The prior knowledge network (PKN) used by COSMOS is a network of heterogenous causal interactions: it contains protein-protein, reactant-enzyme and enzyme-product interactions. It is a combination of multiple resources. Here we present the functions that load each component, the options for customization, and the functions to build the complete PKN.
OmnipathR 3.14.0
1 Institute for Computational Biomedicine, Heidelberg University
The COSMOS PKN is a combination of the following datasets:
Building the PKN is possible in by calling the cosmos_pkn() function that we
present in the last section. First let’s take a closer look at each resource.
library(OmnipathR)The Chalmers Sysbio group provides genome scale models of metabolism (GEMs) for various organisms: human, mouse, rat, fish, fly and worm. These models are available as Matlab files, which contain reaction data, stoichiometry matrix and identifier translation data.
The raw contents of the Matlab file can be loaded by chalmers_gem_raw(). This
results in a convoluted structure of nested lists and arrays:
ch_raw <- chalmers_gem_raw()Another function, chalmers_gem() processes the above structure into a data
frame of reactions, also keeping the stoichiometry matrix and including the
identifier translation data:
ch_processed <- chalmers_gem()
names(ch_processed)## NULLThe identifier translation tables are available by separate functions,
chalmers_gem_metabolites() and chalmers_gem_reactions() for metabolite and
reaction (and enzyme) identifiers, respectively. These return simple data
frames.
ch_met <- chalmers_gem_metabolites()
ch_met## NULLThe metabolite identifier translation available here is also integrated into
the package’s translation service, available by the translate_ids and other
functions.
translate_ids('MAM00001', 'metabolicatlas', 'recon3d', chalmers = TRUE)Finally, the chalmers_gem_network() function uses all above data to compile a
network binary chemical-protein interactions. By default Metabolic Atlas
identifiers are used for the metabolites and Ensembl Gene IDs for the
enzymes. These can be tranlated to the desired identifiers using the
metabolite_ids and protein_ids arguments. Translation to multiple
identifers is possible. The ri or record_id column in case of the Chalmers
GEM represent the reaction ID, a unique identifier of the original reaction.
One reaction yields many binary interactions as it consists of a number of gene
products, reactants and products. The column ci means “complex ID”, it is a
unique identifier of groups of enzymes required together to carry out the
reaction. The column reverse indicates if the row is derived from the
reversed version of a reversible reaction. The column transporter signals
reactions where the same metabolite occures both on reactant and product side,
these are assumed to be transport reactions. In the Chalmers GEM the reactions
are also assigned to compartments, these are encoded by single letter codes in
the comp column. In the original data the compartment codes are postfixes of
the metabolite IDs, here we move them into a separate column, leaving the
Metabolic Atlas IDs clean and usable.
ch <- chalmers_gem_network()
chSTITCH is a large compendium of binary interactions between proteins and
chemicals. Some of these are derived from metabolic reactions. Various
attributes such as mode of action, effect sign and scores are assigned to each
link. The datasets are available by organism, stored in “actions” and “links”
tables, available by the stitch_actions() and stitch_links() functions,
respectively. STITCH supports a broad variety of organisms, please refer to
their website at (https://stitch.embl.de/).
sta <- stitch_actions()
stastl <- stitch_links()
stlstitch_network() combines the actions and links data frames, filters by
confidence scores, removes the prefixes from identifiers and translates them to
the desired ID types. STITCH prefixes Ensembl Protein IDs with the NCBI Taxonomy
ID, while PubChem CIDs with CID plus a lowercase letter “s” or “m”, meaning
stereo specific or merged stereoisomers, respectively. These prefixes are
removed by default by the stitch_remove_prefixes() function. Effect signs (1
= activation, -1 = inhibition) and combined scores are aslo included in the
data frame. Similarly to Chalmers GEM, translation of chemical and protein
identifiers is available. The record_id column uniquely identifies the
original records. Multiple rows with the same record_id are due to
one-to-many identifier translation.
st <- stitch_network()
stAll parameters supported by the OmniPath web service
(omnipath()) can be passed to omnipath_for_cosmos(),
enabling precise control over the resources, interaction types and other
options when preparing the signaling network from OmniPath. By default the
“omnipath” dataset is included which contains high confidence, literature
curated, causal protein-protein interactions. For human, mouse and rat,
orthology translated data is retrieved from the web service, while for other
organisms translation by orthologous gene pairs is carried out client side.
op <- omnipath_for_cosmos()
op## # A tibble: 138,490 × 8
## source target sign record_id uniprot_source uniprot_target genesymbol_source genesymbol_target
## <chr> <chr> <dbl> <int> <chr> <chr> <chr> <chr>
## 1 P0DP23 P48995 -1 1 P0DP23 P48995 CALM1 TRPC1
## 2 P0DP25 P48995 -1 2 P0DP25 P48995 CALM3 TRPC1
## 3 P0DP24 P48995 -1 3 P0DP24 P48995 CALM2 TRPC1
## 4 Q03135 P48995 1 4 Q03135 P48995 CAV1 TRPC1
## 5 P14416 P48995 1 5 P14416 P48995 DRD2 TRPC1
## 6 Q99750 P48995 -1 6 Q99750 P48995 MDFI TRPC1
## 7 Q14571 P48995 1 7 Q14571 P48995 ITPR2 TRPC1
## 8 P29966 P48995 -1 8 P29966 P48995 MARCKS TRPC1
## 9 Q13255 P48995 1 9 Q13255 P48995 GRM1 TRPC1
## 10 Q13586 P48995 1 10 Q13586 P48995 STIM1 TRPC1
## # ℹ 138,480 more rowsBuilding the complete COSMOS PKN is done by cosmos_pkn(). All the resources
above can be customized by arguments passed to this function. With all
downloads and processing the build might take 30-40 minutes. Data
is cached at various levels of processing, shortening processing times. With
all data downloaded and HMDB ID translation data preprocessed, the build
takes 3-4 minutes; the complete PKN is also saved in the cache, if this is
available, loading it takes only a few seconds.
pkn <- cosmos_pkn()
pknThe record_id column identifies the original records within each
resource. If one record_id yields multiple records in the final data
frame, it is the result of one-to-many ID translation or other
processing steps. Before use, it is recommended to select one pair of ID
type columns (by combining the preferred ones) and perform distinct
by the identifier columns and sign. After the common columns, resource specific
columns are labeled with the resource name; after these columns, molecule type
and side specific identifer columns are named after the ID type and the side of
the interaction (“source” vs. “target”).
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