R client for the OmniPath web service and many other resources.
OmnipathR retrieves the data from the OmniPath web service at
The web service implements a very simple REST style API. This package make requests by the HTTP protocol to retreive the data. Hence, fast Internet access is required for a proper use of OmnipathR.
OmniPath is a database of:
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.
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/. Sroll down for quick start examples.
We provide here a brief summary about the data available through OmnipathR. OmnipathR provides access to 5 types of queries:
For a more detailed information, we recommend you to visit the following sites:
First of all, you need a current version of
You can install it by running the following commands on a
if (!requireNamespace('BiocManager', quietly = TRUE)) install.packages('BiocManager') ## Last release in Bioconductor BiocManager::install('OmnipathR', version = '3.12') ## Development version with the lastest updates BiocManager::install('OmnipathR', version = 'devel')
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:
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:
You can also check the manual:
In addition, we provide here some examples for a quick start:
Download human protein-protein interactions from the specified resources:
interactions <- import_omnipath_interactions( resources = c('SignaLink3', 'PhosphoSite', 'SIGNOR') )
Download human enzyme-PTM relationships from the specified resources:
enzsub <- import_omnipath_enzsub(resources = c('PhosphoSite', 'SIGNOR'))
Convert both data frames into networks (
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 shortest paths on the directed network between proteins:
print_path_es(shortest_paths(OPI_g,from = 'TYRO3',to = 'STAT3', output = 'epath')$epath[],OPI_g) source interaction target n_resources n_references 1 TYRO3 (Q06418) ==( ? )==> AKT1 (P31749) 2 0 2 AKT1 (P31749) ==( - )==> DAB2IP (Q5VWQ8) 3 1 3 DAB2IP (Q5VWQ8) ==( - )==> STAT3 (P40763) 1 1
Find all shortest paths between proteins:
print_path_vs(all_shortest_paths(OPI_g,from = 'DYRK2',to = 'MAPKAPK2')$res,OPI_g) Pathway 1: DYRK2 -> TBK1 -> NFKB1 -> MAP3K8 -> MAPK3 -> MAPKAPK2 Pathway 2: DYRK2 -> TBK1 -> AKT3 -> MAP3K8 -> MAPK3 -> MAPKAPK2 Pathway 3: DYRK2 -> TBK1 -> AKT2 -> MAP3K8 -> MAPK3 -> MAPKAPK2 Pathway 4: DYRK2 -> TBK1 -> AKT1 -> MAP3K8 -> MAPK3 -> MAPKAPK2 Pathway 5: DYRK2 -> TBK1 -> AKT3 -> PEA15 -> MAPK3 -> MAPKAPK2 Pathway 6: DYRK2 -> TBK1 -> AKT2 -> PEA15 -> MAPK3 -> MAPKAPK2 .....
A similar web service client is available for Python:
The OmniPath Cytoscape app provides access to the interactions, enzyme-PTM relationships and some of the annotations:
The pypath Python module is a tool for building the OmniPath databases in a fully customizable way. We recommend to use pypath if you want to:
pypath.inputsto download data from resources
pypath.utils, e.g. for identifier translation, homology translation, querying Gene Ontology, working with protein sequences, processing BioPAX, etc.
With pypath it’s also possible to run your own web service and serve your custom databases to the OmnipathR R client and the omnipath Python cient.
Feedbacks and bugreports are always very welcome!
Please use the Github issue page to report bugs or for questions:
Many thanks for using OmnipathR!