Author: David winter
Date: 2025-06-11

# Rentrez Tutorial

#### David winter

#### 2025-06-11

## Introduction: The NCBI, entrez and `rentrez`.

The NCBI shares a lot of data. At the time this document was compiled, there were 31.7 million papers in PubMed, including 6.6 million full-text records available in PubMed Central). The NCBI Nucleotide Database (which includes GenBank) has data for 432 million different sequences, and dbSNP describes 702 million different genetic variants. All of these records can be cross-referenced with the 1.86 million species in the NCBI taxonomy or 27 thousand disease-associated records in OMIM.

The NCBI makes this data available through a web interface, an FTP server and through a REST API called the Entrez Utilities (`Eutils` for short). This package provides functions to use that API, allowing users to gather and combine data from multiple NCBI databases in the comfort of an R session or script.

## Getting started with the rentrez

To make the most of all the data the NCBI shares you need to know a little about their databases, the records they contain and the ways you can find those records. The NCBI provides extensive documentation for each of their databases and for the EUtils API that `rentrez` takes advantage of. There are also some helper functions in `rentrez` that help users learn their way around the NCBI’s databases.

First, you can use `entrez_dbs()` to find the list of available databases:

    entrez_dbs()

There is a set of functions with names starting `entrez_db_` that can be used to gather more information about each of these databases:

Functions that help you learn about NCBI databases

Function name | Return
--- | ---
`entrez_db_summary()` | Brief description of what the database is
`entrez_db_searchable()` | Set of search terms that can used with this database
`entrez_db_links()` | Set of databases that might contain linked records

For instance, we can get a description of the somewhat cryptically named database ‘cdd’…

    entrez_db_summary("cdd")

… or find out which search terms can be used with the Sequence Read Archive (SRA) database (which contains raw data from sequencing projects):

    entrez_db_searchable("sra")

Just how these ‘helper’ functions might be useful will become clearer once you’ve started using `rentrez`, so let’s get started.

## Searching databases: `entrez_search()`

Very often, the first thing you’ll want to do with `rentrez` is search a given NCBI database to find records that match some keywords. You can do this using the function `entrez_search()`. In the simplest case you just need to provide a database name (`db`) and a search term (`term`) so let’s search PubMed for articles about the `R language`:

    r_search <- entrez_search(db="pubmed", term="R Language")

The object returned by a search acts like a list, and you can get a summary of its contents by printing it.

    r_search

There are a few things to note here. First, the NCBI’s server has worked out that we meant R as a programming language, and so included the ‘MeSH’ term term associated with programming languages. We’ll worry about MeSH terms and other special queries later, for now just note that you can use this feature to check that your search term was interpreted in the way you intended. Second, there are many more ‘hits’ for this search than there are unique IDs contained in this object. That’s because the optional argument `retmax`, which controls the maximum number of returned values has a default value of 20.

The IDs are the most important thing returned here. They allow us to fetch records matching those IDs, gather summary data about them or find cross-referenced records in other databases. We access the IDs as a vector using the `$` operator:

    r_search$ids

If we want to get more than 20 IDs we can do so by increasing the `ret_max` argument.

    another_r_search <- entrez_search(db="pubmed", term="R Language", retmax=40)
    another_r_search

If we want to get IDs for all of the thousands of records that match this search, we can use the NCBI’s web history feature described below.

### Building search terms

The EUtils API uses a special syntax to build search terms. You can search a database against a specific term using the format `query[SEARCH FIELD]`, and combine multiple such searches using the boolean operators `AND`, `OR` and `NOT`.

For instance, we can find next generation sequence datasets for the (amazing…) ciliate Tetrahymena thermophila by using the organism (‘ORGN’) search field:

    entrez_search(db="sra",
                  term="Tetrahymena thermophila[ORGN]",
                  retmax=0)

We can narrow our focus to only those records that have been added recently (using the colon to specify a range of values):

    entrez_search(db="sra",
                  term="Tetrahymena thermophila[ORGN] AND 2013:2015[PDAT]",
                  retmax=0)

Or include recent records for either T. thermophila or it’s close relative T. borealis (using parentheses to make ANDs and ORs explicit).

    entrez_search(db="sra",
                  term="(Tetrahymena thermophila[ORGN] OR Tetrahymena borealis[ORGN]) AND 2013:2015[PDAT]",
                  retmax=0)

The set of search terms available varies between databases. You can get a list of available terms or any given data base with `entrez_db_searchable()`

    entrez_db_searchable("sra")

### Using the Filter field

“Filter” is a special field that, as the names suggests, allows you to limit records returned by a search to set of filtering criteria. There is no programmatic way to find the particular terms that can be used with the Filter field. However, the NCBI’s website provides an “advanced search” tool for some databases that can be used to discover these terms.

For example, to find the list of possible to find all of the terms that can be used to filter searches to the nucleotide database using the advanced search for that databse. On that page selecting “Filter” from the first drop-down box then clicking “Show index list” will allow the user to scroll through possible filtering terms.

###Precise queries using MeSH terms

In addition to the search terms described above, the NCBI allows searches using Medical Subject Heading (MeSH) terms. These terms create a ‘controlled vocabulary’, and allow users to make very finely controlled queries of databases.

For instance, if you were interested in reviewing studies on how a class of anti-malarial drugs called Folic Acid Antagonists work against Plasmodium vivax (a particular species of malarial parasite), you could use this search:

    entrez_search(db   = "pubmed",
                  term = "(vivax malaria[MeSH]) AND (folic acid antagonists[MeSH])")

The complete set of MeSH terms is available as a database from the NCBI. That means it is possible to download detailed information about each term and find the ways in which terms relate to each other using `rentrez`. You can search for specific terms with `entrez_search(db="mesh", term =...)` and learn about the results of your search using the tools described below.

### Advanced counting

As you can see above, the object returned by `entrez_search()` includes the number of records matching a given search. This means you can learn a little about the composition of, or trends in, the records stored in the NCBI’s databases using only the search utility. For instance, let’s track the rise of the scientific buzzword “connectome” in PubMed, programmatically creating search terms for the `PDAT` field:

    search_year <- function(year, term){
        query <- paste(term, "AND (", year, "[PDAT])")
        entrez_search(db="pubmed", term=query, retmax=0)$count
    }

    year <- 2008:2014
    papers <- sapply(year, search_year, term="Connectome", USE.NAMES=FALSE)

    plot(year, papers, type='b', main="The Rise of the Connectome")

## Finding cross-references : `entrez_link()`:

One of the strengths of the NCBI databases is the degree to which records of one type are connected to other records within the NCBI or to external data sources. The function `entrez_link()` allows users to discover these links between records.

### My god, it’s full of links

To get an idea of the degree to which records in the NCBI are cross-linked we can find all NCBI data associated with a single gene (in this case the Amyloid Beta Precursor gene, the product of which is associated with the plaques that form in the brains of Alzheimer’s Disease patients).

The function `entrez_link()` can be used to find cross-referenced records. In the most basic case we need to provide an ID (`id`), the database from which this ID comes (`dbfrom`) and the name of a database in which to find linked records (`db`). If we set this last argument to ‘all’ we can find links in multiple databases:

    all_the_links <- entrez_link(dbfrom='gene', id=351, db='all')
    all_the_links

Just as with `entrez_search` the returned object behaves like a list, and we can learn a little about its contents by printing it. In the case, all of the information is in `links` (and there’s a lot of them!):

    all_the_links$links

The names of the list elements are in the format `[source_database]_[linked_database]` and the elements themselves contain a vector of linked-IDs. So, if we want to find open access publications associated with this gene we could get linked records in PubMed Central:

    all_the_links$links$gene_pmc[1:10]

Or if were interested in this gene’s role in diseases we could find links to clinVar:

    all_the_links$links$gene_clinvar

### Narrowing our focus

If we know beforehand what sort of links we’d like to find , we can to use the `db` argument to narrow the focus of a call to `entrez_link`.

For instance, say we are interested in knowing about all of the RNA transcripts associated with the Amyloid Beta Precursor gene in humans. Transcript sequences are stored in the nucleotide database (referred to as `nuccore` in EUtils), so to find transcripts associated with a given gene we need to set `dbfrom=gene` and `db=nuccore`.

    nuc_links <- entrez_link(dbfrom='gene', id=351, db='nuccore')
    nuc_links
    nuc_links$links

The object we get back contains links to the nucleotide database generally, but also to special subsets of that database like refseq. We can take advantage of this narrower set of links to find IDs that match unique transcripts from our gene of interest.

    nuc_links$links$gene_nuccore_refseqrna

We can use these ids in calls to `entrez_fetch()` or `entrez_summary()` to learn more about the transcripts they represent.

### External links

In addition to finding data within the NCBI, `entrez_link` can turn up connections to external databases. Perhaps the most interesting example is finding links to the full text of papers in PubMed. For example, when I wrote this document the first paper linked to Amyloid Beta Precursor had a unique ID of `25500142`. We can find links to the full text of that paper with `entrez_link` by setting the `cmd` argument to ‘llinks’:

    paper_links <- entrez_link(dbfrom="pubmed", id=25500142, cmd="llinks")
    paper_links

Each element of the `linkouts` object contains information about an external source of data on this paper:

    paper_links$linkouts

Each of those linkout objects contains quite a lot of information, but the URL is probably the most useful. For that reason, `rentrez` provides the function `linkout_urls` to make extracting just the URL simple:

    linkout_urls(paper_links)

The full list of options for the `cmd` argument are given in in-line documentation (`?entrez_link`). If you are interested in finding full text records for a large number of articles checkout the package fulltext which makes use of multiple sources (including the NCBI) to discover the full text articles.

### Using more than one ID

It is possible to pass more than one ID to `entrez_link()`. By default, doing so will give you a single elink object containing the complete set of links for all of the IDs that you specified. So, if you were looking for protein IDs related to specific genes you could do:

    all_links_together  <- entrez_link(db="protein", dbfrom="gene", id=c("93100", "223646"))
    all_links_together
    all_links_together$links$gene_protein

Although this behaviour might sometimes be useful, it means we’ve lost track of which `protein` ID is linked to which `gene` ID. To retain that information we can set `by_id` to `TRUE`. This gives us a list of elink objects, each once containing links from a single `gene` ID:

    all_links_sep  <- entrez_link(db="protein", dbfrom="gene", id=c("93100", "223646"), by_id=TRUE)
    all_links_sep
    lapply(all_links_sep, function(x) x$links$gene_protein)

## Getting summary data: `entrez_summary()`

Having found the unique IDs for some records via `entrez_search` or `entrez_link()`, you are probably going to want to learn something about them. The `Eutils` API has two ways to get information about a record. `entrez_fetch()` returns ‘full’ records in varying formats and `entrez_summary()` returns less information about each record, but in relatively simple format. Very often the summary records have the information you are after, so `rentrez` provides functions to parse and summarise summary records.

### The summary record

`entrez_summary()` takes a vector of unique IDs for the samples you want to get summary information from. Let’s start by finding out something about the paper describing Taxize, using its PubMed ID:

    taxize_summ <- entrez_summary(db="pubmed", id=24555091)
    taxize_summ

Once again, the object returned by `entrez_summary` behaves like a list, so you can extract elements using `$`. For instance, we could convert our PubMed ID to another article identifier…

    taxize_summ$articleids

…or see how many times the article has been cited in PubMed Central papers

    taxize_summ$pmcrefcount

### Dealing with many records

If you give `entrez_summary()` a vector with more than one ID you’ll get a list of summary records back. Let’s get those Plasmodium vivax papers we found in the `entrez_search()` section back, and fetch some summary data on each paper:

    vivax_search <- entrez_search(db = "pubmed",
                                  term = "(vivax malaria[MeSH]) AND (folic acid antagonists[MeSH])")
    multi_summs <- entrez_summary(db="pubmed", id=vivax_search$ids)

`rentrez` provides a helper function, `extract_from_esummary()` that takes one or more elements from every summary record in one of these lists. Here it is working with one…

    extract_from_esummary(multi_summs, "fulljournalname")

… and several elements:

    date_and_cite <- extract_from_esummary(multi_summs, c("pubdate", "pmcrefcount",  "title"))
    knitr::kable(head(t(date_and_cite)), row.names=FALSE)

## Fetching full records: `entrez_fetch()`

As useful as the summary records are, sometimes they just don’t have the information that you need. If you want a complete representation of a record you can use `entrez_fetch`, using the argument `rettype` to specify the format you’d like the record in.

### Fetch DNA sequences in fasta format

Let’s extend the example given in the `entrez_link()` section about finding transcript for a given gene. This time we will fetch cDNA sequences of those transcripts.We can start by repeating the steps in the earlier example to get nucleotide IDs for refseq transcripts of two genes:

    gene_ids <- c(351, 11647)
    linked_seq_ids <- entrez_link(dbfrom="gene", id=gene_ids, db="nuccore")
    linked_transripts <- linked_seq_ids$links$gene_nuccore_refseqrna
    head(linked_transripts)

Now we can get our sequences with `entrez_fetch`, setting `rettype` to “fasta” (the list of formats available for each database is give in this table):

    all_recs <- entrez_fetch(db="nuccore", id=linked_transripts, rettype="fasta")
    class(all_recs)
    nchar(all_recs)

Congratulations, now you have a really huge character vector! Rather than printing all those thousands of bases we can take a peak at the top of the file:

    cat(strwrap(substr(all_recs, 1, 500)), sep="\n")

If we wanted to use these sequences in some other application we could write them to file:

    write(all_recs, file="my_transcripts.fasta")

Alternatively, if you want to use them within an R session
we could write them to a temporary file then read that. In this case I’m using `read.dna()` from the pylogenetics package ape (but not executing the code block in this vignette, so you don’t have to install that package):

    temp <- tempfile()
    write(all_recs, temp)
    parsed_recs <- ape::read.dna(all_recs, temp)

### Fetch a parsed XML document

Most of the NCBI’s databases can return records in XML format. In additional to downloading the text-representation of these files, `entrez_fetch()` can return objects parsed by the `XML` package. As an example, we can check out the Taxonomy database’s record for (did I mention they are amazing….) Tetrahymena thermophila, specifying we want the result to be parsed by setting `parsed=TRUE`:

    Tt <- entrez_search(db="taxonomy", term="(Tetrahymena thermophila[ORGN]) AND Species[RANK]")
    tax_rec <- entrez_fetch(db="taxonomy", id=Tt$ids, rettype="xml", parsed=TRUE)
    class(tax_rec)

The package XML (which you have if you have installed `rentrez`) provides functions to get information from these files. For relatively simple records like this one you can use `XML::xmlToList`:

    tax_list <- XML::xmlToList(tax_rec)
    tax_list$Taxon$GeneticCode

For more complex records, which generate deeply-nested lists, you can use XPath expressions along with the function `XML::xpathSApply` or the extraction operatord `[` and `[[` to extract specific parts of the file. For instance, we can get the scientific name of each taxon in T. thermophila’s lineage by specifying a path through the XML

    tt_lineage <- tax_rec["//LineageEx/Taxon/ScientificName"]
    tt_lineage[1:4]

As the name suggests, `XML::xpathSApply()` is a counterpart of base R’s `sapply`, and can be used to apply a function to nodes in an XML object. A particularly useful function to apply is `XML::xmlValue`, which returns the content of the node:

    XML::xpathSApply(tax_rec, "//LineageEx/Taxon/ScientificName", XML::xmlValue)

There are a few more complex examples of using `XPath` on the rentrez wiki

## Using NCBI’s Web History features

When you are dealing with very large queries it can be time consuming to pass long vectors of unique IDs to and from the NCBI. To avoid this problem, the NCBI provides a feature called “web history” which allows users to store IDs on the NCBI servers then refer to them in future calls.

###Post a set of IDs to the NCBI for later use: `entrez_post()`

If you have a list of many NCBI IDs that you want to use later on, you can post them to the NCBI’s severs. In order to provide a brief example, I’m going to post just one ID, the `omim` identifier for asthma:

    upload <- entrez_post(db="omim", id=600807)
    upload

The NCBI sends you back some information you can use to refer to the posted IDs. In `rentrez`, that information is represented as a `web_history` object.

Note that if you have a very long list of IDs you may receive a 414 error when you try to upload them. If you have such a list (and they come from an external sources rather than a search that can be save to a `web_history` object), you may have to ‘chunk’ the IDs into smaller sets that can processed.

### Get a `web_history` object from `entrez_search` or `entrez_link()`

In addition to directly uploading IDs to the NCBI, you can use the web history features with `entrez_search` and `entrez_link`. For instance, imagine you wanted to find all of the sequences of the widely-studied gene COI from all snails (which are members of the taxonomic group Gastropoda):

    entrez_search(db="nuccore", term="COI[Gene] AND Gastropoda[ORGN]")

That’s a lot of sequences! If you really wanted to download all of these it would be a good idea to save all those IDs to the server by setting `use_history` to `TRUE` (note you now get a `web_history` object along with your normal search result):

    snail_coi <- entrez_search(db="nuccore", term="COI[Gene] AND Gastropoda[ORGN]", use_history=TRUE)
    snail_coi
    snail_coi$web_history

Similarity, `entrez_link()` can return `web_history` objects by using the `cmd` `neighbor_history`. Let’s find genetic variants (from the clinvar database) associated with asthma (using the same OMIM ID we identified earlier):

    asthma_clinvar <- entrez_link(dbfrom="omim", db="clinvar", cmd="neighbor_history", id=600807)
    asthma_clinvar$web_histories

As you can see, instead of returning lists of IDs for each linked database (as it would be default), `entrez_link()` now returns a list of web_histories.

### Use a `web_history` object

Once you have those IDs stored on the NCBI’s servers, you are going to want to do something with them. The functions `entrez_fetch()` `entrez_summary()` and `entrez_link()` can all use `web_history` objects in exactly the same way they use IDs.

So, we could repeat the last example (finding variants linked to asthma), but this time using the ID we uploaded earlier

    asthma_variants <- entrez_link(dbfrom="omim", db="clinvar", cmd="neighbor_history", web_history=upload)
    asthma_variants

… if we want to get some genetic information about these variants we need to map our clinvar IDs to SNP IDs:

    snp_links <- entrez_link(dbfrom="clinvar", db="snp",
                             web_history=asthma_variants$web_histories$omim_clinvar,
                             cmd="neighbor_history")
    snp_summ <- entrez_summary(db="snp", web_history=snp_links$web_histories$clinvar_snp)
    knitr::kable(extract_from_esummary(snp_summ, c("chr", "fxn_class", "global_maf")))

If you really wanted to you could also use `web_history` objects to download all those thousands of COI sequences. When downloading large sets of data, it is a good idea to take advantage of the arguments `retmax` and `restart` to split the request up into smaller chunks. For instance, we could get the first 200 sequences in 50-sequence chunks:

(note: this code block is not executed as part of the vignette to save time and bandwidth):

    for( seq_start in seq(1,200,50)){
        recs <- entrez_fetch(db="nuccore", web_history=snail_coi$web_history,
                             rettype="fasta", retmax=50, retstart=seq_start)
        cat(recs, file="snail_coi.fasta", append=TRUE)
        cat(seq_start+49, "sequences downloaded\r")
    }

## Rate-limiting and API Keys

By default, the NCBI limits users to making only 3 requests per second (and `rentrez` enforces that limit). Users who register for an “API key” are able to make up to ten requests per second. Getting one of these keys is simple, you just need to register for “my ncbi” account then click on a button in the account settings page.

Once you have an API key, rentrez will allow you to take advantage of it. For one-off cases, this is as simple as adding the `api_key` argument to given function call. (Note these examples are not executed as part of this document, as the API key used here not a real one).

    entrez_link(db="protein", dbfrom="gene", id=93100, api_key ="ABCD123")

It most cases you will want to use your API for each of several calls to the NCBI. `rentrez` makes this easy by allowing you to set an environment variable ,`ENTREZ_KEY`. Once this value is set to your key `rentrez` will use it for all requests to the NCBI. To set the value for a single R session you can use the function `set_entrez_key()`. Here we set the value and confirm it is available.

    set_entrez_key("ABCD123")
    Sys.getenv("ENTREZ_KEY")

If you use `rentrez` often you should edit your `.Renviron` file (see `r help(Startup)` for description of this file) to include your key. Doing so will mean all requests you send will take advantage of your API key.

    ENTREZ_KEY=ABCD123

As long as an API key is set by one of these methods, `rentrez` will allow you to make up to ten requests per second.

### Slowing rentrez down when you hit the rate-limit

`rentrez` won’t let you send requests to the NCBI at a rate higher than the rate-limit, but it is sometimes possible that they will arrive too close together an produce errors. If you are using `rentrez` functions in a for loop and find rate-limiting errors are occuring, you may consider adding a call to `Sys.sleep(0.1)` before each message sent to the NCBI. This will ensure you stay beloe the rate limit.

## What next ?

This tutorial has introduced you to the core functions of `rentrez`, there are almost limitless ways that you could put them together. Check out the wiki for more specific examples, and be sure to read the inline-documentation for each function. If you run into problem with rentrez, or just need help with the package and `Eutils` please contact us by opening an issue at the github repository