Resource specific interaction attributes
Denes Turei
Institute for Computational Biomedicine, Heidelberg Universityturei.denes@gmail.com Source:
vignettes/extra_attrs.Rmd
extra_attrs.RmdAbstract
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.
Loading a network
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: 80,237 × 3
## source_genesymbol target_genesymbol extra_attrs
## <chr> <chr> <list>
## 1 CALM3 TRPC1 <named list [1]>
## 2 CALM1 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]>
## # … with 80,227 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 extra attributes are available?
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"
## [3] "PhosphoSite_noref_evidence" "PhosphoPoint_category"
## [5] "PhosphoSite_evidence" "HPRD-phos_mechanism"
## [7] "Li2012_mechanism" "Li2012_route"
## [9] "SPIKE_effect" "SPIKE_mechanism"
## [11] "CA1_effect" "CA1_type"
## [13] "Macrophage_type" "Macrophage_location"
## [15] "ACSN_effect" "Cellinker_type"
## [17] "CellChatDB_category" "talklr_putative"
## [19] "CellPhoneDB_type" "Ramilowski2015_source"
## [21] "ARN_effect" "ARN_is_direct"
## [23] "ARN_is_directed" "NRF2ome_effect"
## [25] "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.
Inspecting one attribute
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" "cleavage"
## [9] "Proteolytic Processing" "deubiquitination"
## [11] "Deubiqitination" "relocalization"
## [13] "Ubiquitination" "Dephosphorylation"
## [15] "Other" "guanine nucleotide exchange factor"
## [17] "Transcription Regulation" "gtpase-activating protein"
## [19] "Indirect" ""
## [21] "Sumoylation" "sumoylation"
## [23] "palmitoylation" "demethylation"
## [25] "Demethylation" "mRNA stability"
## [27] "methylation" "Methylation"
## [29] "hydroxylation" "Acetylation"
## [31] "acetylation" "deacetylation"
## [33] "Deacetylation" "Translational Regulation"
## [35] "Protein Degradation" "s-nitrosylation"
## [37] "phosphomotif_binding" "chemical activation"
## [39] "Proteolytic Cleavage" "glycosylation"
## [41] "post transcriptional regulation" "catalytic activity"
## [43] "neddylation" "Neddylation"
## [45] "tyrosination" "lipidation"
## [47] "ADP-ribosylation" "desumoylation"
## [49] "isomerization" "post translational modification"
## [51] "carboxylation" "Alkylation"
## [53] "chemical inhibition" "oxidation"
## [55] "translation regulation" "Carboxylation"
## [57] "destabilization"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.
Converting extra attributes to columns
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: 11,638 × 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 [2]> <chr [2]>
## 8 MAPKAPK2 HNRNPA0 <chr [2]> <NULL>
## 9 MAPKAPK2 PARN <chr [2]> <NULL>
## 10 JAK2 EPOR <chr [2]> <NULL>
## # … with 11,628 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] "phosphorylation" "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: 13,409 × 4
## source_genesymbol target_genesymbol si_mechanism ma_mechanism
## <chr> <chr> <chr> <chr>
## 1 PRKG1 TRPC3 phosphorylation NA
## 2 PRKG1 TRPC7 phosphorylation NA
## 3 OS9 TRPV4 binding NA
## 4 PTPN1 TRPV6 dephosphorylation NA
## 5 RACK1 TRPM6 binding NA
## 6 PRKACA MCOLN1 phosphorylation NA
## 7 MAPK14 MAPKAPK2 phosphorylation Phosphorylation
## 8 MAPK14 MAPKAPK2 phosphorylation Phosphorylation
## 9 MAPK14 MAPKAPK2 Phosphorylation Phosphorylation
## 10 MAPK14 MAPKAPK2 Phosphorylation Phosphorylation
## # … with 13,399 more rowsFiltering records based on extra attributes
Another 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] 11340This 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] 12247It 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: 4,255 × 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 AKT1 CHUK
## # … with 4,245 more rowsExample: finding ubiquitination interactions
First 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"
## [4] "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" "deubiquitination" "Ubiquitination"
##
## $`HPRD-phos_mechanism`
## [1] "Ubiquitination"
##
## $SPIKE_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: 405 × 2
## source_genesymbol target_genesymbol
## <chr> <chr>
## 1 NUMB NOTCH1
## 2 PRKN RANBP2
## 3 PRKN SNCA
## 4 FBXW7 MYC
## 5 UBE2T FANCL
## 6 BIRC2 TRAF2
## 7 TRAF2 MAP3K14
## 8 TRAF6 MAP3K7
## 9 XIAP DIABLO
## 10 TRAF2 RIPK1
## # … with 395 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:
## [1] 150UniProt 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] 2503## [1] 83## [1] 67We 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: 52 × 12
## enzyme substrate enzyme_genesymbol substrate_genesymbol residue_type
## <chr> <chr> <chr> <chr> <chr>
## 1 Q8IUD6 O95786 RNF135 DDX58 K
## 2 Q8IUD6 O95786 RNF135 DDX58 K
## 3 Q8IYW5 P16104 RNF168 H2AX K
## 4 Q8NG06 O95786 TRIM58 DDX58 K
## 5 Q969H0 P23769 FBXW7 GATA2 T
## 6 Q969V5 P31749 MUL1 AKT1 K
## 7 Q96J02 Q7Z434 ITCH MAVS K
## 8 Q96J02 Q7Z434 ITCH MAVS K
## 9 Q96PU5 P35240 NEDD4L NF2 K
## 10 Q9C0C9 O43541 UBE2O SMAD6 K
## # … with 42 more rows, and 7 more variables: residue_offset <dbl>,
## # modification <chr>, sources <chr>, references <chr>, curation_effort <dbl>,
## # n_references <chr>, 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] 50Session information
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Arch Linux
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