In saezlab/OmnipathR: OmniPath web service client and more

Loading a network

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)

Above 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)

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)

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
)

Above 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]]

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
)

Filtering 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))

This 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))

It 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)

Example: 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

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

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)

We 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))

UniProt 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)
length(intersect(unique(i_ubi$source_genesymbol), e3_ligases))
length(setdiff(unique(i_ubi$source_genesymbol), e3_ligases))

We 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

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)))