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| DATE | 2016-10-25 |
| FROM | Ruben Safir
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| SUBJECT | Subject: [Learn] Phylogenetics educational links
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From learn-bounces-at-nylxs.com Tue Oct 25 21:25:43 2016
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http://telliott99.blogspot.com/2010/03/fitch-and-sankoff-algorithms-for.html
"The Fitch algorithm considers the sites (or characters) one at a time. At each tip in the tree, we create a set containing those nucleotides (states) that are observed or are compatible with the observation. Thus, if we see an A, we create the set {A}. If we see an ambiguity such as R, we create the set {AG}. Now we move down the tree [away from the tips]. In algorithmic terms, we do a postorder tree traversal. At each interior node we create a set that is the intersection of sets at the two descendant nodes. However, if that set is empty, we instead create the set that is the union of the two sets at the descendant nodes. Every time we create such a union, we also count one change of state."
https://en.wikipedia.org/wiki/Non-parametric
Nonparametric statistics are statistics not based on parameterized families of probability distributions. They include both descriptive and inferential statistics. The typical parameters are the mean, variance, etc. Unlike parametric statistics, nonparametric statistics make no assumptions about the probability distributions of the variables being assessed. The difference between parametric models and non-parametric models is that the former has a fixed number of parameters, while the latter grows the number of parameters with the amount of training data.[1] Note that the non-parametric model does, counterintuitively, contain parameters: the distinction is that parameters are determined by the training data in the case of non-parametric statistics, not the model.
https://en.wikipedia.org/wiki/Fitch-Margoliash_algorithm
Distance-matrix methods
Distance-matrix methods of phylogenetic analysis explicitly rely on a measure of "genetic distance" between the sequences being classified, and therefore they require an MSA (multiple sequence alignment) as an input. Distance is often defined as the fraction of mismatches at aligned positions, with gaps either ignored or counted as mismatches.[1] Distance methods attempt to construct an all-to-all matrix from the sequence query set describing the distance between each sequence pair. From this is constructed a phylogenetic tree that places closely related sequences under the same interior node and whose branch lengths closely reproduce the observed distances between sequences. Distance-matrix methods may produce either rooted or unrooted trees, depending on the algorithm used to calculate them. They are frequently used as the basis for progressive and iterative types of multiple sequence alignment. The main disadvantage of distance-matrix methods is their inability to efficiently use information about local high-variation regions that appear across multiple subtrees.[2]
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