Notice
2.1. The sequence as a model of DNA
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Descriptif
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 of DNA. Is the sequence a good model of DNA? Well, object, it's organized in chromosomes,plasmids, segments and so on. It is compacted within the cellin the nucleus, in the case of eukaryotic cells or directly in the cytoplasm in the case of bacterial cells orprokaryotic cells. This molecule may also be alteredthrough interaction with all the molecules, for example, mutilationand this may be important for the role of DNA into the cell. So again, is the sequence, asequence of characters, which is our computer science subject,is the sequence of characters a good model of DNA whichis a biophysical object? What is a good model? An exact and complete model, certainly not, no model is an exact representation of its object.
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2.3. The genetic code
RechenmannFrançoisGenes 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.
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2.6. Algorithms + data structures = programs
RechenmannFrançoisBy 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,
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2.9. Whole genome sequencing
RechenmannFrançoisSequencing 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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2.4. A translation algorithm
RechenmannFrançoisWe 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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2.7. The algorithm design trade-off
RechenmannFrançoisWe 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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2.2. Genes: from Mendel to molecular biology
RechenmannFrançoisThe 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
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2.10. How to find genes?
RechenmannFrançoisGetting the sequence of the genome is only the beginning, as I explained, once you have the sequence what you want to do is to locate the gene, to predict the function of the gene and maybe study the
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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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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
Avec les mêmes intervenants et intervenantes
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1.7. DNA walk
RechenmannFrançoisWe 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
RechenmannFrançoisWe 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
RechenmannFrançoisWe 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
RechenmannFrançoisWe 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
RechenmannFrançoisWe 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
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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2.9. Whole genome sequencing
RechenmannFrançoisSequencing 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
RechenmannFrançoisWe 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
RechenmannFrançoisWe 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
RechenmannFrançoisWhen 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
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,
