Dear all, I have replied privately to this query, mentioning that the BioC list may not be appropriate for this kind of question, but I was apparently wrong (I'm not on the list, so not aware of the usual topics). Martin Morgan wrote: > Hi Nora -- > > I do not have direct experience with this package, but from the help > page > >> library(ape) >> ?boot.phylo > > perhaps > > allbacteria <- read.dna("allbacteriafasta","fasta") > distallbacteria <- > dist.dna(allbacteria, pairwise.deletion=TRUE, as.matrix=TRUE) > njtree <- nj(distallbacteria) > > boot.phylo(njtree, > allbacteria, > FUN=function(bootstrappedData) { > nj(dist.dna(bootstrappedData, > pairwise.deletion=TRUE, > as.matrix=TRUE)) > }) > > works? Yes, this is it. The option as.matrix=TRUE is not needed, and will likely slow down the whole thing. Earl Glynn asked me for an example of using boot.phylo with a "toy" problem. Here's what can be done in R: library(ape) data(woodmouse) d <- dist.dna(woodmouse) tr <- nj(d) bp <- boot.phylo(tr, woodmouse, function(xx) nj(dist.dna(xx))) plot(tr) nodelabels(bp) Then you can change the options of dist.dna and boot.phylo to see how this may affect the results. There is a more "real life" example in my book "APER" (this case study is actually spread in several chapters, so that it explains the analysis from A to Z, or nearly). boot.phylo is fairly fast for small to moderate size, on my laptop I have: > system.time(bp <- boot.phylo(tr, woodmouse, function(xx) nj(dist.dna(xx)))) [1] 11.089 0.008 11.098 0.000 0.000 These times are expected to be quite longer for big data sets as a substantial part of the computation is done in R (I plan to rewrite this in C sometime). Also I have experimental codes for dist.dna that are much faster than the current ones. > Martin > > Nora Muda <noramuda at="" yahoo.com=""> writes: > >> Dear BioConducter useRs, >> >> I have problems in writing boot.phylo() function. >> >> Let say I have 30 aligned sequences; then I computed >> pairwise distances with "K80" method. Then I construct >> phylogenetic tree with neighbor-joining method and my >> proposed method. Now I have problems in writing "FUN" >> in boot.phylo() function. Below are examples of my >> programs: >> >> library(ape) >> allbacteria <- read.dna("allbacteriafasta","fasta") >> distallbacteria <- >> dist.dna(allbacteria,pairwise.deletion=TRUE,as.matrix=TRUE) >> plot(nj(distallbacteria)) >> >> boot.phylo(plot(nj(distallbacteria)),allbacteria,nj(distallbacteria)) >> >> What should I put as FUN in boot.phylo? >> >> I make comparison between distances of "K80" in PHYLIP >> and dist.dna("K80").There are a lot of differences esp >> in PHYLIP there is a default for >> transversion/transition rate; which is 2 but not in >> ape package. How to modify it to make it the same? Here the answer I sent to Nora: Indeed, and these differences are expected. PHYLIP assumes a "theoretically expected" value of the ts/tv ratio, the distance is then calculated by maximizing a likelihood function. (I owe this information to my colleague Olivier Gascuel who dissected PHYLIP's code.) You can change the default of the ts/tv ratio, in which case it is also estimated by ML. dist.dna is faithful to Kimura's original formulae; it also computes the variance of the distances (which PHYLIP does not). Emmanuel Paradis