rhizobium {ade4}R Documentation

Genetic structure of two nitrogen fixing bacteria influenced by geographical isolation and host specialization

Description

The data set concerns fixing bacteria belonging to the genus Sinorhizobium (Rhizobiaceae) associated with the plant genus Medicago (Fabaceae). It is a combination of two data sets fully available online from GenBank and published in two recent papers (see reference below). The complete sampling procedure is described in the Additional file 3 of the reference below. We delineated six populations according to geographical origin (France: F, Tunisia Hadjeb: TH, Tunisia Enfidha: TE), the host plant (M. truncatula or similar symbiotic specificity: T, M. laciniata: L), and the taxonomical status of bacteria (S. meliloti: mlt, S. medicae: mdc). Each population will be called hereafter according to the three above criteria, e.g. THLmlt is the population sampled in Tunisia at Hadjeb from M. laciniata nodules which include S. meliloti isolates. S. medicae interacts with M. truncatula while S. meliloti interacts with both M. laciniata (S. meliloti bv. medicaginis) and M. truncatula (S. meliloti bv. meliloti). The numbers of individuals are respectively 46 for FTmdc, 43 for FTmlt, 20 for TETmdc, 24 for TETmlt, 20 for TELmlt, 42 for THTmlt and 20 for THLmlt.

Four different intergenic spacers (IGS), IGSNOD, IGSEXO, IGSGAB, and IGSRKP, distributed on the different replication units of the model strain 1021 of S. meliloti bv. meliloti had been sequenced to characterize each bacterial isolate (DNA extraction and sequencing procedures are described in an additional file). It is noteworthy that the IGSNOD marker is located within the nod gene cluster and that specific alleles at these loci determine the ability of S. meliloti strains to interact with either M. laciniata or M. truncatula.

Usage

data(rhizobium)

Format

rhizobium is a list of 2 components.

  1. dnaobjlist of dna lists. Each dna list corresponds to a locus. For a given locus, the dna list provides the dna sequences The ith sequences of all loci corresponds to the ith individual of the data set.
  2. popThe list of the populations which each individual sequence belongs to.

Source

Pavoine, S. and Bailly, X. (2007) New analysis for consistency among markers in the study of genetic diversity: development and application to the description of bacterial diversity. BMC Evolutionary Biology, 7, e156.

Examples


# The functions used below require the package ape
data(rhizobium)
if (require(ape, quiet = TRUE)) {
dat <- prep.mdpcoa(rhizobium[[1]], rhizobium[[2]], 
    model = c("F84", "F84", "F84", "F81"),
    pairwise.deletion = TRUE)
sam <- dat$sam
dis <- dat$dis
# The distances should be Euclidean. 
# Several transformations exist to render a distance object Euclidean 
# (see functions cailliez, lingoes and quasieuclid in the ade4 package). 
# Here we use the quasieuclid function.
dis <- lapply(dis, quasieuclid)
mdpcoa1 <- mdpcoa(sam, dis, scann = FALSE, nf = 2)

# Reference analysis
plot(mdpcoa1)

# Differences between the loci
kplot(mdpcoa1)

# Alleles projected on the population maps.
kplotX.mdpcoa(mdpcoa1)
}

Worked out examples


> library(ade4)
> ### Name: rhizobium
> ### Title: Genetic structure of two nitrogen fixing bacteria influenced by
> ###   geographical isolation and host specialization
> ### Aliases: rhizobium
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> # The functions used below require the package ape
> data(rhizobium)
> if (require(ape, quiet = TRUE)) {
+ dat <- prep.mdpcoa(rhizobium[[1]], rhizobium[[2]], 
+     model = c("F84", "F84", "F84", "F81"),
+     pairwise.deletion = TRUE)
+ sam <- dat$sam
+ dis <- dat$dis
+ # The distances should be Euclidean. 
+ # Several transformations exist to render a distance object Euclidean 
+ # (see functions cailliez, lingoes and quasieuclid in the ade4 package). 
+ # Here we use the quasieuclid function.
+ dis <- lapply(dis, quasieuclid)
+ mdpcoa1 <- mdpcoa(sam, dis, scann = FALSE, nf = 2)
+ 
+ # Reference analysis
+ plot(mdpcoa1)
+ 
+ # Differences between the loci
+ kplot(mdpcoa1)
+ 
+ # Alleles projected on the population maps.
+ kplotX.mdpcoa(mdpcoa1)
+ }
> 
> 
> 
> 
> 

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