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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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3.1. 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
RechenmannFrançoisWe 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
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3.7. Index and suffix trees
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3.2. A simple algorithm for gene prediction
RechenmannFrançoisBased 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
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3.5. Making the predictions more reliable
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3.8. Probabilistic methods
RechenmannFrançoisUp 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
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3.3. Searching for start and stop codons
RechenmannFrançoisWe 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
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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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1.6. GC and AT contents of DNA sequence
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3.2. A simple algorithm for gene prediction
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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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