Problem input tree t with each leaf labeled by an m

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Unformatted text preview: (n = # species, m = #characters) directly instead of using distance matrix. –  GOAL: determine what character strings at internal nodes would best explain the character strings for the n observed species Character- Based Tree ReconstrucJon •  For example, the “characters” may be nucleoJdes, where A, G, C, T are the states of each character. •  By seing the length of an edge in the tree to the Hamming distance, we may define the parsimony score of the tree as the sum of the lengths (weights) of the edges Parsimony and Tree ReconstrucJon Parsimony Approach to EvoluJonary Tree ReconstrucJon •  Applies Occam’s razor principle to idenJfy the simplest explanaJon for the data •  Assumes observed character differences resulted from the fewest possible mutaJons •  Seeks the tree that yields lowest possible parsimony score - sum of cost of all mutaJons found in the tree Small Parsimony Problem •  Input: Tree T with each leaf labeled by an m- character string. •  Output: Labeling of internal verJces of the tree T minimizing the parsimony score. •  We can assume that every leaf is labeled by...
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This note was uploaded on 02/10/2014 for the course CS 425 taught by Professor Asaben-hur during the Fall '13 term at Colorado State.

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