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1.10. Overlapping sliding window
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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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1.6. GC and AT contents of DNA sequence
RECHENMANN François
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?
RECHENMANN François
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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1.7. DNA walk
RECHENMANN François
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
RECHENMANN 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
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1.5. Counting nucleotides
RECHENMANN François
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
RECHENMANN François
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
RECHENMANN François
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
Avec les mêmes intervenants et intervenantes
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1.7. DNA walk
RECHENMANN François
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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1.2. At the heart of the cell: the DNA macromolecule
RECHENMANN 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
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2.1. The sequence as a model of DNA
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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
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2.9. Whole genome sequencing
RECHENMANN François
Sequencing is anexponential technology. The progresses in this technologyallow now to a sequence whole genome, complete genome. What does it mean? Well let'stake two examples: some twenty years ago,
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3.7. Index and suffix trees
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We have seen with the Boyer-Moore algorithm how we can increase the efficiency of spin searching through the pre-processing of the pattern to be searched. Now we will see that an alternative way of
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4.4. Aligning sequences is an optimization problem
RECHENMANN François
We have seen a nice and a quitesimple solution for measuring the similarity between two sequences. It relied on the so-called hammingdistance that is counting the number of differencesbetween two
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5.2. The tree, an abstract object
RECHENMANN François
When we speak of trees, of species,of phylogenetic trees, of course, it's a metaphoric view of a real tree. Our trees are abstract objects. Here is a tree and the different components of this tree.
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1.5. Counting nucleotides
RECHENMANN François
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,
