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
pypathwhich is not based onigraphany more and provides a modular and versatile access interface to the network data (since version0.9). In January we reorganized the submodules inpypathin order to create a clear structure (since version0.10). These are important milestones towards version1.0and we hope they will makepypathmore 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 oldigraphbased network class is still available in thepypath.legacymodule.
| 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+. |
|---|---|
| documentation: | https://saezlab.github.io/pypath |
| issues: | https://github.com/saezlab/pypath/issues |
| contact: | [email protected] |
| developers: | pypath is developed in the Saez Lab (https://saezlab.org) 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; https://github.com/saezlab/OmnipathR), web service (at https://omnipathdb.org), Cytoscape (the OmniPath Cytoscape app; https://apps.cytoscape.org/apps/omnipath) 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.
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.
The instance of the pypath webserver running at the domain
https://omnipathdb.org/, 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 qualitypathwayextra: activity flow interactions without literature referencekinaseextra: enzyme-substrate interactions without literature referenceligrecextra: ligand-receptor interactions without literature referencedorothea: transcription factor (TF)-target interactions from DoRothEAtf_target: transcription factor (TF)-target interactions from other sourcesmirnatarget: 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.
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:
https://omnipathdb.org
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:
https://omnipathdb.org/info
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.
https://omnipathdb.org/interactions/?partners=P00533&fields=sources,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:
https://omnipathdb.org/interactions/?targets=EGFR&types=transcriptional
The DoRothEA database assigns confidence levels to the interactions. You might want to select only the highest confidence, A category:
https://omnipathdb.org/interactions/?targets=EGFR&types=transcriptional&dorothea_levels=A
Show the transcriptional targets of Smad2 homology translated to rat including the confidence levels from TF Regulons:
https://omnipathdb.org/interactions/?genesymbols=1&fields=type,ncbi_tax_id,dorothea_level&organisms=10116&sources=Smad2&types=transcriptional
Query interactions from PhosphoNetworks which is part of the kinaseextra dataset:
https://omnipathdb.org/interactions/?genesymbols=1&fields=sources&databases=PhosphoNetworks&datasets=kinaseextra
Get the interactions from Signor, SPIKE and SignaLink3:
https://omnipathdb.org/interactions/?genesymbols=1&fields=sources,references&databases=Signor,SPIKE,SignaLink3
All interactions of MAP1LC3B:
https://omnipathdb.org/interactions/?genesymbols=1&partners=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:
https://omnipathdb.org/interactions/?genesymbols=1&fields=sources,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:
https://omnipathdb.org/interactions/?genesymbols=1&fields=sources,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.
Another query type available is ptms which provides enzyme-substrate
interactions. It is very similar to the interactions:
https://omnipathdb.org/enz_sub?genesymbols=1&fields=sources,references,isoforms&enzymes=FYN
Is there any ubiquitination reaction?
https://omnipathdb.org/ens_sub?genesymbols=1&fields=sources,references&types=ubiquitination
And acetylation in mouse?
https://omnipathdb.org/ptms?genesymbols=1&fields=sources,references&types=acetylation&organisms=10090
Rat interactions, both directly from rat and homology translated from human, from the PhosphoSite database:
https://omnipathdb.org/enz_sub?genesymbols=1&fields=sources,references&organisms=10116&databases=PhosphoSite,PhosphoSite_noref
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):
https://omnipathdb.org/complexes?proteins=P42345&databases=CORUM,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:
https://omnipathdb.org/annotations?databases=SignaLink_pathway
Or the tissue expression of BMP7 from Human Protein Atlas:
https://omnipathdb.org/annotations?databases=HPA_tissue&proteins=BMP7
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 https://omnipathdb.org/queries/intercell
Some example queries:
https://omnipathdb.org/intercell?proteins=EGFR,ULK1,ATG4A,BMP8B
All the resource specific functional classes for one protein:
https://omnipathdb.org/intercell?source=resource_specific&aspect=functional&proteins=P00533
A list of all ECM proteins:
https://omnipathdb.org/intercell?categories=ecm
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:
https://omnipathdb.org/queries/interactions
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:
https://github.com/saezlab/OmnipathR
Warning: pip install pypath installs another package, you find
pypath in PyPI under the name pypath-omnipath:
pip install pypath-omnipathIn 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.
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.
pip install git+https://github.com/saezlab/pypath.gitDownload the package from /dist, and install with pip:
pip install pypath-x.y.z.tar.gzClone the git repo, and run setup.py:
python setup.py sdistRecently 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 mac-install-brew.sh installs everything with HomeBrew and
mac-install-conda.sh 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
mac-install-source.sh 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? Tryecho $PYTHONPATHto see the current paths. Add your local install directories if those are not there, e.g.export PYTHONPATH="/Users/me/local/python2.7/site-packages:$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.pkgconfignot found. Check if the$PKG_CONFIG_PATHvariable 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_PATHbefore. See mac-install-source.sh 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 mac-install-source.sh how to set include dirs and library dirs by--global-optionparameters. - 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 mac-install-source.sh.
- 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. mac-install-conda.sh 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 modulespyopenssland its dependencycryptographyon some systems the development headers of OpenSSL need to be available. This is not the case if you can installpyopensslfrom a wheel. If you get an error about a missing libssl header, just install the appropriate packages, in Debian based distros these arelibssl-devandlibffi-dev, in Red Hat based distrosopenssl-develandlibffi-devel. In Mac OS X installopensslbyhomebrew.
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.
The same workflow like you see in mac-install-conda.sh 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 mac-install-conda.sh, and
modify the channel if necessary, until all packages install successfully.
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.
- "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!
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)
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 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'}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