source for code section of Rentrez post

rentrez.Rmd

##Example

Lately, I've been working on a little meta-analysis of phylogenies. In particualr, 
we're interested in why sometimes different genes tell different stories about
the relationships between species from which the come. In terms of being able
to get the individual gene trees I need to do these analyses there are good, 
rather less good and quite bad papers out there. In the best cases I can just 
download the trees as nice, parsable newick files from[TreeBase](treebase.org) 
(which has already been [wrapped by ROpenSci](github.com/ROpenSci/treebase)). 
Sometimes I need to print out the trees from a paper and work with pencil and paper,
which I can handle. In a few cases people haven't actually published their individual
gene trees, if I want to included these papers I need to replicate their work by 
downloading the gene sequences, aligning them and making new trees. 

So, here's an example of how I've been using `rentrez` to automate some of that 
process. I'm going to use a slightly convaluted process to get all the data, but
that's just so I can walk though a bunch of the `rentrez` functions. Let's get
started. Reece et al (2010, [doi:10.1016/j.ympev.2010.07.013](http://dx.doi.org/10.1016/j.ympev.2010.07.013))
presented a phylogeny of moray eels using four different genes, but didn't 
publish the gene trees. I want to get the sequences underlying their analyses,
which will be in the NCBI's databases, so I can reproduce their results. To get 
data associated with this paper from the NCBI I need the PMID (pubmed ID), which
I can find using the `rentrez` function `entrez_search` to query the pubmed 
database with the paper's doi:

```{r get_pmid, message=FALSE}
library(rentrez)
pubmed_search <- entrez_search(db="pubmed", term="10.1016/j.ympev.2010.07.013[doi]")
pubmed_search$ids
```

All the functions in `rentrez` create a URL to get data from the NCBI, then fetch
the resulting document, usually as an XML file. In most cases the functions will
parse the most relevant sections of the XML file out and present them to you
as items in a list (`ids` being one item of the `pubmed_search` list in this case). 

OK, now we have the PMID, what data does NCBI have for this paper? The 
`entrez_link` function lets us find out. In this case the `db` argument can be 
used to limit the number of data sources to check, but I want to see every data 
source here so I'll set this paramater to "all":

```{r get_data}
NCBI_data <- entrez_link(dbfrom="pubmed", id=pubmed_search$ids, db="all")
str(NCBI_data)
```

The most relevant data here is the from the [popset](ncbi.nlm.nih.gov/popset)
database, which containts population and phylogenetic datasets. If I want to 
see what each of the four popset datasets associated with this paper are about I
can use `entrez_summary` to have a look. This function can collect summaries 
from a lot of different databases, and, because the XML return by those databases
isn't conisitant doesn't make any attempt to parse information from the resulting
file. Instead you get a `XMLInternalDocument` object from the `XML` library, which
you have to further process yourself. In this case, a little xpath gets the name
of each dataset:

```{r summary}
data_summaries <- entrez_summary(db="popset", ids=NCBI_data$pubmed_popset)
xpathSApply(data_summaries, "//Item[@Name='Title']", xmlValue)
```

Ok, since we might expect nuclear and mitochondrial genes to hav different 
histories, let's get sequences from each genome (the the COI and RAG1 datasets) 
using `entrez_fetch`. By specifying `file_format="fasta"` we will get characater
vectors in the fasta format:

``` {r fetch}
coi <- entrez_fetch(db="popset", ids=NCBI_data$pubmed_popset[1], file_format="fasta" )
rag1 <- entrez_fetch(db="popset", ids=NCBI_data$pubmed_popset[3], file_format="fasta")
write(coi, "moray_coi_raw.fasta")
write(rag1, "moray_rag1_raw.fasta")
```

So I've got the data on hand - that's all the I need `rentrez` for, but I might 
as well align these  sequences and make gene trees for each. I'll just do a 
quick and diry neighbor-joining tree using `ape` and we can clean up the long 
OTU names with the help of `stingr`. (I put the fussy work of cleaning the names
 and rooting the trees into a function `clean_and_root`):

```{r trees}
library(ape)
library(stringr)
clean_and_root <- function(tr, outgroup, resolved=TRUE){
  tr$tip.label <- sapply(str_split(tr$tip.label, " "), function(x) paste(x[2:3], collapse="_"))
  return(root(tr, outgroup, resolve.root=resolved))
}
par(mfrow=c(1,2))
coi_ali <- muscle(read.dna("moray_coi_raw.fasta", "fasta"))
coi_tr <- nj(dist.dna(coi_ali, "k81"))
clean_coi_tr <- clean_and_root(coi_tr, "Uropterygius_macrocephalus" )
plot(clean_coi_tr, direction="rightwards", cex=0.5)
rag_ali <- muscle(read.dna("moray_rag1_raw.fasta", "fasta"))
rag_tr <- nj(dist.dna(rag_ali, "k81"))
clean_rag_tr <- clean_and_root(rag_tr, "Uropterygius_macrocephalus" )
plot(clean_rag_tr, direction="leftward", cex=0.5)
```