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. 2012 Nov 5;7(11):e48124. doi: 10.1371/journal.pone.0048124

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© 2012 Dib, Carbone

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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Distance trees describe distinct pairs of blocks , thought to belong to two fixed positions in an alignment. Minimal subtrees where correlation between blocks is not perfect, that is , are highlighted in blue. In ABC, trees have the same topology and are characterized by a different labelling of the leaves with the same pairs of words. We call the trees on the top and the trees on the bottom. A: the correlation score of is greater than the correlation score of and this because for perfect correlation is disrupted on a larger subtree. B: both trees display scores of comparison and correlation . Notice that the method does not distinguish the existence of a single mutational event transforming NJ in EF and KL in GH (top) versus a double mutational event realizing the same transformation (bottom). C: the size of subtrees displaying no perfect correlation has an impact in the correlation score of the full tree. displays a greater correlation score than , while scores of comparison are the same.

Inline graphic — Distance trees describe distinct pairs of blocks , thought to belong to two fixed positions in an alignment. Minimal subtrees where correlation between blocks is not perfect, that is , are highlighted in blue. In ABC, trees have the same topology and are characterized by a different labelling of the leaves with the same pairs of words. We call the trees on the top and the trees on the bottom. A: the correlation score of is greater than the correlation score of and this because for perfect correlation is disrupted on a larger subtree. B: both trees display scores of comparison and correlation . Notice that the method does not distinguish the existence of a single mutational event transforming NJ in EF and KL in GH (top) versus a double mutational event realizing the same transformation (bottom). C: the size of subtrees displaying no perfect correlation has an impact in the correlation score of the full tree. displays a greater correlation score than , while scores of comparison are the same.