Dynamic programming algorithm

The constant is empirically set such that the expected score per position in a random alignment is less than zero. This is done to ensure that the local pairwise dynamic programming algorithm (Smith and Waterman, 1981 Karlin and Altschul, 1990) alignments will nearly always exceed those generated between random sequences. 

The idea of finding the best model was extended by Jones et al. (1994). A dynamic programming algorithm was used to select the most plausible model, and the same authors also presented an ambitious method to predict the three-dimensional structure of the a-helical membrane proteins (Taylor etal., 1994). Finally, HMMs were used to model the overall structure of the membrane topology by two groups of researchers (Sonn-hammer et al., 1998 Tusnady and Simon, 1998). 

Sequence alignment programs use the dynamic programming algorithm to quickly find the best of all possible alignments between two sequences. For... 

Sequence alignment (Chapter 2 of Volume I) is a prime example of a problem in bioinformatics that can be solved efficiently with dynamic programming. Dynamic programming for sequence alignment is also discussed extensively in (Gusfield, 1997 Waterman, 1995). Other examples of dynamic programming algorithms occur in Chapters 3 and 6 of Volume I and Chapter 2 of Volume II. 

Eddy, S. R. (2002) A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. BMC Bioinformatics 3, 18. 

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