Notice
3.1. All genes end on a stop codon
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Descriptif
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 a
very 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 the algorithm relies onthe search for patterns over a sequence, so we will see howwe can optimize the algorithm for searching patterns. And thenwe will see how we can compare the efficiency of geneprediction methods. But first let's remember the necessarycondition for finding genes. As we saw last week a gene can onlybe found between two consecutive stop triplets in the same phase,in the same phase means that the two stop, the two tripletsmust be separated by a number of nucleotides which isa multiple of three. Why two consecutive stop triplets?
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3.7. Index and suffix trees
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3.2. A simple algorithm for gene prediction
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3.10. Gene prediction in eukaryotic genomes
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3.5. Making the predictions more reliable
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3.8. Probabilistic methods
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3.3. Searching for start and stop codons
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3.6. Boyer-Moore algorithm
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3.9. Benchmarking the prediction methods
RechenmannFrançoisIt 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
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3.4. Predicting all the genes in a sequence
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1.3. DNA codes for genetic information
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2.1. The sequence as a model of DNA
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3.3. Searching for start and stop codons
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4.1. How to predict gene/protein functions?
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4.10. How efficient is this algorithm?
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5.7. The application domains in microbiology
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