Are you interested in OmniPath data? Check out our R package OmnipathR, the most popular and most versatile access point to OmniPath, a database built from more than 150 original resources. If you use Python and don't need to build the database yourself, try our Python client. Read more about the web service here.
Pypath is the database builder of OmniPath. For most people the data distributed in OmniPath is satisfying (see above), they don't really need pypath. Typically you need pypath to:
- Build a custom or very fresh version of the OmniPath database(s)
- Use one of the utilities such as ID translation, homology translation, etc. (see the utils module)
- Access the raw or preprocessed data directly from the original resources (see the inputs module)
From PyPI:
pip install pypath-omnipathFrom Git:
pip install git+https://github.com/saezlab/pypath.gitRead the reference documentation or check out the tutorials. The most comprehensive guide to pypath is The Pypath Book.
Should you have a question or experiencing an issue, please write us by the Github issues page.
pypath is a Python module for processing molecular biology data resources, combining them into databases and providing a versatile interface in Python as well as exporting the data for access through other platforms such as R, web service, Cytoscape and BEL (Biological Expression Language).
pypath provides access to more than 100 resources! It builds 5 major combined databases and within these we can distinguish different datasets. The 5 major databases are interactions (molecular interaction network or pathways), enzyme-substrate relationships, protein complexes, molecular annotations (functional roles, localizations, and more) and inter-cellular communication roles.
pypath consists of a number of submodules and each of them again contains a number of submodules. Overall pypath consists of around 100 modules. The most important higher level submodules:
- pypath.core: contains the database classes e.g. network, complex, annotations, etc
- pypath.inputs: contains the resource specific methods which directly downlad and preprocess data from the original sources
- pypath.omnipath: higher level applications, e.g. a database manager, a web server
- pypath.utils: stand alone useful utilities, e.g. identifier translator, Gene Ontology processor, BioPax processor, etc
In the beginning the primary aim of pypath was to build networks from
multiple sources using an igraph object as the fundament of the integrated
data structure. From version 0.7 and 0.8 this design principle started to
change. Today pypath builds a number of different databases, exposes them
by a rich API and each of them can be converted to pandas.DataFrame.
The modules and classes responsible for the integrated databases are located
in pypath.core. The five main databases are the followings:
- network -
core.network - enzyme-substrate -
core.enz_sub - complexes -
core.complex - annotations -
core.annot - intercell -
core.intercell
Some of the databases have different variants (e.g. PPI and transcriptional network) and all can be customized by many parameters.
The databases above can be loaded by calling the appropriate classes.
However building the databases require time and memory so we want to avoid
building them more often than necessary or keeping more than one copies
in the memory. Some of the modules listed above have a method get_db
which ensures only one instance of the database is loaded. But there is a
more full featured database management system available in pypath,
this is the pypath.omnipath module. This module is able to build the
databases, automatically saves them to pickle files and loads them from
there in subsequent sessions. pypath comes with a number of database
definitions and users can add more. The pickle files are located by
default in the ~/.pypath/pickles/ directory. With the omnipath
module it's easy to get an instance of a database. For example to get the
omnipath PPI network dataset:
from pypath import omnipath
op = omnipath.db.get_db('omnipath')Important: Building the databases for the first time requires the
download of several MB or GB of data from the original resources. This
normally takes long time and is prone of errors (e.g. truncated or empty
downloads due to interrupted HTTP connection). In this case you should check
the log to find the path of the problematic cache file, check the contents
of this file to find out the reason and possibly delete the file to ensure
another download attempt when you call the database build again. Sometimes
the original resources change their content or go offline. If you encounter
such case please open an issue at https://github.com/saezlab/pypath/issues
so we can fix it in pypath. Once all the necessary contents are
downloaded and stored in the cache, the database builds are much faster,
but still can take minutes.
Apart from the databases, pypath has many submodules with standalone functionality which can be used in other modules and scripts. Below we present a few of these.
The ID conversion module utils.mapping translates between a large variety
of gene, protein, miRNA and small molecule ID types. It has the feature to
translate secondary UniProt ACs to primaries, and Trembl ACs to SwissProt,
using primary Gene Symbols to find the connections. This module automatically
loads and stores the necessary conversion tables. Many tables
are predefined, such as all the IDs in UniProt mapping service, while
users are able to load any table from file using the classes provided
in the module input_formats. An example how to translate identifiers:
from pypath.utils import mapping
mapping.map_name('P00533', 'uniprot', 'genesymbol')
# {'EGFR'}The pypath.utils.homology module is able to find the orthologs of genes
between two organisms. It uses data both from NCBI HomoloGene, Ensembl and
UniProt. This module is really simple to use:
from pypath.utils import homology
homology.translate('P00533', 10090) # translating the human EGFR to mouse
# ['Q01279'] # it returns the mouse Egfr UniProt ACIt is able to handle any ID type supported by pypath.utils.mapping.
Alternatively, you can access a complete dictionary of orthologous genes,
or translate columns in a pandas data frame.
Does it run on my old Python?
Most likely it doesn't. The oldest supported version, currently 3.9, is defined in our pyproject.toml.
Is there something similar in R?
OmniPath's R client, besides accessing data from OmniPath, provides many similar services as pypath: ID translation, homology translation, taxonomy support, GO support, and many more.
We prefer to keep all communication within the Github issues. About private or sensitive matters feel free to contact us by [email protected].
The development of pypath is coordinated by Dénes Türei in the
Saez Lab, with the contribution of developers and scientists from
other groups:
- Erva Ulusoy, Melih Darcan, Ömer Kaan Vural, Tennur Kılıç, Elif Çevrim, Bünyamin Şen, Atabey Ünlü and Mert Ergün in the HU Biological Data Science Lab (PI: Tunca Doğan) created many new input modules in pypath;
- Leila Gul, Dezső Módos, Márton Ölbei and Tamás Korcsmáros in the Korcsmaros Lab contributed to the overall design of OmniPath, the design and implementation of the intercellular communication database, and with various case studies and tutorials;
- Michael Klein from the group of Fabian Theis developed the Python client for the OmniPath web service;
- Charles Tapley Hoyt and Daniel Domingo-Fernández added the BEL export module.
- From the Saez Lab, Olga Ivanova introduced the resource manager in pypath, Sophia Müller-Dott added the CollecTRI gene regulatory network, while Nicolàs Palacio, Sebastian Lobentanzer and Ahmet Rifaioglu have done various maintenance and refactoring works. Aurelien Dugourd and Christina Schmidt helped with the design of the metabolomics related datasets and services.
- The R package and the Cytoscape app are developed and maintained by Francesco Ceccarelli, Attila Gábor, Alberto Valdeolivas, Dénes Türei and Nicolàs Palacio;
- The first logo of OmniPath has been designed by Jakob Wirbel (Saez Lab), the current logo by Dénes Türei, while the cover graphics for Nature Methods is the work of Spencer Phillips from EMBL-EBI.
See here a bird eye view of pypath's development history. For more details about recent developments see the Github releases.