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        "author": "Naomi Most",
        "author_email": "naomi@nthmost.com",
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        "description": "=======\nMetapub\n=======\n\nMetapub is a Python library that provides python objects fetched via eutils \nthat represent papers and concepts found within the NLM.\n\nThese objects abstract some interactions with pubmed, and intends to \nencompass as many types of database lookups and summaries as can be \nprovided via Eutils / Entrez.\n\nCHANGES!  NEW in 0.5:\n\n* NCBI_API_KEY supported as environment variable. Now you can send up to 10 queries per second to NCBI!\n* CrossRef API overhaul.  CrossRefFetcher objects source from newest Crossref API!\n* CrossRefWork object made to parallel PubMedArticle object.\n* Logic of building citations is now in citation.py and abstracted for multiple uses.\n* Python3 support only!  Python2 stuff completely ripped out!\n* Widespread use of Logging so you can see what's going on under the hood.\n* SUPER NEW!  Command line utilities -- see Getting Started below.\n\nHELP NEEDED WITH:\n\n* Logging more consistently -- if you have a logging \"philosophy\" I'd love to hear from you.\n* Test coverage -- especially clever testing strategies to handle data that change all the time.\n* Getting better at following Semantic Versioning (https://semver.org/)\n\nEmail inquiries to the maintainer address in this package. Or just submit a pull request.\n\nGetting Started\n===============\n\nMetapub is most easily installed using pip. It relies entirely on the arcane magic of classic Python setuptools::\n\n  pip install metapub\n\nWhen the package is installed, you'll have a few command line utilities available to you::\n\n  convert pmid2doi <pmid>\n  convert doi2pmid <doi>\n  convert bookid2pmid <ncbi_bookID>\n  pubmed_article <pmid>\n\nAll of these utilities contain their own help screens, by which you will be able to see options and usage.\n\nHere's an example::\n\n  convert pmid2doi 1000 -a -w \n\nOutput::\n\n    PMID:  1000\n    DOI:  10.1042/bj1490739\n\n    <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\n    <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\n\n\nHere's another example, converting an NCBI Book ID to its PMID (if it has one):\n\n`convert bookid2pmid NBK201366`\n\nOutput::\n\n  BookID:  NBK201366\n  PMID:  24830047\n\n\n\nPubMedArticle / PubMedFetcher\n=============================\n\nFirst, a note about the NCBI_API_KEY... You'll want to grab an API key if you're doing more than a few \nrequests per minute over here on the NCBI website: https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/\n\nTo load the API_KEY, do the following on the command line, or set this environment variable\nin your deployment script of choice:\n\n`export NCBI_API_KEY=\"Your_key_here\"`\n\nYou don't need to use an API Key, but without it you are limited to 3 queries per second.\n\nNow, on to PubMedFetcher basic usage::\n\n  fetch = PubMedFetcher()\n  article = fetch.article_by_pmid('123456')\n  print(article.title)\n  print(article.journal, article.year, article.volume, article.issue)\n  print(article.authors)\n  print(article.citation)\n\n\nPubMedFetcher uses an SQLite cacheing engine (provided through eutils), which by \ndefault places a file in your user directory.  E.g. the author's cache directory\npath would be */home/nthmost/.cache/eutils-cache.db*\n\nThis cache file can grow quite large over time. Deleting the cache file is safe\nand can also be regarded as the way to \"reset\" the cache.\n\nThe *cachedir* keyword argument can be supplied to PubMedFetcher as a way to specify\nwhere the cache file will reside.  For example::\n\n  fetch = PubMedFetcher(cachedir='/path/to/cachedir')\n\nUser directory expansion also works:\n\n  fetch = PubMedFetcher(cachedir='~/.othercachedir')\n\nThe cachedir will be created for you if it doesn't already exist, assuming the user \naccount you're running metapub under has permissions to do so.\n\nPubMedArticle Lookup Methods\n----------------------------\n\nThe following methods return a PubMedArticle object (or raise InvalidPMID if NCBI lookup fails).\n\n*article_by_pmid*\n\n      (Attempt to) fetch an article by supplying its pubmed ID (both integer and string accepted).\n\n*article_by_doi* \n\n      (Attempt to) fetch an article by looking up the DOI first.\n\n*article_by_pmcid* \n    \n      Fetch an article by looking up the PMCID first. Both integer and string accepted.\n\n\nPubmed ID List Methods\n----------------------\n\nThe following methods return a list of pubmed IDs (if found) or an empty list (if None).\n\n*pmids_from_citation*\n\n      Produces a list of possible PMIDs for the submitted\n      citation, where the citation is submitted as a collection of keyword\n      arguments.  At least 3 of the 5, preferably 4 or 5 for best results,\n      must be included::\n\n        aulast or author_last_fm1\n        year\n        volume\n        first_page or spage\n        journal or jtitle\n\n      Use NLM Title Abbreviation (aka ISO Abbreviation) journal strings whenever possible.\n\n\n*pmids_for_query*\n\n      Returns list of pmids for given freeform query string plus keyword arguments.\n            \n      All Pubmed Advanced Query tokens are supported.  \n\n      See [NCBI Search Field Descriptions and Tags](http://www.ncbi.nlm.nih.gov/books/NBK3827/)\n\n\n*pmids_for_clinical_query*\n\n      Composes a \"Clinical Query\" as on this page: (http://www.ncbi.nlm.nih.gov/pubmed/clinical/)\n\n      Supply a \"category\" (required) and an optimization (\"broad\" or \"narrow\") for this function.\n      Available categories:\n\n      * therapy\n      * diagnosis\n      * etiology\n      * prognosis\n      * prediction\n\n\n      All keyword arguments for PubMedFetcher.pmids_for_query available.\n\n\n*pmids_for_medical_genetics_query*\n\n      Composes a \"Medical Genetics Query\" as described here: (http://www.ncbi.nlm.nih.gov/books/NBK3827/#pubmedhelp.Medical_Genetics_Search_Filte)\n\n      Supply a \"category\" (required) and an optimization (\"broad\" or \"narrow\") for this function.\n      Available categories:\n\n      * therapy\n      * diagnosis\n      * etiology\n      * prognosis\n      * prediction\n\n\n      All keyword arguments for PubMedFetcher.pmids_for_query available.\n\n\nmetapub.pubmedcentral.* \n-----------------------\n\nThe PubMedCentral functions are a loose collection of conversion \nmethods for academic publishing IDs, allowing conversion (where possible)\nbetween the following ID types::\n\n    doi (Digital object identifier)\n    pmid (PubMed ID)\n    pmcid (Pubmed Central ID (including versioned document ID)\n\nThe following methods are supplied, returning a string (if found) or None::\n\n    get_pmid_for_otherid(string)\n    get_doi_for_otherid(string)\n    get_pmcid_for_otherid(string)\n\nAs implied by the function names, you can supply any valid ID type (\"otherid\")\nto acquire the desired ID type.\n\n\n\nMedGenConcept / MedGenFetcher\n=============================\n\nThe MedGen (medical genetics) database is a clinical dictionary linking medical concepts across multiple medical\nontologies and dictionaries such as OMIM and SNOMED.\n\nBasic usage::\n\n  from metapub import MedGenFetcher\n\n  fetch = MedGenFetcher()\n\n  concept = fetch.concept_by_uid('336867')\n  print(concept.name)\n  print(concept.description)\n  print(concept.associated_genes)\n  print(concept.modes_of_inheritance)\n  print(concept.OMIM)\n  print(concept.synonyms)\n\n\nClinVarVariation / ClinVarFetcher\n=================================\n\nThe ClinVar database contains information submitted by genetic researchers, labs, and testing companies around the world.\n\nInformation queryable using the ClinVarFetcher currently includes searching for the ID of a variant (\"Variation\") in the \ndatabase using an HGVS string and retrieving the Variant Summmary using a variation ID or HGVS string.\n\nSince Pubmed citations by Variation ID are also available by a cross-query between ClinVar and Pubmed, ClinVarFetcher\nallows retrieving PMIDs for given HGVS string.\n\nBasic usage::\n\n    clinvar = ClinVarFetcher()\n    cv = clinvar.variation_by_hgvs('NM_000249.3:c.1958T>G')\n    print(cv.variation_id)\n    print(cv.variation_name)\n    print(cv.genes)\n    print(cv.hgvs)\n    print(cv.molecular_consequences)\n\n    pubmed_citations = clinvar.pmids_for_hgvs('NM_000249.3:c.1958T>G')\n    print(pubmed_citations)\n\n\nCrossRefFetcher\n===============\n\nThe CrossRefFetcher object provides an object layer into search.crossref.org's API.\nSee http://search.crossref.org\n\nCrossRef is a service that excels at resolving DOIs into article citation details.  It can\nalso be used to resolve a DOI /from/ article citation details.\n\nOur interface to Crossref comes through the neat and clean habenero library by @sckott.\n\nIn metapub, the CrossRefFetcher object contains convenience methods into the crossref.works()\nquery that allows us to abstract away a lot of the string-handshaking between PubMedArticles\nand CrossRef and just get what we need as quickly and accurately as possible.\n\n\nBasic usage::\n\n  CR = CrossRefFetcher()       # starts the query cache engine\n  work = CR.article_by_title(\"Some great academic work of pure genius no doubt.\", params)\n\n  if work:\n    print(work)\n\n\nIn the above example, we just had a title.  Sometimes that's good enough to get a result, \nand sometimes it's not.  The above function will return the top result off the list without\na lot of introspection.\n\nThe next method, on the other hand, performs some fancy Levenshtein distance calculation and\nre-querying with different combos of parameters in order to drill down to a really precise \nresult.\n\nExample starting from a known pubmed ID::\n\n  pma = PubMedFetcher().article_by_pmid(known_pmid)\n  work = CR.article_by_pma(pma)\n\nIMPORTANT NOTE\n\nIn this minor version (0.5) of Metapub there is no CrossRefFetcher cache.  \nThis feature is coming back very ASAP.\n\n\nFindIt\n------\n\nLooking for an article PDF? Trying to gather a large corpus of research? \n\nThe FindIt object was designed to be able to locate the direct urls of as many different\narticles from as many different publishers of PubMed content as possible.\n\nAny article that is Open Access, whether it is in PubmedCentral or not, can potentially\nbe \"FindIt-able\".  Usage is simple::\n\n  from metapub import FindIt\n  src = FindIt('18381613')\n  print(src.url)\n\nYou can start FindIt from a DOI instead of a PMID by instantiating with FindIt(doi='10.1234/some.doi').  \n\nIf FindIt couldn't get a URL, you can take a look at the \"reason\" attribute to find out why. \nFor example::\n\n  src = FindIt('1234567')\n  if src.url is None: print(src.reason) \n\nThe FindIt object is cached (keyed to PMID), so while initialization the first time around \nfor a given PMID or DOI may take a few seconds, the second time this information is requested\nit will take far less time.\n\nIf you see a FindIt \"reason\" that starts with NOFORMAT, this is a great place to contribute\nsome help to metapub!  Feel free to dive in and submit a pull request, or contact the author\n(naomi@nthmost.com) for advice on how to fill in these gaps.\n\n\nUrlReverse\n----------\n\nStarting with a URL pointing to the abstract, pdf, or online fulltext of an article, UrlReverse\ncan \"reverse\" the DOI and/or the PubMed ID (pmid) of the article (assuming it can be found in\nPubMed).\n\nThe UrlReverse object provides an interface to the urlreverse logic, and it attributes hold \nstate for all of the information gathered and steps used to gather that information. \n\nUsage is very similar to FindIt::\n\n  from metapub import UrlReverse\n  urlrev = UrlReverse('http://onlinelibrary.wiley.com/doi/10.1002/humu.20708/pdf')\n  print(urlrev.pmid)\n  print(urlrev.doi)\n  print(urlrev.steps)\n\nUrlReverse is cached (keyed to URL); by default its cache db can be found in \n~/.cache/urlreverse-cache.db\n\nAs of metapub 0.4.3, there is no mechanism to have an item in cache expire. This is considered\na deficiency and will be remedied in a future version.\n\nThis is the newest feature in metapub (as of 0.4.2a0) and there is still much work to be done.\nThe world of biomedical literature URLs is fraught with inconsistencies and very weird URL\nformats.  UrlReverse could really benefit from being able to parse supplement URLs, for example.\n\nCollaboration and contributions heartily encouraged.\n\n\nMiscellaneous Utilities\n-----------------------\n\nCurrently underdocumented utilities that you might find useful.\n\nIn metapub.utils:\n\n  * *asciify* (nuke all the unicode from orbit; it's the only way to be sure)\n  * *parameterize* (make strings suitable for submission to GET-based query service)\n  * *deparameterize* (somewhat-undo parameterization in string)\n  * *remove_html_markup* (remove html and xml tags from text. preserves HTML entities like &amp;)\n  * *hostname_of* (returns hostname part of URL, e.g. http://blood.oxfordjournals.org/stuff ==> blood.oxfordjournals.org)\n  * *rootdomain_of* (returns the root domain of hostname of supplied URL, e.g. oxfordjournals.org)\n\n\nIn metapub.text_mining:\n\n  * *find_doi_in_string* (returns the first seen DOI in the input string)\n  * *findall_dois_in_text* (returns all seen DOIs in input string)\n  * *pick_pmid* (return longest numerical string from text (string) as the pmid)\n\n\nIn metapub.convert:\n\n  * *PubMedArticle2doi* (uses CrossRef to find a DOI for given PubMedArticle object.)\n  * *pmid2doi* (returns first found doi for pubmed ID \"by any means necessary.)\n  * *doi2pmid* (uses CrossRef and eutils to return a PMID for given DOI if possible.)\n\n\nIn metapub.cite:\n\n  * *citation* (constructs a research reference grade citation string from keyword arguments.)\n  * *article*  (interface to citation; formats as article.)\n  * *book*     (interface to citation; formats as book, e.g. GeneReviews)\n\n\n\n\nMore Information\n----------------\n\nDigital Identifiers of Scientific Literature: what they are, when they're \nused, and what they look like.\n\nhttp://www.biosciencewriters.com/Digital-identifiers-of-scientific-literature-PMID-PMCID-NIHMS-DOI-and-how-to-use-them.aspx\n\n\nAbout, and a Disclaimer\n-----------------------\n\nMetapub relies on the very neat eutils package created by Reece\nHart, which you can check out here:\n\nhttp://bitbucket.org/biocommons/eutils\n\nMetapub has been in development since November 15, 2014, and has come quite a long\nway in a short time. Metapub has been deployed in production at many bioinformatics \nfacilities (please tell me your story if you are among them!).\n\nFeel free to use the library with confidence that each released version is well tested \nand battle-hardened from extensive use, but until (say) version 0.5, don't expect \ntotal consistency between versions.\n\nYMMV, At your own risk, etc.  Please do report bugs and bring your comments and \nsuggestions to the bitbucket home for metapub at:\n\nhttps://bitbucket.org/metapub/metapub\n\n--Naomi Most (@nthmost)\n\n\nAbout Python 2 and Python 3 Support\n-----------------------------------\n*Alert*: Metapub supports Python 3.x only from version 0.5.x onwards.\n\nThe LAST version of metapub to support Python 2.7 was 0.4.3.6 (2017)",
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