Introduction
OmnipathR is an R package built to provide easy access to
the data stored in the OmniPath webservice (Türei, Korcsmáros, and Saez-Rodriguez 2016):
http://omnipathdb.org/
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 OmnipathR.
Query types
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 (Türei, Korcsmáros, and Saez-Rodriguez 2016) 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 (Garcia-Alonso et al. 2019).
- 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 (Dinkel et al. 2010) and PhosphoSitePlus
[Hornbeck et al. (2014)}.
Complexes: it provides access to a comprehensive database of more than
22000 protein complexes. This data comes from different resources
such as: CORUM (Giurgiu et al. 2018) or Hu.map (Drew et al. 2017).
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) (Dobson et al. 2014) or ExoCarta
(Keerthikumar et al. 2016), 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 (Vento-Tormo et al. 2018) or Receptome
(Ben-Shlomo et al. 2003), are also referred for each reacord.
Figure 1 shows an overview of the resources featured in
OmniPath. For more detailed information about the original data sources
integrated in Omnipath, please visit:
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,
https://github.com/saezlab/pypath/) and use human interaction data.
Usage Examples
In the following paragraphs, we provide some examples to describe how to use
the 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 (Türei, Korcsmáros, and Saez-Rodriguez 2016).
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).
## [1] "ABS" "ACSN" "ACSN_SignaLink3"
## [4] "ARACNe-GTEx_DoRothEA" "ARN" "Adhesome"
## [7] "AlzPathway" "BEL-Large-Corpus_ProtMapper" "Baccin2019"
## [10] "BioGRID" "CA1" "CancerCellMap"
## [13] "CancerDrugsDB" "CellCall" "CellChatDB"
## [16] "CellChatDB-cofactors" "CellPhoneDB" "CellPhoneDB_Cellinker"
## [19] "CellTalkDB" "Cellinker" "DEPOD"
## [22] "DIP" "DLRP_Cellinker" "DLRP_talklr"
## [25] "DOMINO" "DeathDomain" "DoRothEA"
## [28] "DoRothEA-reviews_DoRothEA" "ELM" "EMBRACE"
## [31] "ENCODE-distal" "ENCODE-proximal" "ENCODE_tf-mirna"
## [34] "FANTOM4_DoRothEA" "Fantom5_LRdb" "Guide2Pharma"
## [37] "Guide2Pharma_CellPhoneDB" "Guide2Pharma_Cellinker" "Guide2Pharma_LRdb"
## [40] "Guide2Pharma_talklr" "HOCOMOCO_DoRothEA" "HPMR"
## [43] "HPMR_Cellinker" "HPMR_LRdb" "HPMR_talklr"
## [46] "HPRD" "HPRD-phos" "HPRD_KEA"
## [49] "HPRD_LRdb" "HPRD_MIMP" "HPRD_talklr"
## [52] "HTRIdb" "HTRIdb_DoRothEA" "HuRI"
## [55] "I2D_CellPhoneDB" "ICELLNET" "IMEx_CellPhoneDB"
## [58] "InnateDB" "InnateDB-All_CellPhoneDB" "InnateDB_CellPhoneDB"
## [61] "InnateDB_SignaLink3" "IntAct" "IntAct_CellPhoneDB"
## [64] "IntAct_DoRothEA" "JASPAR_DoRothEA" "KEA"
## [67] "KEGG-MEDICUS" "Kinexus_KEA" "Kirouac2010"
## [70] "LMPID" "LRdb" "Li2012"
## [73] "Lit-BM-17" "LncRNADisease" "MIMP"
## [76] "MINT_CellPhoneDB" "MPPI" "Macrophage"
## [79] "MatrixDB" "MatrixDB_CellPhoneDB" "NCI-PID_ProtMapper"
## [82] "NFIRegulomeDB_DoRothEA" "NRF2ome" "NetPath"
## [85] "NetworKIN_KEA" "ORegAnno" "ORegAnno_DoRothEA"
## [88] "PAZAR" "PAZAR_DoRothEA" "PhosphoNetworks"
## [91] "PhosphoPoint" "PhosphoSite" "PhosphoSite_KEA"
## [94] "PhosphoSite_MIMP" "PhosphoSite_ProtMapper" "PhosphoSite_noref"
## [97] "ProtMapper" "REACH_ProtMapper" "RLIMS-P_ProtMapper"
## [100] "Ramilowski2015" "Ramilowski2015_Baccin2019" "ReMap_DoRothEA"
## [103] "Reactome_LRdb" "Reactome_ProtMapper" "Reactome_SignaLink3"
## [106] "RegNetwork_DoRothEA" "SIGNOR" "SIGNOR_ProtMapper"
## [109] "SPIKE" "STRING_talklr" "SignaLink3"
## [112] "Sparser_ProtMapper" "TCRcuration_SignaLink3" "TFactS_DoRothEA"
## [115] "TFe_DoRothEA" "TRED_DoRothEA" "TRIP"
## [118] "TRRD_DoRothEA" "TRRUST_DoRothEA" "TransmiR"
## [121] "UniProt_CellPhoneDB" "UniProt_LRdb" "Wang"
## [124] "Wojtowicz2020" "connectomeDB2020" "dbPTM"
## [127] "iPTMnet" "iTALK" "lncrnadb"
## [130] "miR2Disease" "miRDeathDB" "miRTarBase"
## [133] "miRecords" "ncRDeathDB" "phosphoELM"
## [136] "phosphoELM_KEA" "phosphoELM_MIMP" "scConnect"
## [139] "talklr"
## # A tibble: 6 × 5
## source interaction target n_resources n_references
## <chr> <chr> <chr> <int> <chr>
## 1 PRKG1 (Q13976) ==( + )==> TRPC7 (Q9HCX4) 3 SIGNOR:21402151;TRIP:21402151;iPTMnet:21402151
## 2 PRKACA (P17612) ==( ? )==> TRPC6 (Q9Y210) 2 PhosphoSite:19961855;PhosphoSite:23645677;TRIP:22001…
## 3 SRC (P12931) ==( + )==> TRPV1 (Q8NER1) 5 PhosphoSite:16319926;ProtMapper:16319926;ProtMapper:…
## 4 OS9 (Q13438) ==(+/-)==> TRPV4 (Q9HBA0) 3 HPRD:17932042;SIGNOR:17932042;TRIP:17932042
## 5 PRKG1 (Q13976) ==( - )==> TRPC3 (Q13507) 8 HPRD:14983059;KEA:14983059;ProtMapper:14983059;ProtM…
## 6 PTPN1 (P18031) ==( - )==> TRPV6 (Q9H1D0) 6 DEPOD:15894168;DEPOD:17197020;HPRD:15894168;IntAct:1…
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
(igraphobject). 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.
## source interaction target n_resources
## 1 TYRO3 (Q06418) ==( + )==> PIK3R1 (P27986) 4
## 2 PIK3R1 (P27986) ==(+/-)==> PIK3CG (P48736) 3
## 3 PIK3CG (P48736) ==( + )==> RAC1 (P63000) 2
## 4 RAC1 (P63000) ==( + )==> STAT3 (P40763) 9
## n_references
## 1 HPRD:10627473;Lit-BM-17:10627473;SignaLink3:10627473;SignaLink3:18988627;SignaLink3:23331499
## 2 NetPath:11733063;NetPath:8596638;SIGNOR:14665640
## 3 SignaLink3:15009099;SignaLink3:23331499
## 4 Adhesome:11021801;CancerCellMap:11021801;HPRD:11021801;Lit-BM-17:11021801;NetPath:11021801;ProtMapper:26430964;SPIKE:11021801;SignaLink3:11021801;SignaLink3:23331499
## Pathway 1: DYRK2 -> TP53 -> RPS6KA1 -> EEF2K -> MAPKAPK2
- 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 2 shows the
subgraph containing the proteins and interactions of a specifc protein, ERBB2
The igraph package contains functions to apply sevaral
different cluster methods on graphs (visit https://igraph.org/r/doc/ for
detailed information.)
Other interaction datasets
We used above the interactions from the dataset described in the original
OmniPath publication (Türei, Korcsmáros, and Saez-Rodriguez 2016). 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.
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 3).
DoRothEA Regulons
Another very interesting interaction dataset also available in OmniPath is
DoRothEA (Garcia-Alonso et al. 2019). 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.
## # A tibble: 9 × 5
## source interaction target n_resources n_references
## <chr> <chr> <chr> <int> <chr>
## 1 GLI1 (P08151) ==( + )==> HHIP (Q96QV1) 1 0
## 2 GLI1 (P08151) ==( + )==> PTCH1 (Q13635) 1 0
## 3 GLI1 (P08151) ==( + )==> PTCH2 (Q9Y6C5) 1 0
## 4 GLI1 (P08151) ==( + )==> BCL2 (P10415) 0 0
## 5 GLI1 (P08151) ==( + )==> CCND2 (P30279) 0 0
## 6 GLI1 (P08151) ==( - )==> EGR2 (P11161) 0 0
## 7 GLI1 (P08151) ==( + )==> IGFBP6 (P24592) 0 0
## 8 GLI1 (P08151) ==( + )==> SFRP1 (Q8N474) 0 0
## 9 GLI1 (P08151) ==( - )==> SLIT2 (O94813) 0 0
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 4 shows a schematic network of
the miRNA targeting GLI1 and the genes regulated by this TF.
## # A tibble: 3 × 5
## source interaction target n_resources n_references
## <chr> <chr> <chr> <int> <chr>
## 1 hsa-miR-324-5p (MIMAT0000761) ==( ? )==> GLI1 (P08151) 3 miRTarBase:18756266;miRTarBase:20216554…
## 2 hsa-miR-125b (MIMAT0000423) ==( ? )==> GLI1 (P08151) 2 miRTarBase:18756266
## 3 hsa-miR-326 (MIMAT0000756) ==( ? )==> GLI1 (P08151) 2 miRTarBase:18756266
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:
## # A tibble: 26 × 5
## source interaction target n_resources n_references
## <chr> <chr> <chr> <int> <chr>
## 1 TRAMETINIB (11707110) ==( - )==> MAP2K1 (Q02750) 2 SIGNOR:26347206
## 2 TRAMETINIB (11707110) ==( - )==> MAP2K2 (P36507) 2 SIGNOR:26347206
## 3 TRAMETINIB (11707110) ==( ? )==> GNAQ (P50148) 1 0
## 4 TRAMETINIB (11707110) ==( ? )==> DDX43 (Q9NXZ2) 1 0
## 5 TRAMETINIB (11707110) ==( ? )==> ETV1 (P50549) 1 0
## 6 TRAMETINIB (11707110) ==( ? )==> STAG2 (Q8N3U4) 1 0
## 7 TRAMETINIB (11707110) ==( ? )==> ARAF (P10398) 1 0
## 8 TRAMETINIB (11707110) ==( ? )==> CDKN2A (P42771) 1 0
## 9 TRAMETINIB (11707110) ==( ? )==> MAP2K5 (Q13163) 1 0
## 10 TRAMETINIB (11707110) ==( ? )==> CDKN2A (Q8N726) 1 0
## # … with 16 more rows
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.
## [1] "BEL-Large-Corpus_ProtMapper" "DEPOD" "HPRD"
## [4] "HPRD_MIMP" "KEA" "Li2012"
## [7] "MIMP" "NCI-PID_ProtMapper" "PhosphoNetworks"
## [10] "PhosphoSite" "PhosphoSite_MIMP" "PhosphoSite_ProtMapper"
## [13] "ProtMapper" "REACH_ProtMapper" "RLIMS-P_ProtMapper"
## [16] "Reactome_ProtMapper" "SIGNOR" "SIGNOR_ProtMapper"
## [19] "Sparser_ProtMapper" "dbPTM" "phosphoELM"
## [22] "phosphoELM_MIMP"
## Warning: Unknown or uninitialised column: `is_stimulation`.
## # A tibble: 6 × 5
## enzyme interaction substrate modification n_resources
## <chr> <chr> <chr> <chr> <int>
## 1 MAP2K1 (Q02750) ====> MAPK3_T202 (P27361) phosphorylation 8
## 2 MAP2K1 (Q02750) ====> MAPK3_Y204 (P27361) phosphorylation 8
## 3 MAP2K1 (Q02750) ====> MAPK3_T207 (P27361) phosphorylation 2
## 4 MAP2K1 (Q02750) ====> MAPK3_Y210 (P27361) phosphorylation 2
## 5 MAP2K1 (Q02750) ====> MAPK3_T80 (P27361) phosphorylation 1
## 6 MAP2K1 (Q02750) ====> MAPK3_Y222 (P27361) phosphorylation 1
## enzyme interaction substrate modification n_resources
## 2 MAP2K1 (Q02750) ==( + )==> MAPK3_T202 (P27361) phosphorylation 8
## 1 MAP2K1 (Q02750) ==( + )==> MAPK3_Y204 (P27361) phosphorylation 8
## 4 MAP2K1 (Q02750) ==( + )==> MAPK3_T207 (P27361) phosphorylation 2
## 5 MAP2K1 (Q02750) ==( + )==> MAPK3_Y210 (P27361) phosphorylation 2
## 3 MAP2K1 (Q02750) ==( + )==> MAPK3_T80 (P27361) phosphorylation 1
## 6 MAP2K1 (Q02750) ==( + )==> MAPK3_Y222 (P27361) phosphorylation 1
Complexes
Some studies indicate that around 80% of the human proteins operate in
complexes, and many proteins belong to several different complexes
(Berggård, Linse, and James 2007). 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.
## [1] "CFinder" "CORUM" "CellChatDB" "CellPhoneDB" "Cellinker" "Compleat"
## [7] "ComplexPortal" "Guide2Pharma" "Havugimana2012" "ICELLNET" "KEGG-MEDICUS" "NetworkBlast"
## [13] "PDB" "SIGNOR" "hu.MAP" "hu.MAP2"
## [1] "NCAPD2_NCAPG_NCAPH_PARP1_SMC2_SMC4_XRCC1"
## [2] "CCNA2_CDK2_LIG1_PARP1_POLA1_POLD1_POLE_RFC1_RFC2_RPA1_RPA2_RPA3_TOP1"
## [3] "CCNA2_CCNB1_CDK1_PARP1_POLA1_POLD1_POLE_RFC1_RFC2_RPA1_RPA2_RPA3_TOP1"
## [4] "MRE11_PARP1_RAD50_TERF2_TERF2IP_XRCC5_XRCC6"
## [5] "TERF2_WRN"
## [6] "CALR_DHX30_H2AX_H2BU1_HSPA5_NPM1_PARP1"
## [1] "PARP1_WRN_XRCC5_XRCC6"
genes_complex <-
unlist(strsplit(complexes_query_genes_join$components_genesymbols, "_"))
## We can perform an enrichment analyses with the genes in the complex
EnrichmentResults <- gost(genes_complex, significant = TRUE,
user_threshold = 0.001, correction_method = c("fdr"),
sources=c("GO:BP","GO:CC","GO:MF"))
## We show the most significant results
EnrichmentResults$result %>%
dplyr::select(term_id, source, term_name,p_value) %>%
dplyr::top_n(5,-p_value)
## term_id source term_name p_value
## 1 GO:0010332 GO:BP response to gamma radiation 2.371053e-08
## 2 GO:0000723 GO:BP telomere maintenance 4.447175e-07
## 3 GO:0010212 GO:BP response to ionizing radiation 4.447175e-07
## 4 GO:0071478 GO:BP cellular response to radiation 7.008413e-07
## 5 GO:0032200 GO:BP telomere organization 7.008413e-07
## 6 GO:0000781 GO:CC chromosome, telomeric region 2.412057e-07
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.
## [1] "Adhesome" "Almen2009" "Baccin2019" "CORUM_Funcat"
## [5] "CORUM_GO" "CSPA" "CSPA_celltype" "CancerDrugsDB"
## [9] "CancerGeneCensus" "CancerSEA" "CellCall" "CellCellInteractions"
## [13] "CellChatDB" "CellChatDB_complex" "CellPhoneDB" "CellPhoneDB_complex"
## [17] "CellTalkDB" "CellTypist" "Cellinker" "Cellinker_complex"
## [21] "ComPPI" "CytoSig" "DGIdb" "DisGeNet"
## [25] "EMBRACE" "Exocarta" "GO_Intercell" "GPCRdb"
## [29] "Guide2Pharma" "HGNC" "HPA_secretome" "HPA_subcellular"
## [33] "HPA_tissue" "HPMR" "HumanCellMap" "ICELLNET"
## [37] "ICELLNET_complex" "IntOGen" "Integrins" "KEGG-PC"
## [41] "Kirouac2010" "LOCATE" "LRdb" "MCAM"
## [45] "MSigDB" "Matrisome" "MatrixDB" "Membranome"
## [49] "NetPath" "OPM" "PROGENy" "PanglaoDB"
## [53] "Phobius" "Phosphatome" "Ramilowski2015" "Ramilowski_location"
## [57] "SIGNOR" "SignaLink_function" "SignaLink_pathway" "Surfaceome"
## [61] "TCDB" "TFcensus" "TopDB" "UniProt_family"
## [65] "UniProt_keyword" "UniProt_location" "UniProt_tissue" "UniProt_topology"
## [69] "Vesiclepedia" "Zhong2015" "connectomeDB2020" "iTALK"
## [73] "kinase.com" "scConnect" "scConnect_complex" "talklr"
## # A tibble: 10 × 3
## source label value
## <chr> <chr> <chr>
## 1 ComPPI location nucleus
## 2 MSigDB collection chemical_and_genetic_perturbations
## 3 MSigDB geneset PUJANA_CHEK2_PCC_NETWORK
## 4 MSigDB collection chemical_and_genetic_perturbations
## 5 MSigDB geneset PUJANA_BRCA1_PCC_NETWORK
## 6 MSigDB collection reactome_pathways
## 7 MSigDB geneset REACTOME_DNA_DOUBLE_STRAND_BREAK_REPAIR
## 8 MSigDB collection reactome_pathways
## 9 MSigDB geneset REACTOME_DNA_REPAIR
## 10 MSigDB collection chemical_and_genetic_perturbations
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:
## # A tibble: 7 × 2
## genesymbol value
## <chr> <chr>
## 1 PARP1 Tumor necrosis factor (TNF) alpha
## 2 PARP1 Androgen receptor (AR)
## 3 PARP1 TNF-related weak inducer of apoptosis (TWEAK)
## 4 PARP1 Corticotropin-releasing hormone (CRH)
## 5 PARP1 Oncostatin-M (OSM)
## 6 XRCC5 Androgen receptor (AR)
## 7 XRCC6 Androgen receptor (AR)
Subcellular localization of our proteins:
Since we have same record_id for some results of our query, we spread these
records across columns:
## # A tibble: 11 × 7
## uniprot genesymbol entity_type source record_id location score
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 P12956 XRCC6 protein ComPPI 2967 nucleus 0.99999997629184
## 2 P09874 PARP1 protein ComPPI 11241 nucleus 0.999999887104
## 3 Q14191 WRN protein ComPPI 16096 nucleus 0.9999996544
## 4 P13010 XRCC5 protein ComPPI 13373 nucleus 0.99999868288
## 5 P13010 XRCC5 protein ComPPI 13371 membrane 0.972
## 6 P12956 XRCC6 protein ComPPI 2965 cytosol 0.958
## 7 P13010 XRCC5 protein ComPPI 13374 cytosol 0.958
## 8 Q14191 WRN protein ComPPI 16097 cytosol 0.94
## 9 P12956 XRCC6 protein ComPPI 2966 extracellular 0.8600000000000001
## 10 P12956 XRCC6 protein ComPPI 2968 membrane 0.8600000000000001
## 11 P13010 XRCC5 protein ComPPI 13372 extracellular 0.8600000000000001
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 import_omnipath_annotations. In this case,
if the data contains more than one resources, a list of data frames
will be returned.
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.
## [1] "transmembrane" "transmembrane_predicted"
## [3] "peripheral" "plasma_membrane"
## [5] "plasma_membrane_transmembrane" "plasma_membrane_regulator"
## [7] "plasma_membrane_peripheral" "secreted"
## [9] "cell_surface" "ecm"
## [11] "ligand" "receptor"
## [13] "secreted_enzyme" "secreted_peptidase"
## [15] "extracellular" "intracellular"
## [17] "receptor_regulator" "secreted_receptor"
## [19] "sparc_ecm_regulator" "ecm_regulator"
## [21] "ligand_regulator" "cell_surface_ligand"
## [23] "cell_adhesion" "matrix_adhesion"
## [25] "adhesion" "matrix_adhesion_regulator"
## [27] "cell_surface_enzyme" "cell_surface_peptidase"
## [29] "secreted_enyzme" "extracellular_peptidase"
## [31] "secreted_peptidase_inhibitor" "transporter"
## [33] "ion_channel" "ion_channel_regulator"
## [35] "gap_junction" "tight_junction"
## [37] "adherens_junction" "desmosome"
## [39] "intracellular_intercellular_related"
## # A tibble: 4 × 3
## category genesymbol parent
## <chr> <chr> <chr>
## 1 intracellular PARP1 intracellular
## 2 intracellular WRN intracellular
## 3 intracellular XRCC5 intracellular
## 4 intracellular XRCC6 intracellular
The import_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:
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.
