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; this must be
11 of class \code{"DNAbin"} (use \code{\link{as.DNAbin}} is they are
12 stored as character).}
13 \item{model}{a character string specifying the evlutionary model to be
14 used; must be one of \code{"raw"}, \code{"N"}, \code{"JC69"},
15 \code{"K80"} (the default), \code{"F81"}, \code{"K81"},
16 \code{"F84"}, \code{"BH87"}, \code{"T92"}, \code{"TN93"},
17 \code{"GG95"}, \code{"logdet"}, or \code{"paralin"}.}
18 \item{variance}{a logical indicating whether to compute the variances
19 of the distances; defaults to \code{FALSE} so the variances are not
21 \item{gamma}{a value for the gamma parameter which is possibly used to
22 apply a gamma correction to the distances (by default \code{gamma =
23 FALSE} so no correction is applied).}
24 \item{pairwise.deletion}{a logical indicating whether to delete the
25 sites with missing data in a pairwise way. The default is to delete
26 the sites with at least one missing data for all sequences.}
27 \item{base.freq}{the base frequencies to be used in the computations
28 (if applicable, i.e. if \code{method = "F84"}). By default, the
29 base frequencies are computed from the whole sample of sequences.}
30 \item{as.matrix}{a logical indicating whether to return the results as
31 a matrix. The default is to return an object of class
35 This function computes a matrix of pairwise distances from DNA
36 sequences using a model of DNA evolution. Eleven substitution models
37 (and the raw distance) are currently available.
40 The molecular evolutionary models available through the option
41 \code{model} have been extensively described in the literature. A
42 brief description is given below; more details can be found in the
46 \item{``raw'', ``N''}{This is simply the proportion or the number of
47 sites that differ between each pair of sequences. This may be useful
48 to draw ``saturation plots''. The options \code{variance} and
49 \code{gamma} have no effect, but \code{pairwise.deletion} can.}
51 \item{``JC69''}{This model was developed by Jukes and Cantor (1969). It
52 assumes that all substitutions (i.e. a change of a base by another
53 one) have the same probability. This probability is the same for all
54 sites along the DNA sequence. This last assumption can be relaxed by
55 assuming that the substition rate varies among site following a
56 gamma distribution which parameter must be given by the user. By
57 default, no gamma correction is applied. Another assumption is that
58 the base frequencies are balanced and thus equal to 0.25.}
60 \item{``K80''}{The distance derived by Kimura (1980), sometimes referred
61 to as ``Kimura's 2-parameters distance'', has the same underlying
62 assumptions than the Jukes--Cantor distance except that two kinds of
63 substitutions are considered: transitions (A <-> G, C <-> T), and
64 transversions (A <-> C, A <-> T, C <-> G, G <-> T). They are assumed
65 to have different probabilities. A transition is the substitution of
66 a purine (C, T) by another one, or the substitution of a pyrimidine
67 (A, G) by another one. A transversion is the substitution of a
68 purine by a pyrimidine, or vice-versa. Both transition and
69 transversion rates are the same for all sites along the DNA
70 sequence. Jin and Nei (1990) modified the Kimura model to allow for
71 variation among sites following a gamma distribution. Like for the
72 Jukes--Cantor model, the gamma parameter must be given by the
73 user. By default, no gamma correction is applied.}
75 \item{``F81''}{Felsenstein (1981) generalized the Jukes--Cantor model
76 by relaxing the assumption of equal base frequencies. The formulae
77 used in this function were taken from McGuire et al. (1999)}.
79 \item{``K81''}{Kimura (1981) generalized his model (Kimura 1980) by
80 assuming different rates for two kinds of transversions: A <-> C and
81 G <-> T on one side, and A <-> T and C <-> G on the other. This is
82 what Kimura called his ``three substitution types model'' (3ST), and
83 is sometimes referred to as ``Kimura's 3-parameters distance''}.
85 \item{``F84''}{This model generalizes K80 by relaxing the assumption
86 of equal base frequencies. It was first introduced by Felsenstein in
87 1984 in Phylip, and is fully described by Felsenstein and Churchill
88 (1996). The formulae used in this function were taken from McGuire
91 \item{``BH87''}{Barry and Hartigan (1987) developed a distance based
92 on the observed proportions of changes among the four bases. This
93 distance is not symmetric.}
95 \item{``T92''}{Tamura (1992) generalized the Kimura model by relaxing
96 the assumption of equal base frequencies. This is done by taking
97 into account the bias in G+C content in the sequences. The
98 substitution rates are assumed to be the same for all sites along
101 \item{``TN93''}{Tamura and Nei (1993) developed a model which assumes
102 distinct rates for both kinds of transition (A <-> G versus C <->
103 T), and transversions. The base frequencies are not assumed to be
104 equal and are estimated from the data. A gamma correction of the
105 inter-site variation in substitution rates is possible.}
107 \item{``GG95''}{Galtier and Gouy (1995) introduced a model where the
108 G+C content may change through time. Different rates are assumed for
109 transitons and transversions.}
111 \item{``logdet''}{The Log-Det distance, developed by Lockhart et
112 al. (1994), is related to BH87. However, this distance is symmetric.}
114 \item{``paralin''}{Lake (1994) developed the paralinear distance which
115 can be viewed as another variant of the Barry--Hartigan distance.}
118 an object of class \link[stats]{dist} (by default), or a numeric
119 matrix if \code{as.matrix = TRUE}. If \code{model = "BH87"}, a numeric
120 matrix is returned because the Barry--Hartigan distance is not
123 If \code{variance = TRUE} an attribute called \code{"variance"} is
124 given to the returned object.
127 Barry, D. and Hartigan, J. A. (1987) Asynchronous distance between
128 homologous DNA sequences. \emph{Biometrics}, \bold{43}, 261--276.
130 Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a
131 maximum likelihood approach. \emph{Journal of Molecular Evolution},
134 Felsenstein, J. and Churchill, G. A. (1996) A Hidden Markov model
135 approach to variation among sites in rate of evolution.
136 \emph{Molecular Biology and Evolution}, \bold{13}, 93--104.
138 Galtier, N. and Gouy, M. (1995) Inferring phylogenies from DNA
139 sequences of unequal base compositions. \emph{Proceedings of the
140 National Academy of Sciences USA}, \bold{92}, 11317--11321.
142 Jukes, T. H. and Cantor, C. R. (1969) Evolution of protein
143 molecules. in \emph{Mammalian Protein Metabolism}, ed. Munro, H. N.,
144 pp. 21--132, New York: Academic Press.
146 Kimura, M. (1980) A simple method for estimating evolutionary rates of
147 base substitutions through comparative studies of nucleotide
148 sequences. \emph{Journal of Molecular Evolution}, \bold{16}, 111--120.
150 Kimura, M. (1981) Estimation of evolutionary distances between
151 homologous nucleotide sequences. \emph{Proceedings of the National
152 Academy of Sciences USA}, \bold{78}, 454--458.
154 Jin, L. and Nei, M. (1990) Limitations of the evolutionary parsimony
155 method of phylogenetic analysis. \emph{Molecular Biology and
156 Evolution}, \bold{7}, 82--102.
158 Lake, J. A. (1994) Reconstructing evolutionary trees from DNA and
159 protein sequences: paralinear distances. \emph{Proceedings of the
160 National Academy of Sciences USA}, \bold{91}, 1455--1459.
162 Lockhart, P. J., Steel, M. A., Hendy, M. D. and Penny, D. (1994)
163 Recovering evolutionary trees under a more realistic model of sequence
164 evolution. \emph{Molecular Biology and Evolution}, \bold{11},
167 McGuire, G., Prentice, M. J. and Wright, F. (1999). Improved error
168 bounds for genetic distances from DNA sequences. \emph{Biometrics},
169 \bold{55}, 1064--1070.
171 Tamura, K. (1992) Estimation of the number of nucleotide substitutions
172 when there are strong transition-transversion and G + C-content
173 biases. \emph{Molecular Biology and Evolution}, \bold{9}, 678--687.
175 Tamura, K. and Nei, M. (1993) Estimation of the number of nucleotide
176 substitutions in the control region of mitochondrial DNA in humans and
177 chimpanzees. \emph{Molecular Biology and Evolution}, \bold{10}, 512--526.
179 \author{Emmanuel Paradis \email{Emmanuel.Paradis@mpl.ird.fr}}
181 \code{\link{read.GenBank}}, \code{\link{read.dna}},
182 \code{\link{write.dna}}, \code{\link{DNAbin}},
183 \code{\link{dist.gene}}, \code{\link{cophenetic.phylo}},
184 \code{\link[stats]{dist}}
187 \keyword{multivariate}