MIT6_047f08_rec06 - MIT OpenCourseWare http/

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Unformatted text preview: MIT OpenCourseWare 6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution Fall 2008 For information about citing these materials or our Terms of Use, visit: . 6.047/6.878 Fall 2008- Recitation 6 - Maximum Likelihood October 10, 2008 1 Model of evolution Our goal in developing this maximum likelihood approach is to find the best tree (most likely) that explains our sequence data. To represent a tree, let the leaves of a tree be numbered 1 ,...,n and the ancestral nodes be n + 1 , 2 n − 1 . Let the branches of the tree be numbered by the most recent of the two nodes it touches (e.g. branch i connects node i and parent ( i ) ). For a tree, we have its topology T and the branch times t 1 ,...,t 2 n − 2 , where t i is the time between nodes i and parent ( i ) . Our sequence data can be represented as a matrix x ( n rows, m columns), such that x i,j is the j th character of the i th sequence. We will be given sequence data for the extant (modern) sequences x 1 ,... x n , and will have to integrate over the ancestral sequences x n + 1 ,... x 2n − 1 . Each sequence has length m . With these definitions, our goal in the maximum likelihood method is to solve the following equation arg max P ( x 1 ,..., x n | T, t ) . T, t 1.1 Defining the distributions: factoring by branches Before we can tackle the equation above, we must first define the distributions of our variables. We will make several assumptions about the process of sequence evolution in order to make the math and algorithm tractable. First note that the distribution above is a marginal of the joint distribution over all sequences P ( x 1 ,..., x n | T, t ) = P ( x 1 ,..., x 2n − 1 | T, t ) x n + 1 ,..., x 2n − 1 The first assumption we will make is that...
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This note was uploaded on 09/24/2010 for the course EECS 6.047 / 6. taught by Professor Manoliskellis during the Fall '08 term at MIT.

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MIT6_047f08_rec06 - MIT OpenCourseWare http/

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