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 improvement to rentrez, including a new vignette 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 contributed to this package’s development.

🔗 Thanks

Thanks to everyone who has filed and issue or written us an email about rentrez, your contributions have been an important part of the package’s development. In particular, we welcome Han Guangchun as a new contributor to rentrez and thank Matthew O’Meara for posting an issue that brought the by_id mode for entrez_link (discussed below) to our attention.

##The New Stuff

###Extract elements from the results of entrez_summary()

The NCBI’s “summary records” are very useful – they provide the most important information about a given record in a relatively small and simple file. rentrez provides the function entrez_summary() to retrieve these records. When more than one unique ID is passed to entrez_summary the function returns a list of esummary objects. For instance, you could find all the genetic variants associated with asthma by finding links between the OMIM record for asthma and records in the database dbSNP:

snps <- entrez_link(dbfrom="omim", db="snp", id= 600807)
snp_summs <- entrez_summary(db="snp", id=snps$links$omim_snp)

A very common use-case for entrez_summary() is to extract a subset of the elements from each record in that list. This release includes the function extract_from_esummary to make this as straightforward as possible. It works with a single element to extract:

extract_from_esummary(snp_summs, "chr")

## 11079657  2786098  1031772  1031771   545659
##     "17"      "1"      "2"      "2"     "11"

Or with multiple elements

summary_table <- extract_from_esummary(snp_summs, c("chr", "global_maf", "fxn_class"))
t(summary_table)

##          chr  global_maf      fxn_class
## 11079657 "17" "A=0.4295/2151" "intron-variant"
## 2786098  "1"  "T=0.1569/786"  "intron-variant"
## 1031772  "2"  "G=0.2131/1067" "downstream-variant-500B"
## 1031771  "2"  "T=0.2582/1293" ""
## 545659   "11" "C=0.3419/1712" "utr-variant-3-prime"

###entrez_link can find external links

In addition to discovering links between records in NCBI databases, the function entrez_link now provides support for finding external links (‘linkouts’ in NCBI terminology). Perhaps the most interesting example is finding links for the full text of articles in PubMed.

Let’s try and find the full text of the paper describing taxize (using that article’s PMID). To override the functions default behaviour (finding links within NCBI databases) we set the cmd argument to llinks (short for library links):

taxize_links <- entrez_link(dbfrom="pubmed", id= 24555091, cmd="llinks")
taxize_links

## elink object with contents:
##  $linkouts: links to external websites

The print function for this object tells you were the links live.

taxize_links$linkouts

## $ID_24555091
## $ID_24555091[[1]]
## Linkout from F1000 Research Ltd
##  $Url: http://f1000research.com/a ...
##
## $ID_24555091[[2]]
## Linkout from Europe PubMed Central
##  $Url: http://europepmc.org/abstr ...
##
## $ID_24555091[[3]]
## Linkout from PubMed Central
##  $Url: http://www.ncbi.nlm.nih.go ...
##
## $ID_24555091[[4]]
## Linkout from PubMed Central Canada
##  $Url: http://pubmedcentralcanada ...

Each of those elements has a lot of information, but the URLs for each object are probably the most important. For this reason, rentrez provides a function to get just the URLs:

linkout_urls(taxize_links)

## $ID_24555091
## [1] "http://f1000research.com/articles/10.12688/f1000research.2-191.v2/doi"
## [2] "http://europepmc.org/abstract/MED/24555091"
## [3] "http://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24555091/"
## [4] "http://pubmedcentralcanada.ca/pmcc/articles/pmid/24555091"

###Web History features are easier to use

The NCBI provides a “Web History” feature to let users store the results of their searches on the NCBI’s severs and refer to those results without having to pass unique ID’s back and forth between computers. These features have always been supported by rentrez but this release makes them easier to use.

Specifically, when the new optional argument use_history is set to TRUE functions will return a web_history object which can be used in the place of unique IDs in calls to entrez_fetch, entrez_summary or entrez_link.

To demonstrate, let’s search for PubMed articles about the ciliate genus Tetrahymena:

Tet_papers <- entrez_search(db="pubmed", term="Tetrahymena[ORGN]", use_history=TRUE)
Tet_papers

## Entrez search result with 6599 hits (object contains 20 IDs and a web_history object)
##  Search term (as translated):  "tetrahymena"[MeSH Terms] OR "tetrahymena"[All Fie ...

Now that we have a web_history object, we can use that to retrieve XML representations of the first 10 records:

recs <- entrez_fetch(db="pubmed",
                     web_history=Tet_papers$web_history,
                     retmax=10, rettype="xml")

###It’s easier to keep track of which records are linked to other records

By default, when entrez_link gets a vector of more than one unique ID, it returns sets of linked-IDs that match any of the IDs in the original call. That means the user loses track of the mapping between the original IDs and those from the linked database.

As of this release, rentrez supports the NCBI’s by_id mode, which solves this problem. Setting the new argument by_id to TRUE returns a list, with each element of that list containing links for only one ID. To demonstrate, let’s find protein sequences associated with specific genes in the NCBI gene database:

all_links  <- entrez_link(db="protein",
                          dbfrom="gene",
                          id=c(93100, 223646),
                          by_id=TRUE)

all_links

## List of 2 elink objects,each containing
##   $links: IDs for linked records from NCBI
##

As you can see, printing the returned object let’s you know what each element contains, and you can extract the specific links you are looking for easily:

lapply(all_links, function(x) x$links$gene_protein)

## [[1]]
##  [1] "768043930" "767953815" "558472750" "194394158" "166221824"
##  [6] "154936864" "119602646" "119602645" "119602644" "119602643"
## [11] "119602642" "37787309"  "37787307"  "37787305"  "33991172"
## [16] "21619615"  "10834676"
##
## [[2]]
##  [1] "148697547" "148697546" "81899807"  "74215266"  "74186774"
##  [6] "37787317"  "37589273"  "31982089"  "26339824"  "26329351"

🔗 The rest

There are also numerous small changes that improve rentrez, fix bugs and extend the package’s documentation. We hope you find this new release helpful, and as always we welcome bug reports via the package’s github repository.