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 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 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.

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:

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

Get the interactions from Signor, SPIKE and 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:

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.

Enzyme-substrate interactions§

Another query type available is ptms which provides enzyme-substrate interactions. It is very similar to the interactions:

Is there any ubiquitination reaction?

And acetylation in mouse?

Rat interactions, both directly from rat and homology translated from human, from the PhosphoSite database:

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):


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 https://omnipathdb.org/queries/intercell

Some example queries:

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: