dwinter

 library(ape)
 data(woodmouse)
 #Just the third codon (assuming alignment is in-frame)
 woodmouse[, seq(1, ncol(woodmouse), by=3) ]

## 15 DNA sequences in binary format stored in a matrix.
## 

dwinter

#Finding the number of sequence records by taxonomic group

Ricardo wants to know how to find the number of sequence records associated with sub-groups within a given taxon . This example grew a bit too big to make into a comment, so here it is in gist form.

So, let's find out how many DNA sequences are present in genbank for each

dwinter

```{r, echo=FALSE}
knitr::opts_knit$set(upload.fun = knitr::imgur_upload, base.url = NULL)  
```


# and now, all animals and with nested taxonomic ranks

So, a few people liked [this example](https://gist.github.com/dwinter/8d7bde0579daf7466508) 
of using `rentrez` to investigate the taxonomic distribution of sequences in
Genbank. I though it might be fun to extend it a little. Specifically:

dwinter

From a biostars question about interleaving vectors...

For loops can (but don't have to) be really slow in R, so I wanted to compare answers provided to this question to compare a straightforward for loop approach, to various work arounds to speed the process up.

First, the functions

f_for <- function(a,b){
  res <- c()

dwinter

#Demo for com call

library(XML)
load_all("~/src/rentrez")

dwinter

library(rcrossref)

add_to_bib <- function(doi, bib="ms.bib", print_ref=TRUE){
    ref <- cr_cn(doi)
    cat("\n", ref, "\n\n", file=bib, append=TRUE)
    if(print_ref){
        cat(ref, "\n")
    }
}

dwinter

traits <- read.csv("flag_traits.csv")
dist_mat <- dist(traits, "manhattan")
tr <- ape::nj(dist_mat)
plot(tr)

##Rooting the tree (arbitrarily) will make a nicer plot
#rooted <- ape::root(tr, node=50)
#plot(rooted)

dwinter

#Rentrez 1.0 released

A new version of `rentrez`, our package for the NCBI's EUtils API, is making
it's way around the CRAN mirrors. This release represents a substantial
mprovement to `rentrez`, including a [new vignette](https://cran.r-project.org/web/packages/rentrez/vignettes/rentrez_tutorial.html)
that documents the whole package. 

This posts describes some of the new things in `rentrez`, and gives us a chance
to thank some of the people that have contriuted to this package's development. 

dwinter

def square_docs(f):
    f.__doc__ = "Square it"
    return(f)
  
@square_docs
def square(x):
    return(x**2)

help(square)
#Type:        function

dwinter

#Won't work by cross linking
```r
tax_search <- entrez_search(db="taxonomy", term="Acetobacter[ORGN] AND genus[RANK]")
linked_recs <- entrez_link(dbfrom="taxonomy", db="genome", id=tax_search$ids)
linked_recs$links$taxonomy_genome
```

```
#[1] "18005"
```