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Pubmed / NCBI / eutils interaction library, handling the metadata of pubmed papers.

Project description

Metapub is a Python library that provides python objects fetched via eutils that represent papers and concepts found within the NLM.

These objects abstract some interactions with pubmed, and intends to encompass as many types of database lookups and summaries as can be provided via Eutils / Entrez.

CHANGES! NEW in 0.5:

  • NCBI_API_KEY supported as environment variable. Now you can send up to 10 queries per second to NCBI!

  • CrossRef API overhaul. CrossRefFetcher objects source from newest Crossref API!

  • CrossRefWork object made to parallel PubMedArticle object.

  • Logic of building citations is now in and abstracted for multiple uses.

  • Python3 support only! Python2 stuff completely ripped out!

  • Widespread use of Logging so you can see what’s going on under the hood.

  • SUPER NEW! Command line utilities – see Getting Started below.

Getting Started

Metapub is most easily installed using pip. It relies entirely on the arcane magic of classic Python setuptools:

pip install metapub

When the package is installed, you’ll have a few command line utilities available to you:

convert pmid2doi <pmid>
convert doi2pmid <doi>
convert bookid2pmid <ncbi_bookID>
pubmed_article <pmid>

All of these utilities contain their own help screens, by which you will be able to see options and usage.

Here’s an example:

convert pmid2doi 1000 -a -w


PMID:  1000
DOI:  10.1042/bj1490739

<PubMedArticle 1000> Wootton JC; Taylor JG; Jackson AA; Chambers GK; Fincham JR. The amino acid sequence of Neurospora NADP-specific glutamate dehydrogenase. The tryptic peptides.. Biochem. J.. 1975. 149(3):739-48
<CrossRefWork 10.1042/bj1490739 Score: 1.0> Wootton. "The amino acid sequence ofNeurosporaNADP-specific glutamate dehydrogenase. The tryptic peptides" Biochemical Journal. 1975. 149(3):739-748

Here’s another example, converting an NCBI Book ID to its PMID (if it has one):

convert bookid2pmid NBK201366


BookID:  NBK201366
PMID:  24830047

PubMedArticle / PubMedFetcher

First, a note about the NCBI_API_KEY… You’ll want to grab an API key if you’re doing more than a few requests per minute over here on the NCBI website:

To load the API_KEY, do the following on the command line, or set this environment variable in your deployment script of choice:

export NCBI_API_KEY=”Your_key_here”

You don’t need to use an API Key, but without it you are limited to 3 queries per second.

Now, on to PubMedFetcher basic usage:

fetch = PubMedFetcher()
article = fetch.article_by_pmid('123456')
print(article.journal, article.year, article.volume, article.issue)

PubMedFetcher uses an SQLite cacheing engine (provided through eutils), which by default places a file in your user directory. E.g. the author’s cache directory path would be /home/nthmost/.cache/eutils-cache.db

This cache file can grow quite large over time. Deleting the cache file is safe and can also be regarded as the way to “reset” the cache.

The cachedir keyword argument can be supplied to PubMedFetcher as a way to specify where the cache file will reside. For example:

fetch = PubMedFetcher(cachedir='/path/to/cachedir')

User directory expansion also works:

fetch = PubMedFetcher(cachedir=’~/.othercachedir’)

The cachedir will be created for you if it doesn’t already exist, assuming the user account you’re running metapub under has permissions to do so.

PubMedArticle Lookup Methods

The following methods return a PubMedArticle object (or raise InvalidPMID if NCBI lookup fails).


(Attempt to) fetch an article by supplying its pubmed ID (both integer and string accepted).


(Attempt to) fetch an article by looking up the DOI first.


Fetch an article by looking up the PMCID first. Both integer and string accepted.

Pubmed ID List Methods

The following methods return a list of pubmed IDs (if found) or an empty list (if None).


Produces a list of possible PMIDs for the submitted citation, where the citation is submitted as a collection of keyword arguments. At least 3 of the 5, preferably 4 or 5 for best results, must be included:

aulast or author_last_fm1
first_page or spage
journal or jtitle

Use NLM Title Abbreviation (aka ISO Abbreviation) journal strings whenever possible.


Returns list of pmids for given freeform query string plus keyword arguments.

All Pubmed Advanced Query tokens are supported.

See [NCBI Search Field Descriptions and Tags](


Composes a “Clinical Query” as on this page: (

Supply a “category” (required) and an optimization (“broad” or “narrow”) for this function. Available categories:

  • therapy

  • diagnosis

  • etiology

  • prognosis

  • prediction

All keyword arguments for PubMedFetcher.pmids_for_query available.


Composes a “Medical Genetics Query” as described here: (

Supply a “category” (required) and an optimization (“broad” or “narrow”) for this function. Available categories:

  • therapy

  • diagnosis

  • etiology

  • prognosis

  • prediction

All keyword arguments for PubMedFetcher.pmids_for_query available.


The PubMedCentral functions are a loose collection of conversion methods for academic publishing IDs, allowing conversion (where possible) between the following ID types:

doi (Digital object identifier)
pmid (PubMed ID)
pmcid (Pubmed Central ID (including versioned document ID)

The following methods are supplied, returning a string (if found) or None:


As implied by the function names, you can supply any valid ID type (“otherid”) to acquire the desired ID type.

MedGenConcept / MedGenFetcher

The MedGen (medical genetics) database is a clinical dictionary linking medical concepts across multiple medical ontologies and dictionaries such as OMIM and SNOMED.

Basic usage:

from metapub import MedGenFetcher

fetch = MedGenFetcher()

concept = fetch.concept_by_uid('336867')

ClinVarVariation / ClinVarFetcher

The ClinVar database contains information submitted by genetic researchers, labs, and testing companies around the world.

Information queryable using the ClinVarFetcher currently includes searching for the ID of a variant (“Variation”) in the database using an HGVS string and retrieving the Variant Summmary using a variation ID or HGVS string.

Since Pubmed citations by Variation ID are also available by a cross-query between ClinVar and Pubmed, ClinVarFetcher allows retrieving PMIDs for given HGVS string.

Basic usage:

clinvar = ClinVarFetcher()
cv = clinvar.variation_by_hgvs('NM_000249.3:c.1958T>G')

pubmed_citations = clinvar.pmids_for_hgvs('NM_000249.3:c.1958T>G')


The CrossRefFetcher object provides an object layer into’s API. See

CrossRef is a service that excels at resolving DOIs into article citation details. It can also be used to resolve a DOI /from/ article citation details.

Our interface to Crossref comes through the neat and clean habenero library by @sckott.

In metapub, the CrossRefFetcher object contains convenience methods into the query that allows us to abstract away a lot of the string-handshaking between PubMedArticles and CrossRef and just get what we need as quickly and accurately as possible.

Basic usage:

CR = CrossRefFetcher()       # starts the query cache engine
work = CR.article_by_title("Some great academic work of pure genius no doubt.", params)

if work:

In the above example, we just had a title. Sometimes that’s good enough to get a result, and sometimes it’s not. The above function will return the top result off the list without a lot of introspection.

The next method, on the other hand, performs some fancy Levenshtein distance calculation and re-querying with different combos of parameters in order to drill down to a really precise result.

Example starting from a known pubmed ID:

pma = PubMedFetcher().article_by_pmid(known_pmid)
work = CR.article_by_pma(pma)


In this minor version (0.5) of Metapub there is no CrossRefFetcher cache. This feature is coming back very ASAP.


Looking for an article PDF? Trying to gather a large corpus of research?

The FindIt object was designed to be able to locate the direct urls of as many different articles from as many different publishers of PubMed content as possible.

Any article that is Open Access, whether it is in PubmedCentral or not, can potentially be “FindIt-able”. Usage is simple:

from metapub import FindIt
src = FindIt('18381613')

You can start FindIt from a DOI instead of a PMID by instantiating with FindIt(doi=’10.1234/some.doi’).

If FindIt couldn’t get a URL, you can take a look at the “reason” attribute to find out why. For example:

src = FindIt('1234567')
if src.url is None: print(src.reason)

The FindIt object is cached (keyed to PMID), so while initialization the first time around for a given PMID or DOI may take a few seconds, the second time this information is requested it will take far less time.

If you see a FindIt “reason” that starts with NOFORMAT, this is a great place to contribute some help to metapub! Feel free to dive in and submit a pull request, or contact the author ( for advice on how to fill in these gaps.


Starting with a URL pointing to the abstract, pdf, or online fulltext of an article, UrlReverse can “reverse” the DOI and/or the PubMed ID (pmid) of the article (assuming it can be found in PubMed).

The UrlReverse object provides an interface to the urlreverse logic, and it attributes hold state for all of the information gathered and steps used to gather that information.

Usage is very similar to FindIt:

from metapub import UrlReverse
urlrev = UrlReverse('')

UrlReverse is cached (keyed to URL); by default its cache db can be found in ~/.cache/urlreverse-cache.db

As of metapub 0.4.3, there is no mechanism to have an item in cache expire. This is considered a deficiency and will be remedied in a future version.

This is the newest feature in metapub (as of 0.4.2a0) and there is still much work to be done. The world of biomedical literature URLs is fraught with inconsistencies and very weird URL formats. UrlReverse could really benefit from being able to parse supplement URLs, for example.

Collaboration and contributions heartily encouraged.

Miscellaneous Utilities

Currently underdocumented utilities that you might find useful.

In metapub.utils:

  • asciify (nuke all the unicode from orbit; it’s the only way to be sure)

  • parameterize (make strings suitable for submission to GET-based query service)

  • deparameterize (somewhat-undo parameterization in string)

  • remove_html_markup (remove html and xml tags from text. preserves HTML entities like &amp;)

  • hostname_of (returns hostname part of URL, e.g. ==>

  • rootdomain_of (returns the root domain of hostname of supplied URL, e.g.

In metapub.text_mining:

  • find_doi_in_string (returns the first seen DOI in the input string)

  • findall_dois_in_text (returns all seen DOIs in input string)

  • pick_pmid (return longest numerical string from text (string) as the pmid)

In metapub.convert:

  • PubMedArticle2doi (uses CrossRef to find a DOI for given PubMedArticle object.)

  • pmid2doi (returns first found doi for pubmed ID “by any means necessary.)

  • doi2pmid (uses CrossRef and eutils to return a PMID for given DOI if possible.)

In metapub.cite:

  • citation (constructs a research reference grade citation string from keyword arguments.)

  • article (interface to citation; formats as article.)

  • book (interface to citation; formats as book, e.g. GeneReviews)

More Information

Digital Identifiers of Scientific Literature: what they are, when they’re used, and what they look like.


Metapub relies on the very neat eutils package created by Reece Hart, which you can check out here:

Metapub has been in development since November 15, 2014, and has come quite a long way since then. Metapub has been deployed in production at many bioinformatics facilities (please tell me your story if you are among them!).

As of version 0.5.5, Metapub follows reasonably-strict Semantic Versioning which you can read about at

Metapub is developed and maintained by a small group of volunteers based out of San Francisco, CA. You are warmly welcome to contribute. Please read the Contributing section carefully, and feel free to contact the main author (Naomi Most, @nthmost) directly with questions, comments, suggestions, and swear words.

Contributing: Help Wanted!

The Metapub project consists of a small handful of committed volunteers (primarily the original author, @nthmost) tracking bugs and making contributions through GitHub.

We welcome all contributions big and small, from ambitious new features all the way down to a thumbs-up on a bug or improvement. Metapub is a highly detailed-oriented project that thrives with critical feedback.

If you’d like to contribute a new feature or bug fix, we ask that you open an issue at and give it as much detail as you can.

Please submit examples of the data that breaks your code and/or the new type of data or API that you wish Metapub would support. Examples are often crucial for reproducing bugs and for creating tests in the wake of a bug fix.

Extra special help is requested with the following items:

  • Logging more consistently – if you have a logging “philosophy” I’d love to hear from you.

  • Test coverage – especially clever testing strategies to handle data that change all the time.

Email inquiries to the maintainer address in this package. Or just submit a pull request.

About Python 2 and Python 3 Support

Alert: Metapub supports Python 3.x only from version 0.5.x onwards.

The LAST version of metapub to support Python 2.7 was (2017)

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