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3.10. Gene prediction in eukaryotic genomes
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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 sameon a eukaryotic genome, the probability is very very very highthat it is on an intergenic region. Indeed if you take the exampleof the human genome, less than 5% of the sequences of a human genome are made up of genes, 95 % of the humangenomes are not genes. What are they? This isstill an open question. Years ago a biologist spoke about germDNA to say, well DNA which is useless. Now the feeling is somewhat different,it certainly has a reason to exist. We understand some of thesereasons but not all.
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3.6. Boyer-Moore algorithm
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3.1. All genes end on a stop codon
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3.9. Benchmarking the prediction methods
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3.4. Predicting all the genes in a sequence
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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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3.5. Making the predictions more reliable
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3.8. Probabilistic methods
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Avec les mêmes intervenants et intervenantes
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1.8. Compressing the DNA walk
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2.7. The algorithm design trade-off
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3.4. Predicting all the genes in a sequence
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4.6. A path is optimal if all its sub-paths are optimal
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2.1. The sequence as a model of DNA
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2.9. Whole genome sequencing
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3.7. Index and suffix trees
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4.3. Measuring sequence similarity
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5.3. Building an array of distances
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1.6. GC and AT contents of DNA sequence
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