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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 (8m57s)

3.10. Gene prediction in eukaryotic genomes

If it is possible to have verygood predictions for bacterial genes, it's certainly not the caseyet for eukaryotic genomes. Eukaryotic cells have manydifferences in comparison to prokaryotic cells. You rememberthe existence of a nucleus and you also remember on one ofthe schemes in the first week that there are more structureswithin a eukaryotic cell. But the differences lie also inthe organization of the genomes. In eukaryotic genomes, the so-calledintergenic regions are very long. Intergenic regions are theregions which separate genes. A bacterial genome is very denseindeed, if you put your fingers somewhere on the genome, if itwas possible of course, it would be on the gene. If you do the ...
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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 (9m27s)

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 ...
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