# 3. Gene prediction

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Mise en ligne : 05 février 2015
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## Descriptif

3.1. All genes end on a stop codon

3.2. A simple algorithm for gene prediction

3.3. Searching for start and stop codons

3.4. Predicting all the genes in a sequence

3.5. Making the predictions more reliable

3.6. Boyer-Moore algorithm

3.7. Index and suffix trees

3.8. Probabilistic methods

3.9. Benchmarking the prediction methods

3.10. Gene prediction in eukaryotic genomes

## Vidéos

Vidéo pédagogique
00:05:41

### 3.1. All genes end on a stop codon

Rechenmann
François

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

Vidéo pédagogique
00:05:13

### 3.2. A simple algorithm for gene prediction

Rechenmann
François

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

Vidéo pédagogique
00:04:45

### 3.3. Searching for start and stop codons

Rechenmann
François

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

Vidéo pédagogique
00:06:22

### 3.4. Predicting all the genes in a sequence

Rechenmann
François

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

Vidéo pédagogique
00:04:45

### 3.5. Making the predictions more reliable

Rechenmann
François

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

Vidéo pédagogique
00:05:58

### 3.6. Boyer-Moore algorithm

Rechenmann
François

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

Vidéo pédagogique
00:07:06

### 3.7. Index and suffix trees

Rechenmann
François

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

Vidéo pédagogique
00:06:09

### 3.8. Probabilistic methods

Rechenmann
François

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

Vidéo pédagogique
00:05:35

### 3.9. Benchmarking the prediction methods

Rechenmann
François

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

Vidéo pédagogique
00:08:56

### 3.10. Gene prediction in eukaryotic genomes

Rechenmann
François

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

## Intervenants et intervenantes

### Rechenmann François

France

Ingénieur. Auteur d'une thèse de docteur-ingénieur en sciences appliquées (Grenoble INPG, 1976). - HDR. Directeur de thèse à Grenoble INPG (1990-1994-) et à l'université de Grenoble 1. Directeur de recherche au centre Inria Grenoble – Rhône-Alpes (2002, 2015)