OmnipathR

An R client for the OmniPath web service and many other resources.

Package contents

• Client for the OmniPath web service
• Functions for post-processing OmniPath data
• Ensembl BioMart, UniProt and KEGG clients
• Access to other databases (18+ resources, see below)

OmniPath web service client

OmnipathR retrieves the data from the OmniPath web service at

https://omnipathdb.org/

The web service implements a very simple REST-style API. This package makes requests by the HTTP protocol to retrieve the data. Hence, fast Internet access is required for a proper use of OmnipathR.

What is OmniPath?

OmniPath is a database of:

• Protein-protein, TF target and miRNA-mRNA interactions
• Enzyme-PTM relationships
• Protein complexes
• Annotations of protein function, structure, localization, expression
• Intercellular communication roles of proteins

To learn more about OmniPath, you can visit its website, or read our recent publication or our first paper from 2016, especially its supplementary material.

Access to further resources

The package provides access to a number of other databases: BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, PrePPI, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011.

Documentation

The latest version of the reference manual is available from https://static.omnipathdb.org/omnipathr_manual.pdf. Tutorials can be found at https://workflows.omnipathdb.org/. Scroll down for quick start examples.

OmniPath query types

We provide here a brief summary of the data available through OmnipathR. OmnipathR provides access to 5 types of queries:

1. Interactions: protein-protein interactions from different datasets.
2. Enzyme-substrate: enzyme-PTM (post-translational modification) relationships.
3. Complexes: comprehensive database of more than 22000 protein complexes.
4. Annotations: large variety of data about protein and complex features.
5. Intercell: information on the roles in inter-cellular signaling.

For more detailed information, we recommend you to visit the following sites:

https://omnipathdb.org/

https://omnipathdb.org/info

https://github.com/saezlab/pypath/blob/master/webservice.rst

https://saezlab.github.io/OmnipathR/articles/omnipath_intro.html

Installation

First of all, you need a current version of R (https://r-project.org).

From Bioconductor

OmnipathR is a freely available package deposited on Bioconductor and Github: (https://bioconductor.org/, https://github.com/saezlab/OmnipathR).

You can install it by running the following commands on a R console:

if (!requireNamespace('BiocManager', quietly = TRUE))
    install.packages('BiocManager')

## Last release in Bioconductor
BiocManager::install('OmnipathR', version = '3.12')
## Development version with the latest updates
BiocManager::install('OmnipathR', version = 'devel')

From github

We add new features to OmnipathR way more often than the Bioconductor release frequency. To make use of the recent developments, you can use devtools to install the package directly from github:

require(devtools)
install_github('saezlab/OmnipathR')

Getting started and some usage examples

To get started, we strongly recommend to read our main vignette in order to deal with the different types of queries and handle the data they return:

https://saezlab.github.io/OmnipathR/articles/omnipath_intro.html

You can also check the manual:

https://saezlab.github.io/OmnipathR/reference/index.html

In addition, we provide here some examples for a quick start:

library(OmnipathR)

Download human protein-protein interactions from the specified resources:

interactions <- omnipath(
    resources = c('SignaLink3', 'PhosphoSite', 'SIGNOR')
)

Download human enzyme-PTM relationships from the specified resources:

enzsub <- enzyme_substrate(resources = c('PhosphoSite', 'SIGNOR'))

Convert both data frames into networks (igraph objects)

ptms_g = ptms_graph(ptms = enzsub)
OPI_g = interaction_graph(interactions = interactions)

Print some interactions in a nice format:

print_interactions(head(interactions))

          source interaction         target n_resources n_references
4    SRC (P12931)  ==( + )==> TRPV1 (Q8NER1)           9            6
2  PRKG1 (Q13976)  ==( - )==> TRPC6 (Q9Y210)           7            5
1  PRKG1 (Q13976)  ==( - )==> TRPC3 (Q13507)           9            2
5    LYN (P07948)  ==( + )==> TRPV4 (Q9HBA0)           9            2
6  PTPN1 (P18031)  ==( - )==> TRPV6 (Q9H1D0)           3            2
3 PRKACA (P17612)  ==( + )==> TRPV1 (Q8NER1)           6            1

Find interactions between a specific kinase and a specific substrate:

print_interactions(dplyr::filter(enzsub,enzyme_genesymbol=='MAP2K1',
  substrate_genesymbol=='MAPK3'))

           enzyme interaction           substrate    modification n_resources
1 MAP2K1 (Q02750)       ====> MAPK3_Y204 (P27361) phosphorylation           8
2 MAP2K1 (Q02750)       ====> MAPK3_T202 (P27361) phosphorylation           8
3 MAP2K1 (Q02750)       ====> MAPK3_Y210 (P27361) phosphorylation           2
4 MAP2K1 (Q02750)       ====> MAPK3_T207 (P27361) phosphorylation           2

Find the shortest paths on the directed network between proteins:

print_path_es(shortest_paths(OPI_g,from = 'TYRO3',to = 'STAT3',
    output = 'epath')$epath[[1]],OPI_g)

           source interaction          target n_resources n_references
1  TYRO3 (Q06418)  ==( ? )==>   AKT1 (P31749)           2            0
2   AKT1 (P31749)  ==( - )==> DAB2IP (Q5VWQ8)           3            1
3 DAB2IP (Q5VWQ8)  ==( - )==>  STAT3 (P40763)           1            1

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