Molecular signaling prior knowledge processing
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.
Do you need pypath?
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:
pip install pypath-omnipath
pip install git+https://github.com/saezlab/pypath.git
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.
Further modules in pypath
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')
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 AC
It 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?
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;
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 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.
History and releases
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