OmniPath provides a broad variety of protein annotations, but for
interactions, until recently, only a standard set of essential attributes
(direction, effect, etc) and a handful of others (e.g. DoRothEA confidence
level) were available. The newly introduced extra_attrs column consists of
JSON encoded custom, resource specific attributes from network databases.
We also revised the processing of these resources to ensure that we include
as many useful attributes as possible. In the OmnipathR package we added a
few new functions to support the processing of the JSON encoded column:
to scan it for keys and values, and to extract specific variables of
interest into new columns. We give a brief overview of these here.
OmnipathR 3.12.4
1 Institute for Computational Biomedicine, Heidelberg University
library(OmnipathR)First we retrieve the complete directed PPI network. Importantly, the extra
attributes are only included if the fields = "extra_attrs" argument is
provided.
i <- import_post_translational_interactions(fields = 'extra_attrs')
dplyr::select(i, source_genesymbol, target_genesymbol, extra_attrs)## # A tibble: 134,282 × 3
## source_genesymbol target_genesymbol extra_attrs
## <chr> <chr> <list>
## 1 CALM1 TRPC1 <named list [1]>
## 2 CALM3 TRPC1 <named list [1]>
## 3 CALM2 TRPC1 <named list [1]>
## 4 CAV1 TRPC1 <named list [1]>
## 5 DRD2 TRPC1 <named list [1]>
## 6 MDFI TRPC1 <named list [1]>
## 7 ITPR2 TRPC1 <named list [1]>
## 8 MARCKS TRPC1 <named list [1]>
## 9 TRPC1 GRM1 <named list [0]>
## 10 GRM1 TRPC1 <named list [1]>
## # ℹ 134,272 more rowsAbove we see, the extra_attrs column is a list type column. Each list
is a nested list itself, containing the extra attributes from all resources,
as it was extracted from the JSON.
Which attributes present in the network depends only on the interactions: if
none of the interactions is from the SPIKE database, obviously the
SPIKE_mechanism won’t be present. The names of the extra attributes consist
of the name of the resource and the name of the attribute, separated by an
underscore. The resource name never contains underscore, while some attribute
names do. To list the extra attributes available in a particular data frame
use the extra_attrs function:
extra_attrs(i)## [1] "TRIP_method" "SIGNOR_mechanism" "PhosphoSite_noref_evidence"
## [4] "PhosphoPoint_category" "PhosphoSite_evidence" "HPRD-phos_mechanism"
## [7] "Li2012_mechanism" "Li2012_route" "SPIKE_effect"
## [10] "SPIKE_mechanism" "SPIKE_LC_effect" "SPIKE_LC_mechanism"
## [13] "CA1_effect" "CA1_type" "Macrophage_type"
## [16] "Macrophage_location" "ACSN_effect" "Cellinker_type"
## [19] "CellChatDB_category" "talklr_putative" "CellPhoneDB_type"
## [22] "Ramilowski2015_source" "HPMR_partner_role" "ARN_effect"
## [25] "ARN_is_direct" "ARN_is_directed" "NRF2ome_effect"
## [28] "NRF2ome_is_direct" "NRF2ome_is_directed"The labels listed here are the top level keys in the lists in the
extra_attrs column. Note, the coverage of these variables varies a lot,
typically in agreement with the size of the resource.
The values of each extra attribute, in theory, can be arbitrarily complex
nested lists, but in reality, these are most often simple numeric, logical
or character values or vectors. To see the unique values of one attribute
use the extra_attr_values function. Let’s see the values of the
SIGNOR_mechanism attribute:
extra_attr_values(i, SIGNOR_mechanism)## [1] "phosphorylation" "binding"
## [3] "dephosphorylation" "Phosphorylation"
## [5] "ubiquitination" "N/A"
## [7] "Physical Interaction" "Proteolytic Processing"
## [9] "cleavage" "Ubiquitination"
## [11] "Deubiqitination" "deubiquitination"
## [13] "relocalization" "Dephosphorylation"
## [15] "Other" "guanine nucleotide exchange factor"
## [17] "Transcription Regulation" "gtpase-activating protein"
## [19] "Indirect" ""
## [21] "Sumoylation" "sumoylation"
## [23] "palmitoylation" "Acetylation"
## [25] "acetylation" "polyubiquitination"
## [27] "Demethylation" "demethylation"
## [29] "mRNA stability" "methylation"
## [31] "Methylation" "trimethylation"
## [33] "hydroxylation" "monoubiquitination"
## [35] "Deacetylation" "deacetylation"
## [37] "Translational Regulation" "Protein Degradation"
## [39] "Glycosylation" "s-nitrosylation"
## [41] "phosphomotif_binding" "chemical activation"
## [43] "Proteolytic Cleavage" "tyrosination"
## [45] "post transcriptional regulation" "post translational modification"
## [47] "translation regulation" "carboxylation"
## [49] "neddylation" "Carboxylation"
## [51] "desumoylation" "glycosylation"
## [53] "ADP-ribosylation" "stabilization"
## [55] "catalytic activity" "deglycosylation"
## [57] "destabilization" "chemical inhibition"
## [59] "isomerization" "Neddylation"
## [61] "lipidation" "chemical modification"
## [63] "oxidation" "Alkylation"The values are provided as they are in the original resource, including potential typos and inconsistencies, e.g. see above the capitalized vs. lowercase forms of each value.
To make use of the attributes, it is convenient to extract the interesting
ones into separate columns of the data frame. With the extra_attrs_to_cols
function multiple attributes can be converted in a single call. Custom column
names can be passed by argument names. As an example, let’s extract two
attributes:
i0 <- extra_attrs_to_cols(
i,
si_mechanism = SIGNOR_mechanism,
ma_mechanism = Macrophage_type,
keep_empty = FALSE
)
dplyr::select(
i0,
source_genesymbol,
target_genesymbol,
si_mechanism,
ma_mechanism
)## # A tibble: 61,406 × 4
## source_genesymbol target_genesymbol si_mechanism ma_mechanism
## <chr> <chr> <list> <list>
## 1 PRKG1 TRPC3 <list [1]> <NULL>
## 2 PRKG1 TRPC7 <list [1]> <NULL>
## 3 OS9 TRPV4 <list [1]> <NULL>
## 4 PTPN1 TRPV6 <list [1]> <NULL>
## 5 RACK1 TRPM6 <list [1]> <NULL>
## 6 PRKACA MCOLN1 <list [1]> <NULL>
## 7 MAPK14 MAPKAPK2 <list [1]> <list [1]>
## 8 MAPKAPK2 HNRNPA0 <list [2]> <NULL>
## 9 MAPKAPK2 PARN <list [2]> <NULL>
## 10 JAK2 EPOR <list [2]> <NULL>
## # ℹ 61,396 more rowsAbove we disabled the keep_empty option, otherwise the new columns would
have NULL values for most of the records, simply because out of the 80k
interactions in the data frame only a few thousands are from either SIGNOR
or Macrophage. The new columns are list type, individual values are character
vectors. Let’s look into one value:
dplyr::pull(i0, si_mechanism)[[7]]## [[1]]
## [1] "phosphorylation"Here we have two values, but only because the inconsistent names in the resource.
Depending on downstream methods, atomic columns might be preferable instead
of lists. In this case one interaction record might yield multiple rows in
the resulted data frame, depending on the number of attributes it has. To
have atomic columns, use the flatten option:
i1 <- extra_attrs_to_cols(
i,
si_mechanism = SIGNOR_mechanism,
ma_mechanism = Macrophage_type,
keep_empty = FALSE,
flatten = TRUE
)
dplyr::select(
i1,
source_genesymbol,
target_genesymbol,
si_mechanism,
ma_mechanism
)## # A tibble: 63,434 × 4
## source_genesymbol target_genesymbol si_mechanism ma_mechanism
## <chr> <chr> <list> <list>
## 1 PRKG1 TRPC3 <chr [1]> <NULL>
## 2 PRKG1 TRPC7 <chr [1]> <NULL>
## 3 OS9 TRPV4 <chr [1]> <NULL>
## 4 PTPN1 TRPV6 <chr [1]> <NULL>
## 5 RACK1 TRPM6 <chr [1]> <NULL>
## 6 PRKACA MCOLN1 <chr [1]> <NULL>
## 7 MAPK14 MAPKAPK2 <chr [1]> <chr [1]>
## 8 MAPKAPK2 HNRNPA0 <chr [1]> <NULL>
## 9 MAPKAPK2 HNRNPA0 <chr [1]> <NULL>
## 10 MAPKAPK2 PARN <chr [1]> <NULL>
## # ℹ 63,424 more rowsAnother useful application of extra attributes is filtering the records of
the interactions data frame. The with_extra_attrs function filters to
records which have certain extra attributes. For example, to have only
interactions with SIGNOR_mechanism given:
nrow(with_extra_attrs(i, SIGNOR_mechanism))## [1] 61111This results around 11 thousands rows. Filtering for multiple attributes the records which have at least one of them will be selected. Adding some more attributes results more interactions:
nrow(with_extra_attrs(i, SIGNOR_mechanism, CA1_effect, Li2012_mechanism))## [1] 62017It is possible to filter the records not only by the names but the values of the extra attributes. Let’s select the interactions which are phosphorylation according to SIGNOR:
phos <- c('phosphorylation', 'Phosphorylation')
si_phos <- filter_extra_attrs(i, SIGNOR_mechanism = phos)
dplyr::select(si_phos, source_genesymbol, target_genesymbol)## # A tibble: 4,353 × 2
## source_genesymbol target_genesymbol
## <chr> <chr>
## 1 PRKG1 TRPC3
## 2 PRKG1 TRPC7
## 3 PRKACA MCOLN1
## 4 MAPK14 MAPKAPK2
## 5 MAPKAPK2 HNRNPA0
## 6 MAPKAPK2 PARN
## 7 JAK2 EPOR
## 8 MAPK14 ZFP36
## 9 MAPKAPK2 ZFP36
## 10 PRKAA1_PRKAA2_PRKAB1_PRKAB2_PRKAG1_PRKAG2_PRKAG3 CRTC2
## # ℹ 4,343 more rowsFirst let’s search for the word “ubiquitination” in the attributes. Below is a slow but simple solution:
keys <- extra_attrs(i)
keys_ubi <- purrr::keep(
keys,
function(k){
any(stringr::str_detect(extra_attr_values(i, !!k), 'biqu'))
}
)
keys_ubi## [1] "SIGNOR_mechanism" "HPRD-phos_mechanism" "SPIKE_mechanism" "SPIKE_LC_mechanism"
## [5] "CA1_type" "Macrophage_type"We found five attributes that have at least one value which matches “biqu”. Next take a look at their values:
ubi <- rlang::set_names(
purrr::map(
keys_ubi,
function(k){
stringr::str_subset(extra_attr_values(i, !!k), 'biqu')
}
),
keys_ubi
)
ubi## $SIGNOR_mechanism
## [1] "ubiquitination" "Ubiquitination" "deubiquitination" "polyubiquitination" "monoubiquitination"
##
## $`HPRD-phos_mechanism`
## [1] "Ubiquitination"
##
## $SPIKE_mechanism
## [1] "Ubiquitination" "Polyubiquitination"
##
## $SPIKE_LC_mechanism
## [1] "Ubiquitination" "Polyubiquitination"
##
## $CA1_type
## [1] "Ubiquitination"
##
## $Macrophage_type
## [1] "Ubiquitination"Actually to match all ubiquitination interactions, it’s enough to filter for “ubiquitination” in its lowercase and capitalized forms (note, we could also include deubiqutination and polyubiquitination):
ubi_kws <- c('ubiquitination', 'Ubiquitination')
i_ubi <-
dplyr::distinct(
dplyr::bind_rows(
purrr::map(
keys_ubi,
function(k){
filter_extra_attrs(i, !!k := ubi_kws, na_ok = FALSE)
}
)
)
)
dplyr::select(i_ubi, source_genesymbol, target_genesymbol)## # A tibble: 49,308 × 2
## source_genesymbol target_genesymbol
## <chr> <chr>
## 1 NUMB NOTCH1
## 2 BTRC_CUL1_SKP1 PER2
## 3 PRKN RANBP2
## 4 PRKN SNCA
## 5 FBXW7 MYC
## 6 UBE2T FANCL
## 7 BIRC2 TRAF2
## 8 TRAF2 MAP3K14
## 9 TRAF6 MAP3K7
## 10 BTRC_CUL1_SKP1 WEE1
## # ℹ 49,298 more rowsWe found 405 ubiquitination interactions. We had to use map, bind_rows
and distinct because otherwise filter_extra_attrs would return the
intersection of the matches, instead of their union.
In this data frame we have 150 unique ubiquitin E3 ligases:
length(unique(i_ubi$source_genesymbol))## [1] 365UniProt annotates E3 ligases by the “Ubl conjugation” keyword. We can check how many of those 150 proteins have this annotation:
uniprot_kws <- import_omnipath_annotations(
resources = 'UniProt_keyword',
entity_type = 'protein',
wide = TRUE
)
e3_ligases <- dplyr::pull(
dplyr::filter(uniprot_kws, keyword == 'Ubl conjugation'),
genesymbol
)
length(e3_ligases)## [1] 2542length(intersect(unique(i_ubi$source_genesymbol), e3_ligases))## [1] 106length(setdiff(unique(i_ubi$source_genesymbol), e3_ligases))## [1] 259We retrieved 2503 E3 ligases from UniProt. 83 of these has substrates in the interaction database, while 67 of the effectors of the interactions are not annotated in UniProt.
In the OmniPath enzyme-substrate database we collect ubiquitination interactions from enzyme-PTM resources. However, these contain only a small number of interactions:
es_ubi <- import_omnipath_enzsub(types = 'ubiquitination')
es_ubi## # A tibble: 70 × 12
## enzyme substrate enzyme_genesymbol substrate_genesymbol residue_type residue_offset modification sources
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Q12933 Q13546 TRAF2 RIPK1 K 377 ubiquitination SIGNOR
## 2 Q8IUD6 O95786 RNF135 RIGI K 907 ubiquitination SIGNOR
## 3 Q8IUD6 O95786 RNF135 RIGI K 909 ubiquitination SIGNOR
## 4 P60604 Q92813 UBE2G2 DIO2 K 237 ubiquitination SIGNOR
## 5 P60604 Q92813 UBE2G2 DIO2 K 244 ubiquitination SIGNOR
## 6 Q13489 Q13546 BIRC3 RIPK1 K 377 ubiquitination SIGNOR
## 7 Q96J02 Q7Z434 ITCH MAVS K 420 ubiquitination SIGNOR
## 8 Q96J02 Q7Z434 ITCH MAVS K 371 ubiquitination SIGNOR
## 9 Q66K89 P04637 E4F1 TP53 K 319 ubiquitination HPRD;SI…
## 10 Q66K89 P04637 E4F1 TP53 K 321 ubiquitination HPRD;SI…
## # ℹ 60 more rows
## # ℹ 4 more variables: references <chr>, curation_effort <dbl>, n_references <int>, n_resources <int>With only two exception, all these have been recovered by using the extra attributes from the network database:
es_i_ubi <-
dplyr::inner_join(
es_ubi,
i_ubi,
by = c(
'enzyme_genesymbol' = 'source_genesymbol',
'substrate_genesymbol' = 'target_genesymbol'
)
)
nrow(dplyr::distinct(dplyr::select(es_i_ubi, enzyme, substrate, residue_offset)))## [1] 57sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_GB
## [4] LC_COLLATE=C LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] magrittr_2.0.3 ggraph_2.2.1 igraph_2.0.3 ggplot2_3.5.1 dplyr_1.1.4 OmnipathR_3.12.4
## [7] BiocStyle_2.32.1
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## [1] gtable_0.3.5 xfun_0.47 bslib_0.8.0 ggrepel_0.9.6 websocket_1.4.2
## [6] processx_3.8.4 tzdb_0.4.0 vctrs_0.6.5 tools_4.4.1 ps_1.8.0
## [11] generics_0.1.3 curl_5.2.3 parallel_4.4.1 tibble_3.2.1 fansi_1.0.6
## [16] highr_0.11 pkgconfig_2.0.3 checkmate_2.3.2 readxl_1.4.3 lifecycle_1.0.4
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## [26] munsell_0.5.1 ggforce_0.4.2 chromote_0.3.1 graphlayouts_1.2.0 htmltools_0.5.8.1
## [31] sass_0.4.9 yaml_2.3.10 later_1.3.2 pillar_1.9.0 crayon_1.5.3
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