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le (7m40s)

5.5. Differences are not always what they look like

The algorithm we have presented works on an array of distance between sequences. These distances are evaluated on the basis of differences between the sequences. The problem is that behind the differences we observed on the set of sequences, there may beother mutations which cannot be observed and we should modify the distances. We will have a look at some simple cases of these observed differences which may correspond to hidden differences and then we will see how the evaluation, computationof the number of differences may be affected. The simple case is this one, aunique substitution between, in the sequence One we have a Cand it turns out that in ...
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le (4m46s)

5.2. The tree, an abstract object

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. Here is what we call an edge or a branch. We have nodes, a particular nodeis the root and other nodes are the leaves here terminal nodesand we see that when we draw a tree as an abstract object, we put the root upside and the leaves downside so it's the reverse of a classical natural tree. We need an expression to describe a tree and we will use this kind of expression, how ...
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le (4m50s)

5.3. Building an array of distances

So using the sequences of homologous gene between several species, our aim is to reconstruct phylogenetic tree of the corresponding species. For this, we have to comparesequences and compute distances between these sequences and we have seen last week how we were able to measure the similarity between sequences and we can use this similarity as a measureof distance between sequences. So we will compare pairs of sequences, measure the similarity and store the value of distance, of similarity into what we could call a matrix or an array. Before going further, let's makemore explicit the use of these two terms, they are not equivalentbut some people mix them. The ...
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le (5m0s)

5.4. The UPGMA algorithm

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 reconstructing phylogenetic trees. The name of this algorithm israther complicated but the method itself is rather simple,too simple indeed. We will see that. The name standsfor Unweighted Pair Group Method with Arithmetic Mean, wewill understand these terms along the presentationof the algorithm. The algorithm starts withan array of distances. Let's take this very simpleexample, it implies seven species and here we have the values of thedistances between these different sequences associated with a species. As you ...
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le (8m30s)

5.6. The diversity of bioinformatics algorithms

In this course, we have seen a very little set of bioinformatic algorithms. There exist numerous various algorithms in bioinformatics which deal with a large span of classes of problems. For example, read assembly. We have seen how NGS sequencers produce large sets of reads, small sequences which overlap. And the problem of assembly isto use the overlap in order to ordering this read and reconstructing the whole genomic sequence. This is the overlapping and you see that you can use this overlap to get a longer sequence. Of course, here the example issimple: you have to imagine a set of millions of reads to beassembled into genomic sequences of millions or ...
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le (7m26s)

5.7. The application domains in microbiology

Bioinformatics relies on many domains of mathematics and computer science. Of course, algorithms themselves on character strings are important in bioinformatics, we have seen them. Algorithms and trees, for example,for reconstructing phylogenetic trees, algorithms on networks toreconstruct gene interaction networks, metabolic networks and maybe to simulate the dynamics of the time. We have seen also the implicationof probability and statistics. The implication of optimizationmethods, for example, for the computation of the optimalalignment of a pair of sequences. Constraint satisfaction is used forpredicting molecule structure. Automata and formal grammarswhich are some exotic parts of computer science are also usefulin bioinformatics, the same for signal processing. And soother domains may be listed here. We also ...
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