Synopsis

A tool to parse MEDLINE XML files or download eUtils’ PubMed XML, bootstrapping a MEDLINE/PubMed database store, updating and/or deleting the records, and writing the contents of selected PMIDs into flat-files.

Entity Relationship Model

[Author] → [Medline] ← [Descriptor] ← [Qualifier]
            ↑     ↑
  [Identifier]   [Section]  [Database]  [Chemical]

Medline (records)

pmid:BIGINT, status:ENUM(state), journal:VARCHAR(256), created:DATE, completed:DATE, revised:DATE, modified:DATE

Author (authors)

pmid:FK(Medline), pos:SMALLINT, name:TEXT, initials:VARCHAR(128), forename:VARCHAR(128), suffix:VARCHAR(128),

Descriptor (descriptors)

pmid:FK(Medline), pos:SMALLINT, name:TEXT, major:BOOL

Qualifier (qualifiers)

pmid:FK(Descriptor), pos:FK(Descriptor), sub:SMALLINT, name:TEXT, major:BOOL

Identifier (identifiers)

pmid:FK(Medline), namespace:VARCHAR(32), value:VARCHAR(256)

Database (databases)

pmid:FK(Medline), name:VARCHAR(32), accession:VARCHAR(256)

Chemical (chemicals)

pmid:FK(Medline), num:VARCHAR(32), uid:VARCHAR(256), name:VARCHAR(256)

Section (sections)

pmid:FK(Medline), seq:SMALLINT, name:ENUM(section), label:VARCHAR(256), content:TEXT

• bold (Composite) Primary Key

• italic NOT NULL

Supported PubMed XML Elements

Requirements

• Python 3.2+

• SQL Alchemy 0.7+

• any database SQL Alchemy can work with

Note that while any SQL Alchemy DB will work, it is strongly discouraged to use any other combination that PostgeSQL and psycogp2, because it is the only combination in SQL Alchemy where data streaming from the DB actually works. You can use other DBs for small MEDLINE collections, but in general, for now, it is recommended to stick to this combo.

Notice: VersionID

MEDLINE has began to use versions to allow publishers to add multiple citations for the same PMID. This only occurs with 71 articles from one journal, “PLOS Curr”, in the 2013 baseline, creating a total of 149 non-unique records.

As this is the only journal and as there should only be one abstract per publication in the database, alternative versions are currently being ignored. In other words, if a MedlineCitation has a VersionID value, that records can be skipped to avoid DB errors from non-unique records.

In short, this tool currently only removes alternate citations.

Setup

If you are not using pip install medic, install all dependencies/requirements:

pip install argparse # only for python3 < 3.2
pip install sqlalchemy
pip install psycopg2 # optional, can use any other DB driver

Create the PostreSQL database (optional):

createdb medline

Usage

medic [options] COMMAND PMID|FILE...

The --url URL option represents the DSN of the database and might be needed (default: postgresql://localhost/medline); For example:

Postgres

postgresql://host//dbname

SQLite

sqlite:////absolute/path/to/foo.db or sqlite:///relative/path/to/foo.db

The tool has five COMMAND options:

insert

create records in the DB by parsing MEDLINE XML files or by downloading PubMed XML from NCBI eUtils for a list of PMIDs

write

write records as plaintext files to a directory, each file named as “<pmid>.txt”, and containing most of the DB stored content or just the TIAB (title and abstract). In addition, summary files in TSV and HTML format can be generated (see option --format).

update

insert or update records in the DB (instead of creating them); note that if a record exists, but is added with create, this would throw an IntegrityError. If you are not sure if the records are in the DB or not, use update (N.B. that update is slower).

delete

delete records from the DB for a list of PMIDs

parse

does not interact with the DB, but rather creates “.tab” files for each table that later can be used to load a database, particularly useful when bootstrapping a large collection

For example, to download two PubMed records by PMID and put them into the DB:

medic update 1000 123456

To add a MEDLINE XML update file to the DB:

medic parse --update medline14n1234.xml.gz
psql medline -f delete.sql
# load all tables; see below

Add a single MEDLINE XML file quickly to the database:

medic insert medline13n0001.xml.gz

Export a few records from the database into a HTML file:

medic write --format html 292837491 128374 213487

Note that in the above examples, because of the suffix “.gz”, the parser automatically decompresses the file(s) first. This feature only works with GNU-zipped files and the “.gz” suffix must be present.

Therefore, command line arguments are treated as follows:

integer values

are always treated as PMIDs to download PubMed XML data

all other values

are always treated as MEDLINE XML files to parse

values ending in “.gz”

are always treated as gzipped MEDLINE XML files

Loading the MEDLINE baseline

Please be aware that the MEDLINE baseline is not unique, meaning that it contains a few records multiple times (see the above notice about the VersionID above).

For example, in the 2013 baseline, PMID 20029614 is present ten times in the baseline, each version at a different stage of revision. Because it is the first entry (in the order they appear in the baseline files) without a VersionID that seems to be the relevant record, it medic by default filters citations with other versions than “1”. If you want to actually parse other versions of a citation, use the option --all.

To quickly load a parsed dump into a PostgreSQL DB on the same machine, do:

for table in records descriptors qualifiers authors sections databases \
identifiers chemicals;
  do psql medline -c "COPY $table FROM '`pwd`/${table}.tab';";
done

For the update files, you need to go one-by-one, adding them in order, and using the flag --update when parsing the XML. After parsing an XML file and before loading the dumps, run psql medline -f delete.sql to get rid of all entities that are being updated or should be removed (PMIDs listed as DeleteCitations).