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3.2. A simple algorithm for gene prediction
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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 region, that is a gene. OK. Let's see that in more details. So our algorithms start withthe declaration of variable we need, some counter indexes here integer and here I have an array of integer with two columns and as many rows as necessary. A row will allow to record the beginning of the gene as a position in the sequence and the end ofthe gene as another position in the sequence normally, this number mustof course be greater than this one. They are integers because theyare positioned in the sequence. We initialized some index andthen what do we have to do?
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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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3.4. Predicting all the genes in a sequence
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3.7. Index and suffix trees
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3.1. All genes end on a stop codon
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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
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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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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
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3.8. Probabilistic methods
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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
Avec les mêmes intervenants et intervenantes
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1.7. DNA walk
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1.2. At the heart of the cell: the DNA macromolecule
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1.10. Overlapping sliding window
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2.3. The genetic code
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3.7. Index and suffix trees
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1.5. Counting nucleotides
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