used by the cell to produce proteins. And, this is what Francis Crick stated as the central dogma of molecular biology. One gene ...
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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 (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 (5m48s)

2.6. Algorithms + data structures = programs

By writing the Lookup GeneticCode Function, we completed our translation algorithm. So we may ask the question about the algorithm, does it terminate? Andthe answer is yes, obviously. Is it pertinent, that is, doesit return the expected answer? The answer is yes, if you giveas an input a sequence of DNA, you will get as an output asequence of amino acids unless, of course, one of the tripletsis not one of the 64 expected triplets and then you will get, ofcourse, a nonsense protein sequence. Is it efficient? Well, for measuring the efficiency of an algorithm, you can ask the question, how manybasic operations you have to execute. In ...
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