Notice
4.5. A sequence alignment as a path
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Comparing two sequences and thenmeasuring their similarities is an optimization problem. Why? Because we have seen thatwe have to take into account substitution and deletion. During the alignment, the comparison of the two sequences, we haveto insert blank characters at a certain position in order tohave an optimal score that is we want the sequence to be themore similar as possible. So the problem is to find whereto locate the blank character. There are many solutions and wewant to find the best one, it is an optimization problem. How do we deal with thisoptimization problem? We will consider an alignmentbetween two sequences as a path in that kind of grid. Here we havethe characters of the first sequence and there, the charactersof the second sequence. The path starts from this nodeand must arrive on that node. What are the rules of the game? You may have unitary path likethat or like that or like that, along, horizontal or vertical linesor diagonal lines and so on. The idea is to arrive to the lastnode and we'll see that the "S" corresponds to one alignment,one possible alignment between these two sequences. Why? Simply because drawing a diagonallike that means taking into account a substitution. Thisis the first sequence.
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4.2. Why gene/protein sequences may be similar?
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4.6. A path is optimal if all its sub-paths are optimal
RechenmannFrançoisA sequence alignment between two sequences is a path in a grid. So that, an optimal sequence alignmentis an optimal path in the same grid. We'll see now that a property of this optimal path provides
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4.9. Recursion can be avoided: an iterative version
RechenmannFrançoisWe have written a recursive function to compute the optimal path that is an optimal alignment between two sequences. Here all the examples I gave were onDNA sequences, four letter alphabet. OK. The
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4.3. Measuring sequence similarity
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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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4.4. Aligning sequences is an optimization problem
RechenmannFrançoisWe have seen a nice and a quitesimple solution for measuring the similarity between two sequences. It relied on the so-called hammingdistance that is counting the number of differencesbetween two
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4.8. A recursive algorithm
RechenmannFrançoisWe have seen how we can computethe optimal cost, the ending node of our grid if we know the optimal cost of the three adjacent nodes. This is this computation scheme we can see here using the notation
Avec les mêmes intervenants et intervenantes
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4.9. Recursion can be avoided: an iterative version
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1.7. DNA walk
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2.6. Algorithms + data structures = programs
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3.3. Searching for start and stop codons
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5.2. The tree, an abstract object
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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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3.6. Boyer-Moore algorithm
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4.4. Aligning sequences is an optimization problem
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5.6. The diversity of bioinformatics algorithms
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