Author: Palmer, Duncan S.; Turner, Isaac; Fidler, Sarah; Frater, John; Goedhals, Dominique; Goulder, Philip; Huang, Kuan-Hsiang Gary; Oxenius, Annette; Phillips, Rodney; Shapiro, Roger; Vuuren, Cloete van; McLean, Angela R.; McVean, Gil
Title: Mapping the drivers of within-host pathogen evolution using massive data sets Document date: 2019_7_9
ID: 100r7w2n_5
Snippet: where φ is the empirically estimated probability of observing a single codon change along the lineage joining D to D B , A is the total codon substitution rate out of C i , a i is the number of non-synonymous transitions from C i , k is the number of sequences in D B and π C is the empirical probability of observing C given a ≥ 2 step codon change. We approximate the probability in this manner to avoid computationally expensive matrix exponen.....
Document: where φ is the empirically estimated probability of observing a single codon change along the lineage joining D to D B , A is the total codon substitution rate out of C i , a i is the number of non-synonymous transitions from C i , k is the number of sequences in D B and π C is the empirical probability of observing C given a ≥ 2 step codon change. We approximate the probability in this manner to avoid computationally expensive matrix exponentiation. Given recombination probabilities and codon transition probabilities for all possible codon changes and the Li and Stephens approximation, we are armed with an HMM to describe an approximation to the process generating members of D. We can therefore use the forwards and backwards algorithms to integrate over all paths through D B to generate each member of D in turn. We then take the product over all members of D to approximate the likelihood:
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