PAMn: PAM and Dayhoff matrices calculation

In ge11232002/TKF: Pairwise Distance Estimation with TKF91 and TKF92 Model

Description

These functions calculate the mutation matrix or Dayhoff matrix from the mutation matrix at PAM 1 and base backrgound frequency.

Usage

  PAMn(PAM1, n, method=c("R", "C"))
  Dayhoffn(PAM1, BF, n)

Arguments

PAM1	A matrix of numeric: the mutation probability from one AA to another AA at PAM distance 1. The order of AA in the matrix should be identical to AACharacterSet.
n	A numeric: the PAM distance.
method	The implementation can be in R or C. Usually R is fine. C is for debug.
BF	A numeric vector: the backrgound frequency of AAs. The order of AA in the vector should also be identical to AACharacterSet.

Details

Calculate the n-PAM matrices from PAM1 mutation matrix and n. To compute n-PAM matrices, we multiply the PAM1 matrix through itself N times, which is most efficiently achieved through n additions in log space.

Computing Dayhoff matrices from PAM mutation matrices and AA frequency. Dayhoff matrices are the ratios P("alignment i and j arose through evolution") / P("alignment i and j arose by chance")

Value

A numeric matrix is returned.

Author(s)

Ge Tan

References

Dayhoff, M.O., and Schwartz, R.M. (1978). A model of evolutionary change in proteins. In In Atlas of Protein Sequence and Structure,.

Gonnet, G.H., and Scholl, R. (2009). Scientific Computation (Cambridge, UK; New York: Cambridge University Press).

Examples

  data(GONNET)
  data(GONNETBF)
  ## PAM 250 mutation matrix
  PAM250 <- PAMn(GONNET, 250, method="R")
  
  ## Dayhoff 250 matrix
  Dayhoff250 <- Dayhoffn(GONNET, GONNETBF, 250)
  

ge11232002/TKF documentation built on May 17, 2019, 12:13 a.m.