Author: Masaki Tagashira
Title: PhyloFold: Precise and Swift Prediction of RNA Secondary Structures to Incorporate Phylogeny among Homologs Document date: 2020_3_6
ID: l72x4wn3_6
Snippet: Let STA RNA , f e STA be a set of all possible alignments STA RNA and the free energy (or inverse of the score) of the alignment STA, respectively. Assume that the probability of any alignment STA ∈ STA RNA , p STA , obeys a Boltzmann (probability) distribution, i.e. p STA = exp(−βf e STA ) Z where Z = STA exp(−βf e STA ) and β is a positive real number. Z is called a partition function. β scales any energy f e STA . Let P PA RNA be the.....
Document: Let STA RNA , f e STA be a set of all possible alignments STA RNA and the free energy (or inverse of the score) of the alignment STA, respectively. Assume that the probability of any alignment STA ∈ STA RNA , p STA , obeys a Boltzmann (probability) distribution, i.e. p STA = exp(−βf e STA ) Z where Z = STA exp(−βf e STA ) and β is a positive real number. Z is called a partition function. β scales any energy f e STA . Let P PA RNA be the pair alignment probability matrix given the pair RNA. Let I PA STAijkl be 1 if the pairs (i, j), (k, l) are pair-aligned in the alignment STA and 0 otherwise, where i, j are two positions in the sequence RN A, k, l are two positions in the other sequence RN A , and i < j, k < l. The matrix P PA RNA can be written by the probabilities p STA :
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