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Additionally, because the dynamic programming focuses on a large number of equivalent residues, this method tends to align two proteins by a larger domain, which is normally the main domain in a DSCO pair.
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Then, at every split state, the number of dimensions of the dynamic program will be multiplied by the maximum gap length, because the dynamic program must keep track of scores for each possible sequence position (up to the maximum gap length) that could be mapped to that state.
Because we are stopping the dynamic programming early to generate seeds, the global best alignment may contain multiple seeds and in practice this will tend to be the case for long alignments.
Here, the run-time analysis is restricted to the run time of the dynamic programming part because all methods share the same tree-decomposition algorithm.
Loops are problematic because their presence can prevent the propagation of the dynamic programming algorithm to all the contigs of a locus.
Furthermore, because union is invariant with respect to repetition of points, the dynamic programming is allowed to be redundant, or equivalently the grammar is allowed to be ambiguous.
This is mainly because the tree decomposition requires most of the computing time and the benefits of the dynamic programming cannot show to advantage in this case.
The dynamic programming corrects some errors during the pitch tracking.
The dynamic programming recursion formula is shown in Eqs.
The dynamic programming algorithm has two assumptions.
The dynamic programming algorithm works as follows.
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