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{"TS"},
15 \code{"TV"}, \code{"JC69"}, \code{"K80"} (the default),
16 \code{"F81"}, \code{"K81"}, \code{"F84"}, \code{"BH87"},
17 \code{"T92"}, \code{"TN93"}, \code{"GG95"}, \code{"logdet"}, or
19 \item{variance}{a logical indicating whether to compute the variances
20 of the distances; defaults to \code{FALSE} so the variances are not
22 \item{gamma}{a value for the gamma parameter which is possibly used to
23 apply a gamma correction to the distances (by default \code{gamma =
24 FALSE} so no correction is applied).}
25 \item{pairwise.deletion}{a logical indicating whether to delete the
26 sites with missing data in a pairwise way. The default is to delete
27 the sites with at least one missing data for all sequences.}
28 \item{base.freq}{the base frequencies to be used in the computations
29 (if applicable, i.e. if \code{method = "F84"}). By default, the
30 base frequencies are computed from the whole sample of sequences.}
31 \item{as.matrix}{a logical indicating whether to return the results as
32 a matrix. The default is to return an object of class
36 This function computes a matrix of pairwise distances from DNA
37 sequences using a model of DNA evolution. Eleven substitution models
38 (and the raw distance) are currently available.
41 The molecular evolutionary models available through the option
42 \code{model} have been extensively described in the literature. A
43 brief description is given below; more details can be found in the
47 \item{\code{raw}, \code{N}: }{This is simply the proportion or the number of
48 sites that differ between each pair of sequences. This may be useful
49 to draw ``saturation plots''. The options \code{variance} and
50 \code{gamma} have no effect, but \code{pairwise.deletion} can.}
52 \item{\code{TS}, \code{TV}: }{These are the numbers of transitions and
53 transversions, respectively.}
55 \item{\code{JC69}: }{This model was developed by Jukes and Cantor (1969). It
56 assumes that all substitutions (i.e. a change of a base by another
57 one) have the same probability. This probability is the same for all
58 sites along the DNA sequence. This last assumption can be relaxed by
59 assuming that the substition rate varies among site following a
60 gamma distribution which parameter must be given by the user. By
61 default, no gamma correction is applied. Another assumption is that
62 the base frequencies are balanced and thus equal to 0.25.}
64 \item{\code{K80}: }{The distance derived by Kimura (1980), sometimes referred
65 to as ``Kimura's 2-parameters distance'', has the same underlying
66 assumptions than the Jukes--Cantor distance except that two kinds of
67 substitutions are considered: transitions (A <-> G, C <-> T), and
68 transversions (A <-> C, A <-> T, C <-> G, G <-> T). They are assumed
69 to have different probabilities. A transition is the substitution of
70 a purine (C, T) by another one, or the substitution of a pyrimidine
71 (A, G) by another one. A transversion is the substitution of a
72 purine by a pyrimidine, or vice-versa. Both transition and
73 transversion rates are the same for all sites along the DNA
74 sequence. Jin and Nei (1990) modified the Kimura model to allow for
75 variation among sites following a gamma distribution. Like for the
76 Jukes--Cantor model, the gamma parameter must be given by the
77 user. By default, no gamma correction is applied.}
79 \item{\code{F81}: }{Felsenstein (1981) generalized the Jukes--Cantor model
80 by relaxing the assumption of equal base frequencies. The formulae
81 used in this function were taken from McGuire et al. (1999)}.
83 \item{\code{K81}: }{Kimura (1981) generalized his model (Kimura 1980) by
84 assuming different rates for two kinds of transversions: A <-> C and
85 G <-> T on one side, and A <-> T and C <-> G on the other. This is
86 what Kimura called his ``three substitution types model'' (3ST), and
87 is sometimes referred to as ``Kimura's 3-parameters distance''}.
89 \item{\code{F84}: }{This model generalizes K80 by relaxing the assumption
90 of equal base frequencies. It was first introduced by Felsenstein in
91 1984 in Phylip, and is fully described by Felsenstein and Churchill
92 (1996). The formulae used in this function were taken from McGuire
95 \item{\code{BH87}: }{Barry and Hartigan (1987) developed a distance based
96 on the observed proportions of changes among the four bases. This
97 distance is not symmetric.}
99 \item{\code{T92}: }{Tamura (1992) generalized the Kimura model by relaxing
100 the assumption of equal base frequencies. This is done by taking
101 into account the bias in G+C content in the sequences. The
102 substitution rates are assumed to be the same for all sites along
105 \item{\code{TN93}: }{Tamura and Nei (1993) developed a model which assumes
106 distinct rates for both kinds of transition (A <-> G versus C <->
107 T), and transversions. The base frequencies are not assumed to be
108 equal and are estimated from the data. A gamma correction of the
109 inter-site variation in substitution rates is possible.}
111 \item{\code{GG95}: }{Galtier and Gouy (1995) introduced a model where the
112 G+C content may change through time. Different rates are assumed for
113 transitons and transversions.}
115 \item{\code{logdet}: }{The Log-Det distance, developed by Lockhart et
116 al. (1994), is related to BH87. However, this distance is
117 symmetric. Formulae from Gu and Li (1996) are used.
118 \code{dist.logdet} in \pkg{phangorn} uses a different
119 implementation that gives substantially different distances for
120 low-diverging sequences.}
122 \item{\code{paralin}: }{Lake (1994) developed the paralinear distance which
123 can be viewed as another variant of the Barry--Hartigan distance.}
126 an object of class \link[stats]{dist} (by default), or a numeric
127 matrix if \code{as.matrix = TRUE}. If \code{model = "BH87"}, a numeric
128 matrix is returned because the Barry--Hartigan distance is not
131 If \code{variance = TRUE} an attribute called \code{"variance"} is
132 given to the returned object.
135 Barry, D. and Hartigan, J. A. (1987) Asynchronous distance between
136 homologous DNA sequences. \emph{Biometrics}, \bold{43}, 261--276.
138 Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a
139 maximum likelihood approach. \emph{Journal of Molecular Evolution},
142 Felsenstein, J. and Churchill, G. A. (1996) A Hidden Markov model
143 approach to variation among sites in rate of evolution.
144 \emph{Molecular Biology and Evolution}, \bold{13}, 93--104.
146 Galtier, N. and Gouy, M. (1995) Inferring phylogenies from DNA
147 sequences of unequal base compositions. \emph{Proceedings of the
148 National Academy of Sciences USA}, \bold{92}, 11317--11321.
150 Gu, X. and Li, W.-H. (1996) Bias-corrected paralinear and LogDet
151 distances and tests of molecular clocks and phylogenies under
152 nonstationary nucleotide frequencies. \emph{Molecular Biology and
153 Evolution}, \bold{13}, 1375--1383.
155 Jukes, T. H. and Cantor, C. R. (1969) Evolution of protein
156 molecules. in \emph{Mammalian Protein Metabolism}, ed. Munro, H. N.,
157 pp. 21--132, New York: Academic Press.
159 Kimura, M. (1980) A simple method for estimating evolutionary rates of
160 base substitutions through comparative studies of nucleotide
161 sequences. \emph{Journal of Molecular Evolution}, \bold{16}, 111--120.
163 Kimura, M. (1981) Estimation of evolutionary distances between
164 homologous nucleotide sequences. \emph{Proceedings of the National
165 Academy of Sciences USA}, \bold{78}, 454--458.
167 Jin, L. and Nei, M. (1990) Limitations of the evolutionary parsimony
168 method of phylogenetic analysis. \emph{Molecular Biology and
169 Evolution}, \bold{7}, 82--102.
171 Lake, J. A. (1994) Reconstructing evolutionary trees from DNA and
172 protein sequences: paralinear distances. \emph{Proceedings of the
173 National Academy of Sciences USA}, \bold{91}, 1455--1459.
175 Lockhart, P. J., Steel, M. A., Hendy, M. D. and Penny, D. (1994)
176 Recovering evolutionary trees under a more realistic model of sequence
177 evolution. \emph{Molecular Biology and Evolution}, \bold{11},
180 McGuire, G., Prentice, M. J. and Wright, F. (1999). Improved error
181 bounds for genetic distances from DNA sequences. \emph{Biometrics},
182 \bold{55}, 1064--1070.
184 Tamura, K. (1992) Estimation of the number of nucleotide substitutions
185 when there are strong transition-transversion and G + C-content
186 biases. \emph{Molecular Biology and Evolution}, \bold{9}, 678--687.
188 Tamura, K. and Nei, M. (1993) Estimation of the number of nucleotide
189 substitutions in the control region of mitochondrial DNA in humans and
190 chimpanzees. \emph{Molecular Biology and Evolution}, \bold{10}, 512--526.
192 \author{Emmanuel Paradis}
194 \code{\link{read.GenBank}}, \code{\link{read.dna}},
195 \code{\link{write.dna}}, \code{\link{DNAbin}},
196 \code{\link{dist.gene}}, \code{\link{cophenetic.phylo}},
197 \code{\link[stats]{dist}}
200 \keyword{multivariate}