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Nombre de programmes trouvés : 1878
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le (5m42s)

3.1. All genes end on a stop codon

Last week we studied genes and proteins and so how genes, portions of DNA, are translated into proteins.We also saw the very fast evolutionof the sequencing technology which allows for producing large genomic texts, it is now possible to sequence a whole genome. But it is just thebeginning of the story. The challenge to come is to analysethe texts of these genomes and find genes, so this week wewill see how we can design avery first algorithm for predicting genes on a bacterial genome. We will first remember the conditionfor finding genes, we will design and propose an algorithmfor that and we will see that a part of ...
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le (5m14s)

3.2. A simple algorithm for gene prediction

Based on the principle we statedin the last session, we will now write in pseudo code a firstalgorithm for locating genes on a bacterial genome. Remember first how this algorithm should work, we first need to find two consecutive stop triplets in the same phase, same phase meansthe number of letters between these two stop triplets might bea multiple of three so that this sequence here can be divided into triplets. This is called an open reading frame. Once we have an open reading framewe look for the start triplet which is situated leftmost onthe open reading frame and we declare, we make the hypothesis that thisis a coding ...
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le (4m46s)

3.3. Searching for start and stop codons

We have written an algorithm for finding genes. But you remember that we arestill to write the two functions for finding the next stop codonand the next start codon. Let's see how we can do that. We are looking for triplets. We use the term triplets as long as wehave no proof that they are codons. You can have triplets outside genes. Within genes, they are called codons. In general, we arelooking for triplets. If you have a sequence like thisone and you are looking for occurrences of this triplet, whatyou have to do is: position your triplet at the beginning of the sequence. Compare the first letter. If it is not ...
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le (6m23s)

3.4. Predicting all the genes in a sequence

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 different phases and on the two strands. It means that to retrieve all the genes on a genome we have to look on six different sequences, three phases on each strand. Let's looknow how we can deal with this kind of search. First we have to modify a little bit our algorithm so that instead of starting at position One, I want to introduce a variable, a parameter which could be One or Two ...
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le (4m46s)

3.5. Making the predictions more reliable

We have got a bacterial gene predictor but the way this predictor works is rather crude and if we want to have more reliable results, we have to inject into this algorithmmore biological knowledge. We will use a notion of RBS, RBS stands for Ribosome Binding Sites. What is it? OK. Let's have a look atthe cell machinery or part of it here. You certainly see here that wedeal with a eukaryotes cell. Why? It's because you have anucleus and you remember that the difference between prokaryoticcell and eukaryotic cell lies n the existence of a nucleus. Within the nucleus you have the DNA. The DNA is transcribed into ...
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le (5m59s)

3.6. Boyer-Moore algorithm

We have seen how we can make gene predictions more reliable through searching for all the patterns,all the occurrences of patterns. We have seen, for example, howif we locate the RBS, Ribosome Binding Site, upstream gene we can make the prediction of the coding sequence more reliable. So it is clear that pattern searching isa central topic in sequence analysis. So let's have a look at searching algorithms for strings or patterns and their performance. First,what we call the naive algorithm. What does it mean? The naive algorithm consists in comparing every letter of the pattern toevery letter of the text, so if N is the length of the ...
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le (7m7s)

3.7. Index and suffix trees

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 improving the performance is to pre-process the text itself,the searchable text itself and we will, for that, study two methods, the construction of indexes of fixed length words and the algorithm which uses prefix trees. An index of fixed lengthword, what does it mean? Imagine you have a text, a searchable text, that is a text in which you want to search a pattern,here is quite a short text, the sequence is 14 correctors. We will ...
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le (6m10s)

3.8. Probabilistic methods

Up to now, to predict our gene,we only rely on the process of searching certain strings or patterns. In order to further improve our gene predictor, the idea is to use, to rely onprobabilistic methods. What does it mean? I will firsttake an example, which is not related to genomic but I think it'sgood to understand the idea. Imagine you have a very long text which is known to be written in some human understandable language but you don't know which one but you know that some passages of this text only are written in a human understandable language,maybe English, maybe French and so on, whatever. You don't know. How ...
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le (5m36s)

3.9. Benchmarking the prediction methods

It is necessary to underline that gene predictors produce predictions. Predictions mean that you have no guarantees that the coding sequences, the coding regions,the genes you get when applying your algorithm, are true genes, thatis genes which have a biological existence. Only experimental analysiscan confirm or infirm your predictions. Nevertheless it is interesting and also important to be able to evaluate your algorithm, thisis the role of benchmarking. Benchmarking means measuring the capacity of your algorithm to produce good predictions. How can we make thiskind of measurement? We need a reference, an idealreference would be a genome which is well annotated and for whichall of the annotations have been confirmed through ...
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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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