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4.3. Measuring sequence similarity
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So we understand why gene orprotein sequences may be similar. It's because they evolve togetherwith the species and they evolve in time, there aremodifications in the sequence and that the sequence may still besimilar, similar enough again to retrieve information on onesequence to transfer it to another sequence of interest. So thequestion now is how can we measure this similarity between twosequences for the moment. The first approach to similarityis a very simple one is to apply a distance which is calledhere the Editing System or the Hamming Distance.The idea is very basic. You would take two sequences likethese two sequences here and you look at the differences and youcount the number of differences. Here, for example, you have twodifferences so you will say that the distance, the similaritybetween the two sequences, the distance is two. Here wehave another pair of sequences which are less similar becausethey are three differences. That's quite nice, it'sa hamming distance. Is it really a distance? A distance is a mathematicalconcept and to be a distance, it must satisfy three conditions:the distance between a sequence and itself must be zero, a sequencebetween a sequence and another one must be the same betweenthe last one and the first one and we must have this inequalitywhich is always verified.
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4.6. A path is optimal if all its sub-paths are optimal
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4.9. Recursion can be avoided: an iterative version
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4.7. Alignment costs
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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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4.5. A sequence alignment as a path
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4.8. A recursive algorithm
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4.2. Why gene/protein sequences may be similar?
RechenmannFrançoisBefore measuring the similaritybetween the sequences, it's interesting to answer the question: why gene or protein sequences may be similar? It is indeed veryinteresting because the answer is related
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1.10. Overlapping sliding window
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