pypath: A Python module for molecular signaling prior knowledge processing¶
pypathsupports both Python 2.7 and Python 3.6+. In the beginning, pypath has been developed only for Python 2.7. Then the code have been adjusted to Py3 and for a few years we develop and test
pypathin Python 3. Therefore this is the better supported Python variant.
- Release history
pypath consists of a number of submodules to build various databases. Most of these are provided as pandas data frames. The network database is built around igraph to work with molecular network representations e.g. protein, miRNA and drug compound interaction networks.
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:
The query types
about have not been
implemented yet in the new webservice.
The instance of the
pypath webserver running at the domain
http://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
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
tfregulons: transcription factor (TF)-target interactions from DoRothEA
mirnatarget: miRNA-mRNA and TF-miRNA interactions
TF-target interactions from TF Regulons, 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
A request without any parameter provides the main webpage:
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¶
interactions query accepts some parameters and returns interactions in
tabular format. This example returns all interactions of EGFR (P00533), with
sources and references listed.
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 TF Regulons 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:
Query interactions from PhosphoNetworks which is part of the kinaseextra dataset:
Get the interactions from Signor, SPIKE and SignaLink3:
All interactions of MAP1LC3B:
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:
As you see above you can use UniProt IDs and Gene Symbols in the queries and also mix them. Get the miRNA regulating 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
Is there any ubiquitination reaction?
And acetylation in mouse?
Rat interactions, both directly from rat and homology translated from human, from the PhosphoSite database:
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):
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
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
to connect the related key-value pairs. You can easily do this with
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
intercell providing information on the roles in
inter-cellular signaling. E.g. if a protein is a ligand, a receptor, an
extracellular matrix (ECM) component, etc. This query type is very similar
annotations but here the data does not come from original sources but
combined from several databases by us. However we refer also to the original
databases whenever the
sub (subclass). E.g. the main
ligand is a combination of
HPMR and many other databases, hence besides the
ligand category you
will find sub-categories like
and so on. An example how to get all intercell annotations for 4 selected
Or all the main classes for one protein:
Or 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
query type. The server checks the paremeter 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
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:
Alternatively here is a very simple example:
In almost any up-to-date Linux distribution the dependencies of pypath are built-in, or provided by the distributors. You only need to install a couple of things in your package manager (cairo, py(2)cairo, igraph, python(2)-igraph, graphviz, pygraphviz), and after install pypath by pip (see below). If any module still missing, you can install them the usual way by pip or your package manager.
igraph C library, cairo and pycairo¶
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+https://github.com/saezlab/pypath.git
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 setup.py:
python setup.py sdist
Mac OS X¶
On OS X installation is not straightforward primarily because cairo needs to be compiled from source. We provide 2 scripts here: 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 for omitting the 2 latter. 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 theinstallation 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
echo $PYTHONPATHto see the current paths. Add your local install directories if those are not there, e.g.
export PYTHONPATH="/Users/me/local/python2.7/site-packages:$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
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.
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
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: http://www.lfd.uci.edu/~gohlke/pythonlibs/. 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 e.g. [here](https://cscheng.info/2018/01/26/installing-pycurl-on-macos-high-sierra.html)
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 paremeters 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, 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 each having pandas.DataFrame as a final format. Each of these integrates a specific kind of data from various databases (e.g. protein complexes, interactions, enzyme-PTM relationships, etc). pypath has many submodules with standalone functionality which can be used in other modules and scripts. For example the ID conversion module pypath.mapping.
Submodules perform various features, e.g. graph visualization, working with rug compound data, searching drug targets and compounds in ChEMBL.
The ID conversion module
mapping can be used independently. It has the
feature to translate secondary UniProt IDs to primaries, and Trembl IDs 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 or MySQL, using the classes
provided in the module
pypath includes data and predefined format descriptions for more than 25
high quality, literature curated databases. The inut formats are defined in
data_formats module. For some resources data downloaded on the fly,
where it is not possible, data is redistributed with the module. Descriptions
and comprehensive information about the resources is available in the
One of the modules called
intera provides many classes for representing
structures and mechanisms behind protein interactions. These are
Interface. All these classes have
methods to test equality between instances, and also
methods to look up easily if a residue is within a short motif or protein
domain, or is the target residue of a PTM.
seq contains a simple class for quick lookup any residue or
segment in UniProt protein sequences while being aware of isoforms.
For 3 protein expression databases there are functions and modules for
downloading and combining the expression data with the network. These are the
Human Protein Atlas, the ProteomicsDB and GIANT. The
proteomicsdb modules can be used also as stand alone Python clients for
GSEA and Gene Ontology are two approaches for annotating genes and
gene products, and enrichment analysis technics aims to use these annotations
to highlight the biological functions a given set of genes is related to. Here
enrich module gives abstract classes to calculate enrichment
statistics, while the
go and the
gsea modules give access to GO and
GSEA data, and make it easy to count enrichment statistics for sets of genes.
UniChem submodule provides an interface to effectively query the UniChem service, use connectivity search with custom settings, and translate SMILEs to ChEMBL IDs with ChEMBL web service.
ChEMBL submodule queries directly your own ChEMBL MySQL instance, has the features to search targets and compounds from custom assay types and relationship types, to get activity values, binding domains, and action types. You need to download the ChEMBL MySQL dump, and load into your own server.
pypath.curl provides a very flexible download manager
built on top of
pycurl. The classes
pypath.curl.FileOpener accept numerous arguments, try to deal in a smart
way with local cache, authentication, redirects, uncompression, character
encodings, FTP and HTTP transactions, and many other stuff. Cache can grow to
several GBs, and takes place in
~/.pypath/cache by default. If you
experience issues using
pypath these are most often related to failed
downloads which often result nonsense cache contents. To debug such issues
you can see the cache file names and cache usage in the log, and you can use
the context managers in
pypath.curl to show, delete or bypass the cache
for some particular method calls (
You can always set up an alternative cache directory for the entire session
pypath.log modules take care of setting up
session level parameters and logging. Each session has a random 5 character
y5jzx. The default log file in this case is
pypath_log/pypath-y5jzx.log. The log messages flushed in every 2 seconds
by default. You can always change these things by the
In this module you can get and set the values of various parameters using
pypath.settings.setup() and the
A simple webservice comes with this module: the
server module based on
twisted.web.server opens a custom port and serves plain text tables over
HTTP with REST style querying.