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