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Molecular signaling prior knowledge processing

Project description


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 110 original resources. If you use Python and don’t need to build the database yourself, try our Python client.

Important: New module structure and new network API (January 2020)

Around the end of December we added a new network API to pypath which is not based on igraph any more and provides a modular and versatile access interface to the network data (since version 0.9). In January we reorganized the submodules in pypath in order to create a clear structure (since version 0.10). These are important milestones towards version 1.0 and we hope they will make pypath more convenient to use for everyone. By 18 February we merged these changes to the master branch however the pypath guide is still to be updated. Apologies for this inconvenience and please don’t hesitate to ask questions by opening an issue on github. The old igraph based network class is still available in the pypath.legacy module.

Py2/3:Although we still keep the compatibility with Python 2, we don’t test pypath in this environment and already very few people use Python 2. We highly recommend to use pypath in Python 3.6+.
developers:pypath is developed in the Saez Lab ( by Ahmet Rifaioglu, Sebastian Lobentanzer and Dénes Türei. Olga Ivanova and Nicolàs Palacio also contributed in the past. The R package and the Cytoscape app are developed and maintained by Francesco Ceccarelli, Attila Gábor, Alberto Valdeolivas, Dénes Türei and Nicolàs Palacio. The Python client for the OmniPath web service has been developed and is maintained by Michael Klein in the group of Fabian Theis.

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 the R (the OmnipathR R/Bioconductor package;, web service (at, Cytoscape (the OmniPath Cytoscape app; 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


New webservice from 14 June 2018: the queries slightly changed, have been largely extended. See the examples below.

The webservice implements a very simple REST style API, you can make requests by the HTTP protocol (browser, wget, curl or whatever). After defining the query type and optionally a set of molecular entities (proteins) you can add further GET parameters encoded in the URL.

Query types

The webservice currently recognizes 7 types of queries: interactions, enz_sub, annotations, complexes, intercell, queries and info. The query types resources, network and about have not been implemented yet in the new webservice.

Interaction datasets

The instance of the pypath webserver running at the domain, serves not only the OmniPath data but also other datasets. Each of them has a short name what you can use in the queries (e.g. &datasets=omnipath,pathwayextra).

  • omnipath: the OmniPath data as defined in the paper, an arbitrary optimum between coverage and quality
  • pathwayextra: activity flow interactions without literature reference
  • kinaseextra: enzyme-substrate interactions without literature reference
  • ligrecextra: ligand-receptor interactions without literature reference
  • dorothea: transcription factor (TF)-target interactions from DoRothEA
  • tf_target: transcription factor (TF)-target interactions from other sources
  • mirnatarget: miRNA-mRNA and TF-miRNA interactions

TF-target interactions from DoRothEA, a large collection additional enzyme-substrate interactions, and literature curated miRNA-mRNA interacions combined from 4 databases.

Mouse and rat

Except the miRNA interactions all interactions are available for human, mouse and rat. The rodent data has been translated from human using the NCBI Homologene database. Many human proteins do not have known homolog in rodents hence rodent datasets are smaller than their human counterparts. Note, if you work with mouse omics data you might do better to translate your dataset to human (for example using the pypath.homology module) and use human interaction data.


A request without any parameter provides the main webpage:

The info returns a HTML page with comprehensive information about the resources. The list here should be and will be updated as currently OmniPath includes much more databases:

Molecular interaction network

The interactions query accepts some parameters and returns interactions in tabular format. This example returns all interactions of EGFR (P00533), with sources and references listed.,references

By default only the OmniPath dataset used, to include any other dataset you have to set additional parameters. For example to query the transcriptional regulators of EGFR:

The DoRothEA database assigns confidence levels to the interactions. You might want to select only the highest confidence, A category:

Show the transcriptional targets of Smad2 homology translated to rat including the confidence levels from TF Regulons:,ncbi_tax_id,dorothea_level&organisms=10116&sources=Smad2&types=transcriptional

Query interactions from PhosphoNetworks which is part of the kinaseextra dataset:

Get the interactions from Signor, SPIKE and SignaLink3:,references&databases=Signor,SPIKE,SignaLink3

All interactions of MAP1LC3B:

By default partners queries the interaction where either the source or the arget is among the partners. If you set the source_target parameter to AND both the source and the target must be in the queried set:,references&sources=ATG3,ATG7,ATG4B,SQSTM1&targets=MAP1LC3B,MAP1LC3A,MAP1LC3C,Q9H0R8,GABARAP,GABARAPL2&source_target=AND

As you see above you can use UniProt IDs and Gene Symbols in the queries and also mix them. Get the miRNA regulating NOTCH1:,references&datasets=mirnatarget&targets=NOTCH1

Note: with the exception of mandatory fields and genesymbols, the columns appear exactly in the order you provided in your query.

Enzyme-substrate interactions

Another query type available is ptms which provides enzyme-substrate interactions. It is very similar to the interactions:,references,isoforms&enzymes=FYN

Is there any ubiquitination reaction?,references&types=ubiquitination

And acetylation in mouse?,references&types=acetylation&organisms=10090

Rat interactions, both directly from rat and homology translated from human, from the PhosphoSite database:,references&organisms=10116&databases=PhosphoSite,PhosphoSite_noref

Molecular complexes

The complexes query provides a comprehensive database of more than 22,000 protein complexes. For example, to query all complexes from CORUM and PDB containing MTOR (P42345):,PDB


The annotations query provides a large variety of data about proteins, complexes and in the future other kinds of molecules. For example an annotation can tell if a protein is a kinase, or if it is expressed in the hearth muscle. These data come from dozens of databases and each kind of annotation record contains different fields. Because of this here we have a record_id field which is unique within the records of each database. Each row contains one key value pair and you need to use the record_id to connect the related key-value pairs. You can easily do this with tidyr and dplyr in R or pandas in Python. An example to query the pathway annotations from SignaLink:

Or the tissue expression of BMP7 from Human Protein Atlas:

Roles in inter-cellular communication

Another query type is the intercell, providing information about the roles in inter-cellular signaling. E.g. if a protein is a ligand, a receptor, an extracellular matrix (ECM) component, etc. The proteins and protein complexes are classified into categories. The categories are defined by a number of attributes:

  • aspect: funtional (e.g. ion channel) or locational (e.g. plasma membrane transmembrane).
  • scope: generic (e.g. ligand) or specific (e.g. interleukin)
  • source: resource specific (from one resource) or composite (combined from more resources)
  • causality: transmitter (delivering signal from the expressing cell) or receiver (receiving signal into the expressing cell) or both
  • topology: major localization categories derived from the locational categories: plasma membrane transmembrane or peripheral or secreted

The intercell database defines 25 functional and 10 locational generic, composite categories. The number of specific categories is above 1,000.

You can use all these attributes in your queries, see the exact keys and values at

Some example queries:,ULK1,ATG4A,BMP8B

All the resource specific functional classes for one protein:

A list of all ECM proteins:

Exploring possible parameters

Sometimes the names and values of the query parameters are not intuitive, even though in many cases the server accepts multiple alternatives. To see the possible parameters with all possible values you can use the queries query type. The server checks the parameter names and values exactly against these rules and if any of them don’t match you will get an error message instead of reply. To see the parameters for the interactions query:

Can I use OmniPath in R?

You can download the data from the webservice and load into R. Thanks to our colleague Attila Gabor we have a dedicated package for this:


Warning: pip install pypath installs another package, you find pypath in PyPI under the name pypath-omnipath:

pip install pypath-omnipath


In almost any up-to-date Linux distribution the dependencies of pypath are built-in, or provided by the distributors. You can simply install pypath by pip (see below). If any non mandatory dependency is still missing, you can install them the usual way by pip or your package manager.

igraph C library, cairo and pycairo

For the legacy network class or the igraph conversion from the current network class python-igraph must be installed. python(2)-igraph is a Python interface to use the igraph C library. The C library must be installed. The same goes for cairo, py(2)cairo and graphviz.

Directly from git

pip install git+

With pip

Download the package from /dist, and install with pip:

pip install pypath-x.y.z.tar.gz

Build source distribution

Clone the git repo, and run

python sdist

Mac OS X

Recently the installation on Mac should not be more complicated than on Linux: you can simply install by pip (see above).

When igraph was a mandatory dependency and it didn’t provide wheels the OS X installation was not straightforward primarily because cairo needs to be compiled from source. If you want igraph and cairo we provide two scripts in scripts: the installs everything with HomeBrew and installs from Anaconda distribution. With these scripts, installation of igraph, cairo and graphviz goes smoothly most of the time and options are available to omit the last two. To know more, see the description in the script header. There is a third script which compiles everything from source and presumes only Python 2.7 and Xcode installed. We do not recommend this as it is time consuming and troubleshooting requires expertise.


  • no module named ... when you try to load a module in Python. Did the installation of the module run without error? Try to run again the specific part from the mac install shell script to see if any error comes up. Is the path where the module has been installed in your $PYTHONPATH? Try echo $PYTHONPATH to see the current paths. Add your local install directories if those are not there, e.g. export PYTHONPATH="/Users/me/local/python2.7/site-packages:$PYTHONPATH". If it works afterwards, don’t forget to append these export path statements to your ~/.bash_profile, so these will be set every time you launch a new shell.
  • pkgconfig not found. Check if the $PKG_CONFIG_PATH variable is set correctly, and pointing on a directory where pkgconfig really can be found.
  • Error while trying to install py(2)cairo by pip. py(2)cairo could not be installed by pip, but only by waf. Please set the $PKG_CONFIG_PATH before. See on how to install with waf.
  • Error at pygraphviz build: graphviz/cgraph.h file not found. This is because the directory of graphviz detected wrong by pkgconfig. See how to set include dirs and library dirs by --global-option parameters.
  • Can not install bioservices, because installation of jurko-suds fails. Ok, this fails because pip is not able to install the recent version of setuptools, because a very old version present in the system path. The development version of jurko-suds does not require setuptools, so you can install it directly from git as it is done in
  • In Anaconda, pypath can be imported, but the modules and classes are missing. Apparently Anaconda has some built-in stuff called pypath. This has nothing to do with this module. Please be aware that Anaconda installs a completely separated Python distribution, and does not detect modules in the main Python installation. You need to install all modules within Anaconda’s directory. does exactly this. If you still experience issues, please contact us.
  • error: openssl/ssl.h: No such file or directory: In order to install the Python modules pyopenssl and its dependency cryptography on some systems the development headers of OpenSSL need to be available. This is not the case if you can install pyopenssl from a wheel. If you get an error about a missing libssl header, just install the appropriate packages, in Debian based distros these are libssl-dev and libffi-dev, in Red Hat based distros openssl-devel and libffi-devel. In Mac OS X install openssl by homebrew.

Microsoft Windows

Not many people have used pypath on Microsoft computers so far. Please share your experiences and contact us if you encounter any issue. We appreciate your feedback, and it would be nice to have better support for other computer systems.

With Anaconda

The same workflow like you see in should work for Anaconda on Windows. The only problem you certainly will encounter is that not all the channels have packages for all platforms. If certain channel provides no package for Windows, or for your Python version, you just need to find an other one. For this, do a search:

anaconda search -t conda <package name>

For example, if you search for pycairo, you will find out that vgauther provides it for osx-64, but only for Python 3.4, while richlewis provides also for Python 3.5. And for win-64 platform, there is the channel of KristanAmstrong. Go along all the commands in, and modify the channel if necessary, until all packages install successfully.

With other Python distributions

Here the basic principles are the same as everywhere: first try to install all external dependencies, after pip install should work. On Windows certain packages can not be installed by compiled from source by pip, instead the easiest to install them precompiled. These are in our case fisher, lxml, numpy (mkl version), pycairo, igraph, pygraphviz, scipy and statsmodels. The precompiled packages are available here. We tested the setup with Python 3.4.3 and Python 2.7.11. The former should just work fine, while with the latter we have issues to be resolved.

Known issues

  • “No module fabric available.” – or pysftp missing: this is not important, only certain data download methods rely on these modules, but likely you won’t call those at all.
  • Progress indicator floods terminal: sorry about that, will be fixed soon.
  • Encoding related exceptions in Python2: these might occur at some points in the module, please send the traceback if you encounter one, and we will fix as soon as possible.
  • For Mac OS X (v >= 10.11 El Capitan) import of pypath fails with error: “libcurl link-time ssl backend (openssl) is different from compile-time ssl backend (none/other)”. To fix it, you may need to reinstall pycurl library using special flags. More information and steps can be found here.

Special thanks to Jorge Ferreira for testing pypath on Windows!

Release History

Main improvements in the past releases:


  • First release of PyPath, for initial testing.


  • Lots of small improvements in almost every module
  • Networks can be read from local files, remote files, lists or provided by any function
  • Almost all redistributed data have been removed, every source downloaded from the original provider.


  • First version with partial Python 3 support.


  • pyreact module with BioPaxReader and PyReact classes added
  • Process description databases, BioPax and PathwayCommons SIF conversion rules are supported
  • Format definitions for 6 process description databases included.


  • Many classes have been added to the plot module
  • All figures and tables in the manuscript can be generated automatically
  • This is supported by a new module, analysis, which implements a generic workflow in its Workflow class.


  • chembl, unichem, mysql and mysql_connect modules made Python3 compatible


  • Orthology translation of network
  • Homologene UniProt dict to translate between different organisms UniProt-to-UniProt
  • Orthology translation of PTMs
  • Better processing of PhosphoSite regulatory sites


  • TF-target, miRNA-mRNA and TF-miRNA interactions from many databases


  • New web server based on pandas data frames
  • New module export for generating data frames of interactions or enzyme-substrate interactions
  • New module websrvtab for exporting data frames for the web server
  • TF-target interactions from DoRothEA


  • New dataio methods for Gene Ontology


  • Many new docstrings


  • New module complex: a comprehensive database of complexes
  • New module annot: database of protein annotations (function, location)
  • New module intercell: special methods for data integration focusing on intercellular communication
  • New module bel: BEL integration
  • Module go and all the connected dataio methods have been rewritten offering a workaround for data access despite GO’s terrible web services and providing much more versatile query methods
  • Removed MySQL support (e.g. loading mapping tables from MySQL)
  • Modules mapping, reflists, complex, ptm, annot, go became services: these modules build databases and provide query methods, sometimes they even automatically delete data to free memory
  • New interaction category in data_formats: ligand_receptor
  • Improved logging and control over verbosity
  • Better control over parameters by the settings module
  • Many methods in dataio have been improved or fixed, docs and code style largely improved
  • Started to add tests especially for methods in dataio


  • The network database is not dependent any more on python-igraph hence it has been removed from the mandatory dependencies
  • New API for the network, interactions, evidences, molecular entities


  • New module structure: modules grouped into core, inputs, internals, legacy, omnipath, resources, share and utils submodules.


  • Redesign of the intercell (intercellular communication roles) database


  • New, more flexible network reader class
  • Full support for multi-species molecular interaction networks (e.g. pathogene-host)
  • Better support for not protein only molecular interaction networks (metabolites, drug compounds, RNA)


Integrated databases

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 -
  • 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.

Database management

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 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.

ID conversion

The ID conversion module utils.mapping translates between a large variety of gene, protein and miRNA 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'}

Homology translation

The pypath.utils.homology module is able to find the homologues of genes between two organisms. It uses data from NCBI HomoloGene, and soon we will extend it to use Ensembl or UniProt as alternatives. 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

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