--- title: "OmnipathR: an R client for the OmniPath web service" author: - name: Alberto Valdeolivas email: alvaldeolivas@gmail.com - name: Attila Gabor email: attila.gabor@bioquant.uni-heidelberg.de - name: Denes Turei email: turei.denes@gmail.com - name: Julio Saez-Rodriguez affiliation: Institute for Computational Biomedicine, Heidelberg University package: OmnipathR output: bookdown::html_document2: base_format: rmarkdown::html_vignette toc: true toc_depth: 2 number_sections: false fig_caption: true pkgdown: as_is: true bibliography: Bioc.bib abstract: | This vignette describes how to use the `r BiocStyle::Biocpkg("OmnipathR")` package to retrieve information from the OmniPath database: . In addition, it includes some utility functions to filter analyse and visualize the data. vignette: | %\VignetteIndexEntry{OmnipathR: an R client for the OmniPath web service} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # Introduction `r BiocStyle::Biocpkg("OmnipathR")` is an _R_ package built to provide easy access to the data stored in the OmniPath webservice [@Turei2016]: The webservice 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 `r BiocStyle::Biocpkg("OmnipathR")`. ## Query types `r BiocStyle::Biocpkg("OmnipathR")` can retrieve five different types of data: * **Interactions**: protein-protein interactions organized in different datasets: + **omnipath**: the OmniPath data as defined in the original publication [@Turei2016] and collected from different databases. + **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** [@GarciaAlonso2019]. + **tf-miRNA**: transcription factor-miRNA interactions + **miRNA-target**: miRNA-mRNA interactions. + **lncRNA-mRNA**: lncRNA-mRNA interactions. + **small molecule-protein**: interactions between small molecules (metabolites, intrinsic ligands, drug compounds) and proteins. * **Post-translational modifications (PTMs)**: It provides enzyme-substrate reactions in a very similar way to the aforementioned interactions. Some of the biological databases related to PTMs integrated in OmniPath are Phospho.ELM [@Dinkel2010] and PhosphoSitePlus [@Hornbeck2014}. * **Complexes**: it provides access to a comprehensive database of more than 22000 protein complexes. This data comes from different resources such as: CORUM [@Giurgiu2018] or Hu.map [@Drew2017]. * **Annotations**: it provides a large variety of data regarding different annotations about proteins and complexes. These data come from dozens of databases covering different topics such as: The Topology Data Bank of Transmembrane Proteins (TOPDB) [@Dobson2014] or ExoCarta [@Keerthikumar2016], a database collecting the proteins that were identified in exosomes in multiple organisms. * **Intercell**: it provides information on the roles in inter-cellular signaling. For instance. if a protein is a ligand, a receptor, an extracellular matrix (ECM) component, etc. The data does not come from original sources but combined from several databases by us. The source databases, such as CellPhoneDB [@VentoTormo2018] or Receptome [@BenShlomo2003], are also referred for each reacord. Figure \@ref(fig:fig1) shows an overview of the resources featured in **OmniPath**. For more detailed information about the original data sources integrated in Omnipath, please visit: + + ```{r fig1, fig.cap="Overview of the resources featured in OmniPath. Causal resources (including activity-flow and enzyme-substrate resources) can provide direction (*) or sign and direction (+) of interactions.", echo=FALSE} library(knitr) knitr::include_graphics("man/figures/page1_1.png") ``` ## Mouse and rat Excluding the miRNA interactions, all interactions and PTMs 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. In case 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. # Installation of the `r BiocStyle::Biocpkg("OmnipathR")` package First of all, you need a current version of _R_. `r BiocStyle::Biocpkg("OmnipathR")` is a freely available package deposited on _Bioconductor_ and [GitHub](https://github.com/saezlab/OmnipathR). You can install it by running the following commands on an _R_ console: ```{r installation, eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("OmnipathR") ``` We also load here the required packages to run the code in this vignette. ```{r libraries, message=FALSE} library(OmnipathR) library(igraph) library(tidyr) library(gprofiler2) ``` # Usage Examples In the following paragraphs, we provide some examples to describe how to use the `r BiocStyle::Biocpkg("OmnipathR")` package to retrieve different types of information from **Omnipath** webserver. In addition, we play around with the data aiming at obtaining some biological relevant information. Noteworthy, the sections **complexes**, **annotations** and **intercell** are linked. We explore the annotations and roles in inter-cellular communications of the proteins involved in a given complex. This basic example shows the usefulness of integrating the information available in the different **Omnipath** resources. ## Interactions Proteins interact among them and with other biological molecules to perform cellular functions. Proteins also participates in pathways, linked series of reactions occurring inter/intra cells to transform products or to transmit signals inducing specific cellular responses. Protein interactions are therefore a very valuable source of information to understand cellular functioning. We here download the original **OmniPath** human interactions [@Turei2016]. To do so, we first check the different source databases and select some of them. Then, we print some of the downloaded interactions ("+" means activation, "-" means inhibition and "?" means undirected interactions or inconclusive data). ```{r interactions} ## We check some of the different interaction databases interaction_resources() ## The interactions are stored into a data frame. pathways <- omnipath(resources = c("SignaLink3", "PhosphoSite", "SIGNOR")) ## We visualize the first interactions in the data frame. print_interactions(head(pathways)) ``` ### Protein-protein interaction networks Protein-protein interactions are usually converted into networks. Describing protein interactions as networks not only provides a convenient format for visualization, but also allows applying graph theory methods to mine the biological information they contain. We convert here our set of interactions to a network/graph (`r BiocStyle::CRANpkg("igraph")`object). Then, we apply two very common approaches to extract information from a biological network: + **Shortest Paths**: finding a path between two nodes (proteins) going through the minimum number of edges. This can be very useful to track consecutive reactions within a given pathway. We display below the shortest path between two given proteins and all the possible shortests paths between two other proteins. It is to note that the functions `print_path\_es` and `print_path\_vs` display very similar results, but the first one takes as an input an edge sequence and the second one a node sequence. ```{r sp, message=TRUE} ## We transform the interactions data frame into a graph pathways_g <- interaction_graph(pathways) ## Find and print shortest paths on the directed network between proteins ## of interest: print_path_es( igraph::shortest_paths( pathways_g, from = "TYRO3", to = "STAT3", output = "epath" )$epath[[1]], pathways_g ) ## Find and print all shortest paths between proteins of interest: print_path_vs( igraph::all_shortest_paths( pathways_g, from = "DYRK2", to = "MAPKAPK2" )$res, pathways_g ) ``` + **Clustering**: grouping nodes (proteins) in such a way that nodes belonging to the same group (called cluster) are more connected in the network to each other than to those in other groups (clusters). Since proteins interact to perform their functions, proteins within the same cluster are likely to be implicated in similar biological tasks. Figure \@ref(fig:fig2) shows the subgraph containing the proteins and interactions of a specifc protein, ERBB2 The `r BiocStyle::CRANpkg("igraph")` package contains functions to apply sevaral different cluster methods on graphs (visit for detailed information.) ```{r clustering, message=FALSE} ## We apply a clustering algorithm (Louvain) to group proteins in ## our network. We apply here Louvain which is fast but can only run ## on undirected graphs. Other clustering algorithms can deal with ## directed networks but with longer computational times, ## such as cluster_edge_betweenness. These cluster methods are directly ## available in the igraph package. pathways_g_u <- igraph::as.undirected(pathways_g, mode = "mutual") pathways_g_u <- igraph::simplify(pathways_g_u) clusters <- igraph::cluster_fast_greedy(pathways_g_u) ## We extract the cluster where a protein of interest is contained erbb2_cluster_id <- clusters$membership[which(clusters$names == "ERBB2")] erbb2_cluster_g <- igraph::induced_subgraph( pathways_g_u, igraph::V(pathways_g)$name[which(clusters$membership == erbb2_cluster_id)] ) ``` ```{r fig2, echo = FALSE, fig.cap="ERBB2 associated cluser. Subnetwork extracted from the interactions graph representing the cluster where we can find the gene *ERBB2* (yellow node)"} ## We print that cluster with its interactions. par(mar = c(0.1, 0.1, 0.1, 0.1)) plot( erbb2_cluster_g, vertex.label.color = "black", vertex.frame.color = "#ffffff", vertex.size = 15, edge.curved = 0.2, vertex.color = ifelse( igraph::V(erbb2_cluster_g)$name == "ERBB2", "yellow", "#00CCFF" ), edge.color = "blue", edge.width = 0.8 ) ``` ## Other interaction datasets We used above the interactions from the dataset described in the original **OmniPath** publication [@Turei2016]. In this section, we provide examples on how to retry and deal with interactions from the remaining datasets. The same functions can been applied to every interaction dataset. ### Pathway Extra In the first example, we are going to get the interactions from the **pathwayextra** dataset, which contains activity flow interactions without literature reference. We are going to focus on the mouse interactions for a given gene in this particular case. ```{r pathwayextra} ## We query and store the interactions into a dataframe iactions <- pathwayextra( resources = c("Wang", "Lit-BM-17"), organism = 10090 # mouse ) ## We select all the interactions in which Amfr gene is involved iactions_amfr <- dplyr::filter( iactions, source_genesymbol == "Amfr" | target_genesymbol == "Amfr" ) ## We print these interactions: print_interactions(iactions_amfr) ``` ### Kinase Extra Next, we download the interactions from the **kinaseextra** dataset, which contains enzyme-substrate interactions without literature reference. We are going to focus on rat reactions targeting a particular gene. ```{r kinaseextra} ## We query and store the interactions into a dataframe phosphonetw <- kinaseextra( resources = c("PhosphoPoint", "PhosphoSite"), organism = 10116 # rat ) ## We select the interactions in which Dpysl2 gene is a target upstream_dpysl2 <- dplyr::filter( phosphonetw, target_genesymbol == "Dpysl2" ) ## We print these interactions: print_interactions(upstream_dpysl2) ``` ### Ligand-receptor Extra In the following example we are going to work with the **ligrecextra** dataset, which contains ligand-receptor interactions without literature reference. Our goal is to find the potential receptors associated to a given ligand, CDH1 (Figure \@ref(fig:fig3)). ```{r ligrecextra} ## We query and store the interactions into a dataframe ligrec_netw <- ligrecextra( resources = c("iTALK", "Baccin2019"), organism = 9606 # human ) ## Receptors of the CDH1 ligand. downstream_cdh1 <- dplyr::filter( ligrec_netw, source_genesymbol == "CDH1" ) ## We transform the interactions data frame into a graph downstream_cdh1_g <- interaction_graph(downstream_cdh1) ## We induce a network with these genes downstream_cdh1_g <- igraph::induced_subgraph( interaction_graph(omnipath()), igraph::V(downstream_cdh1_g)$name ) ``` ```{r fig3, echo = FALSE, fig.cap="Ligand-receptor interactions for the ADM2 ligand."} ## We print the induced network par(mar = c(0.1, 0.1, 0.1, 0.1)) plot( downstream_cdh1_g, vertex.label.color = "black", vertex.frame.color = "#ffffff", vertex.size = 20, edge.curved = 0.2, vertex.color = ifelse( igraph::V(downstream_cdh1_g)$name == "CDH1", "yellow", "#00CCFF" ), edge.color = "blue", edge.width = 0.8 ) ``` ### DoRothEA Regulons Another very interesting interaction dataset also available in OmniPath is **DoRothEA** [@GarciaAlonso2019]. It contains transcription factor (TF)-target interactions with confidence score, ranging from A-E, being A the most confident interactions. In the code chunk shown below, we select and print the most confident interactions for a given TF. ```{r dorothea} ## We query and store the interactions into a dataframe dorothea_netw <- dorothea( dorothea_levels = "A", organism = 9606 ) ## We select the most confident interactions for a given TF and we print ## the interactions to check the way it regulates its different targets downstream_gli1 <- dplyr::filter( dorothea_netw, source_genesymbol == "GLI1" ) print_interactions(downstream_gli1) ``` ### miRNA-target dataset The last dataset describing interactions is **mirnatarget**. It stores miRNA-mRNA and TF-miRNA interactions. These interactions are only available for human so far. We next select the miRNA interacting with the TF selected in the previous code chunk, *GLI1*. The main function of miRNAs seems to be related with gene regulation. It is therefore interesting to see how some miRNA can regulate the expression of a TF which in turn regulates the expression of other genes. Figure \@ref(fig:fig4) shows a schematic network of the miRNA targeting *GLI1* and the genes regulated by this TF. ```{r mirnatarget} ## We query and store the interactions into a dataframe mirna_targets <- mirna_target( resources = c("miR2Disease", "miRDeathDB") ) ## We select the interactions where a miRNA is interacting with the TF ## used in the previous code chunk and we print these interactions. upstream_gli1 <- dplyr::filter( mirna_targets, target_genesymbol == "GLI1" ) print_interactions(upstream_gli1) ## We transform the previous selections to graphs (igraph objects) downstream_gli1_g <- interaction_graph(downstream_gli1) upstream_gli1_g <- interaction_graph(upstream_gli1) ``` ```{r fig4, echo = FALSE, fig.cap="miRNA-TF-target network. Schematic network of the miRNA (red square nodes) targeting \textit{GLI1} (yellow node) and the genes regulated by this TF (blue round nodes)."} ## We print the union of both previous graphs par(mar = c(0.1, 0.1, 0.1, 0.1)) gli1_g <- upstream_gli1_g %u% downstream_gli1_g plot( gli1_g, vertex.label.color = "black", vertex.frame.color = "#ffffff", vertex.size = 20, edge.curved = 0.25, vertex.color = ifelse( grepl("miR", igraph::V(gli1_g)$name), "red", ifelse( igraph::V(gli1_g)$name == "GLI1", "yellow", "#00CCFF" ) ), edge.color = "blue", vertex.shape = ifelse( grepl("miR",igraph::V(gli1_g)$name), "vrectangle", "circle" ), edge.width = 0.8 ) ``` ### Small molecule-protein dataset This new dataset has been first added to OmniPath in January 2022. It is still quite small: 3.5k interactions from three resources (SIGNOR, CancerDrugsDB and Cellinker), but has prospects of a great growth in the future. As an example, lets look for targets of a cancer drug, the MEK inhibitor Trametinib: ```{r small-molecules} trametinib_targets <- small_molecule(sources = "TRAMETINIB") print_interactions(trametinib_targets) ``` Note, the human readable compound names are not reliable, use PubChem CIDs instead. ## Post-translational modifications (PTMs) Another query type available is PTMs which provides enzyme-substrate reactions in a very similar way to the aforementioned interactions. PTMs refer generally to enzymatic modification of proteins after their synthesis in the ribosomes. PTMs can be highly context-specific and they play a main role in the activation/inhibition of biological pathways. In the next code chunk, we download the **PTMs** for human. We first check the different available source databases, even though we do not perform any filter. Then, we select and print the reactions involving a specific enzyme-substrate pair. Those reactions lack information about activation or inhibition. To obtain that information, we match the data with **OmniPath** interactions. Finally, we show that it is also possible to build a graph using this information, and to retrieve PTMs from mouse or rat. ```{r PTMs} ## We check the different PTMs databases enzsub_resources() ## We query and store the enzyme-PTM interactions into a dataframe. ## No filtering by databases in this case. enzsub <- enzyme_substrate() ## We can select and print the reactions between a specific kinase and ## a specific substrate print_interactions(dplyr::filter( enzsub, enzyme_genesymbol == "MAP2K1", substrate_genesymbol == "MAPK3" )) ## In the previous results, we can see that enzyme-PTM relationships do not ## contain sign (activation/inhibition). We can generate this information ## based on the protein-protein OmniPath interaction dataset. interactions <- omnipath() enzsub <- signed_ptms(enzsub, interactions) ## We select again the same kinase and substrate. Now we have information ## about inhibition or activation when we print the enzyme-PTM relationships print_interactions( dplyr::filter( enzsub, enzyme_genesymbol=="MAP2K1", substrate_genesymbol=="MAPK3" ) ) ## We can also transform the enzyme-PTM relationships into a graph. enzsub_g <- enzsub_graph(enzsub = enzsub) ## We download PTMs for mouse enzsub <- enzyme_substrate( resources = c("PhosphoSite", "SIGNOR"), organism = 10090 ) ``` ## Complexes Some studies indicate that around 80% of the human proteins operate in complexes, and many proteins belong to several different complexes [@Berggrd2007]. These complexes play critical roles in a large variety of biological processes. Some well-known examples are the proteasome and the ribosome. Thus, the description of the full set of protein complexes functioning in cells is essential to improve our understanding of biological processes. The **complexes** query provides access to more than 20000 protein complexes. This comprehensive database has been created by integrating different resources. We now download these molecular complexes filtering by some of the source databases. We check the complexes where a couple of specific genes participate. First, we look for the complexes where any of these two genes participate. We then identify the complex where these two genes are jointly involved. Finally, we perform an enrichment analysis with the genes taking part in that complex. You should keep an eye on this complex since it will be used again in the forthcoming sections. ```{r complexes} ## We check the different complexes databases complex_resources() ## We query and store complexes from some sources into a dataframe. the_complexes <- complexes(resources = c("CORUM", "hu.MAP")) ## We check all the molecular complexes where a set of genes participate query_genes <- c("WRN", "PARP1") ## Complexes where any of the input genes participate wrn_parp1_complexes <- unique( complex_genes( the_complexes, genes = query_genes, all_genes = FALSE ) ) ## We print the components of the different selected components head(wrn_parp1_complexes$components_genesymbols, 6) ## Complexes where all the input genes participate jointly complexes_query_genes_join <- unique( complex_genes( the_complexes, genes = query_genes, all_genes = TRUE ) ) ## We print the components of the different selected components complexes_query_genes_join$components_genesymbols ``` ```{r enrichment} wrn_parp1_cplx_genes <- unlist( strsplit(wrn_parp1_complexes$components_genesymbols, "_") ) ## We can perform an enrichment analyses with the genes in the complex enrichment <- gprofiler2::gost( wrn_parp1_cplx_genes, significant = TRUE, user_threshold = 0.001, correction_method = "fdr", sources = c("GO:BP", "GO:CC", "GO:MF") ) ## We show the most significant results enrichment$result %>% dplyr::select(term_id, source, term_name, p_value) %>% dplyr::top_n(5, -p_value) ``` ## Annotations Biological annotations are statements, usually traceable and curated, about the different features of a biological entity. At the genetic level, annotations describe the biological function, the subcellular situation, the DNA location and many other related properties of a particular gene or its gene products. The annotations query provides a large variety of data about proteins and complexes. 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 (see examples below). Now, we focus in the annotations of the complex studied in the previous section. We first inspect the different available databases in the omnipath webserver. Then, we download the annotations for our complex itself as a biological entity. We find annotations related to the nucleus and transcriptional control, which is in agreement with the enrichment analysis results of its individual components. ```{r complex_annotations} ## We check the different annotation databases annotation_resources() ## We can further investigate the features of the complex selected ## in the previous section. ## We first get the annotations of the complex itself: cplx_annot <- annotations( proteins = paste0("COMPLEX:", wrn_parp1_complexes$components_genesymbols) ) head(dplyr::select(cplx_annot, source, label, value), 10) ``` Afterwards, we explore the annotations of the individual components of the complex in some databases. We check the pathways where these proteins are involved. Once again, we also find many nucleus related annotations when checking their cellular location. Then, we explore some annotations of its individual components. Pathways where the proteins belong: ```{r annotations_components} comp_annot <- annotations( proteins = wrn_parp1_cplx_genes, resources = "NetPath" ) dplyr::select(comp_annot, genesymbol, value) ``` Subcellular localization of our proteins: ```{r subcell_loc} subcell_loc <- annotations( proteins = wrn_parp1_cplx_genes, resources = "ComPPI" ) ``` Since we have same record_id for some results of our query, we spread these records across columns: ```{r annot_spread} tidyr::spread(subcell_loc, label, value) %>% dplyr::arrange(desc(score)) %>% dplyr::top_n(10, score) ``` The way above, we more or less reconstituted the data as it is in the original resource. The same can be done much easier by passing the `wide = TRUE` parameter to `annotations`. In this case, if the data contains more than one resources, a list of data frames will be returned. ```{r annot_wide} signalink_pathways <- annotations( resources = "SignaLink_pathway", wide = TRUE ) ``` ## Intercell Cells perceive cues from their microenvironment and neighboring cells, and respond accordingly to ensure proper activities and coordination between them. The ensemble of these communication process is called inter-cellular signaling (**intercell**). **Intercell** query provides information about the roles of proteins 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 to annotations. However, **intercell** data does not come from original sources, but combined from several databases by us into categories (we also refer to the original sources). We first inspect the different categories available in the OmniPath webserver. Then, we focus again in our previously selected complex and we check its the location of its individual components in the inter-cellular context. We can however see that the components of this particular complex are intracellular. ```{r intercell} ## We check some of the different intercell categories intercell_generic_categories() ## We import the intercell data into a dataframe intercell_loc <- intercell( scope = "generic", aspect = "locational" ) ## We check the intercell annotations for the individual components of ## our previous complex. We filter our data to print it in a good format dplyr::filter(intercell_loc, genesymbol %in% wrn_parp1_cplx_genes) %>% dplyr::distinct(genesymbol, parent, .keep_all = TRUE) %>% dplyr::select(category, genesymbol, parent) %>% dplyr::arrange(genesymbol) ``` The `intercell_network` function creates the most complete network, including many interactions which are false positives in the context of interacellular communication. It is highly recommended to apply some quality filtering on this network. The `high_confidence` parameter performs a quiet stringent filtering: ```{r intercell_quality} icn <- intercell_network(high_confidence = TRUE) ``` Using the function `filter_intercell_network` instead, you have much more flexibility to adjust the stringency of the filtering to the needs of your analysis. See the full list of options in the docs of the function. ```{r intercell_filter} icn <- intercell_network() %>% filter_intercell_network( min_curation_effort = 1, consensus_percentile = 33 ) ``` ```{r close_dev} ## We close graphical connections while (!is.null(dev.list())) dev.off() ``` # Conclusion `r BiocStyle::Biocpkg("OmnipathR")` provides access to the wealth of data stored in the OmniPath webservice from the _R_ enviroment. In addition, it contains some utility functions for visualization, filtering and analysis. The main strength of `r BiocStyle::Biocpkg("OmnipathR")` is the straightforward transformation of the different OmniPath data into commonly used _R_ objects, such as dataframes and graphs. Consequently, it allows an easy integration of the different types of data and a gateway to the vast number of _R_ packages dedicated to the analysis and representaiton of biological data. We highlighted these abilities in some of the examples detailed in previous sections of this document. # Session info {.unnumbered} ```{r sessionInfo, echo=FALSE} sessionInfo() ``` # References