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1.10. Overlapping sliding window
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We have made some drawings along a genomic sequence. And we have seen that although the algorithm is quite simple, even if some points of the algorithmare bit trickier than the others, we were able to produce an interesting result that is a prediction of the origin of replication of bacterial genomes. We have seen also that it may work for a large part of bacterial genomes but for some of them it doesn't work and this is the real life of bio informaticians. We have to deal with that. But, our algorithm was very visual. Now, we want to have a more quantitative approach to make apparent the bias in G, C or A, T and so on. Not only on the visual basis buton a more quantitative basis. Let's see how we can do that. We will change the algorithm a little bit. We are already familiar withthe notion of window. So, we know how to compute nucleotidefrequencies in a sliding window. Frequencies or number ofoccurrences, it's the same thing. So, what we will do is this newversion of the algorithm is we have a window onthe genomic sequence. On this window, we are able to compute the number of G and C. This should be very easy for you right now.
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1.5. Counting nucleotides
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1.8. Compressing the DNA walk
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1.3. DNA codes for genetic information
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1.6. GC and AT contents of DNA sequence
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1.1. The cell, atom of the living world
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1.9. Predicting the origin of DNA replication?
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1.4. What is an algorithm?
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1.7. DNA walk
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1.2. At the heart of the cell: the DNA macromolecule
RechenmannFrançoisDuring the last session, we saw how at the heart of the cell there's DNA in the nucleus, sometimes of cells, or directly in the cytoplasm of the bacteria. The DNA is what we call a macromolecule, that
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1.2. At the heart of the cell: the DNA macromolecule
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2.1. The sequence as a model of DNA
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3.7. Index and suffix trees
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5.2. The tree, an abstract object
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1.5. Counting nucleotides
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2.5. Implementing the genetic code
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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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4.8. A recursive algorithm
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5.6. The diversity of bioinformatics algorithms
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