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## 4.10. How efficient is this algorithm?

We have seen the principle of an iterative algorithm in two paths for aligning and comparing two sequences of characters, here DNA sequences. And we understoodwhy the iterative version is much more efficient than the recursive version. But, how efficient is reallythis iterative algorithm? You remember that in order to measure the efficiency of algorithms, the computer scientists do not use any mean of measuring the time or any other thing. They evaluate the number of timethe main operation inside the algorithm is executed. In the caseof this Needleman and Wunsch algorithm which has been published 40 years ago, the operation which is critical is the comparisonbetween two letters of ... Voir la vidéole (6m59s)

## 4.9. Recursion can be avoided: an iterative version

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 writing of this recursive function is very elegant but unfortunately we will see now that it isnot very efficient in execution time. Let's see why. Remember the computing schema weapply during the recursion, for example here, to compute the cost of this node, we saw that it was required to computerecursively the cost of that node, that node and that node. OK but to compute the cost of that node here, you need to compute the cost ... Voir la vidéole (5m17s)

## 5.1. The tree of life

Welcome to this fifth and last week of our course on genomes and algorithms that is the computer analysis of genetic information. During this week, we will firstsee what phylogenetic trees are and how we can reconstruct these trees from the available data. Then to conclude this week and this course, we will present an overview, a larger overview of bioinformatic algorithms and we will conclude on the application of bioinformatics at least in the microbial world. So first the tree of life, we have already seen that due to the ideas of Darwin, we know that species evolve and the evolution of these species canbe seen as a ... Voir la vidéole (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 ... Voir la vidéole (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 ... Voir la vidéole (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 ... Voir la vidéole (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 ... Voir la vidéole (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 ... Voir la vidéole (7m26s)