3 \title{Pairwise Distances from DNA Sequences}
5 dist.dna(x, model = "K80", variance = FALSE,
6 gamma = FALSE, pairwise.deletion = FALSE,
7 base.freq = NULL, as.matrix = FALSE)
10 \item{x}{a matrix or a list containing the DNA sequences.}
11 \item{model}{a character string specifying the evlutionary model to be
12 used; must be one of \code{"raw"}, \code{"N"}, \code{"JC69"},
13 \code{"K80"} (the default), \code{"F81"}, \code{"K81"},
14 \code{"F84"}, \code{"BH87"}, \code{"T92"}, \code{"TN93"},
15 \code{"GG95"}, \code{"logdet"}, or \code{"paralin"}.}
16 \item{variance}{a logical indicating whether to compute the variances
17 of the distances; defaults to \code{FALSE} so the variances are not
19 \item{gamma}{a value for the gamma parameter which is possibly used to
20 apply a gamma correction to the distances (by default \code{gamma =
21 FALSE} so no correction is applied).}
22 \item{pairwise.deletion}{a logical indicating whether to delete the
23 sites with missing data in a pairwise way. The default is to delete
24 the sites with at least one missing data for all sequences.}
25 \item{base.freq}{the base frequencies to be used in the computations
26 (if applicable, i.e. if \code{method = "F84"}). By default, the
27 base frequencies are computed from the whole sample of sequences.}
28 \item{as.matrix}{a logical indicating whether to return the results as
29 a matrix. The default is to return an object of class
33 This function computes a matrix of pairwise distances from DNA
34 sequences using a model of DNA evolution. Eleven substitution models
35 (and the raw distance) are currently available.
38 The molecular evolutionary models available through the option
39 \code{model} have been extensively described in the literature. A
40 brief description is given below; more details can be found in the
44 \item{``raw'', ``N''}{This is simply the proportion or the number of
45 sites that differ between each pair of sequences. This may be useful
46 to draw ``saturation plots''. The options \code{variance} and
47 \code{gamma} have no effect, but \code{pairwise.deletion} can.}
49 \item{``JC69''}{This model was developed by Jukes and Cantor (1969). It
50 assumes that all substitutions (i.e. a change of a base by another
51 one) have the same probability. This probability is the same for all
52 sites along the DNA sequence. This last assumption can be relaxed by
53 assuming that the substition rate varies among site following a
54 gamma distribution which parameter must be given by the user. By
55 default, no gamma correction is applied. Another assumption is that
56 the base frequencies are balanced and thus equal to 0.25.}
58 \item{``K80''}{The distance derived by Kimura (1980), sometimes referred
59 to as ``Kimura's 2-parameters distance'', has the same underlying
60 assumptions than the Jukes--Cantor distance except that two kinds of
61 substitutions are considered: transitions (A <-> G, C <-> T), and
62 transversions (A <-> C, A <-> T, C <-> G, G <-> T). They are assumed
63 to have different probabilities. A transition is the substitution of
64 a purine (C, T) by another one, or the substitution of a pyrimidine
65 (A, G) by another one. A transversion is the substitution of a
66 purine by a pyrimidine, or vice-versa. Both transition and
67 transversion rates are the same for all sites along the DNA
68 sequence. Jin and Nei (1990) modified the Kimura model to allow for
69 variation among sites following a gamma distribution. Like for the
70 Jukes--Cantor model, the gamma parameter must be given by the
71 user. By default, no gamma correction is applied.}
73 \item{``F81''}{Felsenstein (1981) generalized the Jukes--Cantor model
74 by relaxing the assumption of equal base frequencies. The formulae
75 used in this function were taken from McGuire et al. (1999)}.
77 \item{``K81''}{Kimura (1981) generalized his model (Kimura 1980) by
78 assuming different rates for two kinds of transversions: A <-> C and
79 G <-> T on one side, and A <-> T and C <-> G on the other. This is
80 what Kimura called his ``three substitution types model'' (3ST), and
81 is sometimes referred to as ``Kimura's 3-parameters distance''}.
83 \item{``F84''}{This model generalizes K80 by relaxing the assumption
84 of equal base frequencies. It was first introduced by Felsenstein in
85 1984 in Phylip, and is fully described by Felsenstein and Churchill
86 (1996). The formulae used in this function were taken from McGuire
89 \item{``BH87''}{Barry and Hartigan (1987) developed a distance based
90 on the observed proportions of changes among the four bases. This
91 distance is not symmetric.}
93 \item{``T92''}{Tamura (1992) generalized the Kimura model by relaxing
94 the assumption of equal base frequencies. This is done by taking
95 into account the bias in G+C content in the sequences. The
96 substitution rates are assumed to be the same for all sites along
99 \item{``TN93''}{Tamura and Nei (1993) developed a model which assumes
100 distinct rates for both kinds of transition (A <-> G versus C <->
101 T), and transversions. The base frequencies are not assumed to be
102 equal and are estimated from the data. A gamma correction of the
103 inter-site variation in substitution rates is possible.}
105 \item{``GG95''}{Galtier and Gouy (1995) introduced a model where the
106 G+C content may change through time. Different rates are assumed for
107 transitons and transversions.}
109 \item{``logdet''}{The Log-Det distance, developed by Lockhart et
110 al. (1994), is related to BH87. However, this distance is symmetric.}
112 \item{``paralin''}{Lake (1994) developed the paralinear distance which
113 can be viewed as another variant of the Barry--Hartigan distance.}
116 an object of class \link[stats]{dist} (by default), or a numeric
117 matrix if \code{as.matrix = TRUE}. If \code{model = "BH87"}, a numeric
118 matrix is returned because the Barry--Hartigan distance is not
121 If \code{variance = TRUE} an attribute called \code{"variance"} is
122 given to the returned object.
125 Barry, D. and Hartigan, J. A. (1987) Asynchronous distance between
126 homologous DNA sequences. \emph{Biometrics}, \bold{43}, 261--276.
128 Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a
129 maximum likelihood approach. \emph{Journal of Molecular Evolution},
132 Felsenstein, J. and Churchill, G. A. (1996) A Hidden Markov model
133 approach to variation among sites in rate of evolution.
134 \emph{Molecular Biology and Evolution}, \bold{13}, 93--104.
136 Galtier, N. and Gouy, M. (1995) Inferring phylogenies from DNA
137 sequences of unequal base compositions. \emph{Proceedings of the
138 National Academy of Sciences USA}, \bold{92}, 11317--11321.
140 Jukes, T. H. and Cantor, C. R. (1969) Evolution of protein
141 molecules. in \emph{Mammalian Protein Metabolism}, ed. Munro, H. N.,
142 pp. 21--132, New York: Academic Press.
144 Kimura, M. (1980) A simple method for estimating evolutionary rates of
145 base substitutions through comparative studies of nucleotide
146 sequences. \emph{Journal of Molecular Evolution}, \bold{16}, 111--120.
148 Kimura, M. (1981) Estimation of evolutionary distances between
149 homologous nucleotide sequences. \emph{Proceedings of the National
150 Academy of Sciences USA}, \bold{78}, 454--458.
152 Jin, L. and Nei, M. (1990) Limitations of the evolutionary parsimony
153 method of phylogenetic analysis. \emph{Molecular Biology and
154 Evolution}, \bold{7}, 82--102.
156 Lake, J. A. (1994) Reconstructing evolutionary trees from DNA and
157 protein sequences: paralinear distances. \emph{Proceedings of the
158 National Academy of Sciences USA}, \bold{91}, 1455--1459.
160 Lockhart, P. J., Steel, M. A., Hendy, M. D. and Penny, D. (1994)
161 Recovering evolutionary trees under a more realistic model of sequence
162 evolution. \emph{Molecular Biology and Evolution}, \bold{11},
165 McGuire, G., Prentice, M. J. and Wright, F. (1999). Improved error
166 bounds for genetic distances from DNA sequences. \emph{Biometrics},
167 \bold{55}, 1064--1070.
169 Tamura, K. (1992) Estimation of the number of nucleotide substitutions
170 when there are strong transition-transversion and G + C-content
171 biases. \emph{Molecular Biology and Evolution}, \bold{9}, 678--687.
173 Tamura, K. and Nei, M. (1993) Estimation of the number of nucleotide
174 substitutions in the control region of mitochondrial DNA in humans and
175 chimpanzees. \emph{Molecular Biology and Evolution}, \bold{10}, 512--526.
177 \author{Emmanuel Paradis \email{Emmanuel.Paradis@mpl.ird.fr}}
179 \code{\link{read.GenBank}}, \code{\link{read.dna}},
180 \code{\link{write.dna}}, \code{\link{DNAbin}},
181 \code{\link{dist.gene}}, \code{\link{cophenetic.phylo}},
182 \code{\link[stats]{dist}}
185 \keyword{multivariate}