The goal of STRINGDatabaseManipulation is to provide functions for reading in and manipulating STRING protein-protein interaction networks.
Only the development version of STRINGDatabaseManipulation is available.
remotes::install_github("moseleyBioinformaticsLab/STRINGDatabaseManipulation")If you have a question about how to use the package, a request for something new to be implemented, or have a bug, please file an issue.
This package works with preprocessed data from STRING itself.
You can preprocess files from STRING by downloading link detail and alias files. These files are available from the STRING download page. I do not recommend trying to work with the full interaction network across all species, but rather download files for your specific species of interest. For example, you can filter to just the human files.
We can get the files we want at the command line using a tool like
wget:
# get the PPI network itself
wget https://stringdb-static.org/download/protein.links.detailed.v11.5/9606.protein.links.detailed.v11.5.txt.gz
# get aliases
wget https://stringdb-static.org/download/protein.aliases.v11.5/9606.protein.aliases.v11.5.txt.gz
And then process them so they are easier to use:
library(STRINGDatabaseManipulation)
# assuming data is in the file, you would do:
ppi_data = process_string_links("9606.protein.links.detailed.v11.5.txt.gz")
# save it for later
saveRDS(ppi_data, file = "tmp_ppi_data.rda")
# process aliases
protein_aliases = process_string_aliases("9606.protein.aliases.v11.5.txt.gz")
# save for later
saveRDS(protein_aliases, file = "tmp_aliases.rda")This package has smaller versions of the link and data files (10,000 links) saved for examples, both as raw text files, and as package data files. These files have been filtered to those links with an experimental score >= 400, as well as links that include EGFR and TP53.
library(STRINGDatabaseManipulation)
links_file = system.file("extdata", "STRING11.5_9606_links_raw.txt.gz", package = "STRINGDatabaseManipulation")
ppi_data = process_string_links(links_file)
head(ppi_data)
#> protein1 protein2 neighborhood fusion cooccurence coexpression
#> 1 9606.ENSP00000001008 9606.ENSP00000354558 0 0 0 69
#> 2 9606.ENSP00000003084 9606.ENSP00000306330 0 0 0 0
#> 3 9606.ENSP00000003084 9606.ENSP00000449404 0 0 0 0
#> 4 9606.ENSP00000005257 9606.ENSP00000353590 0 0 0 51
#> 5 9606.ENSP00000005340 9606.ENSP00000262320 0 0 0 86
#> 6 9606.ENSP00000005340 9606.ENSP00000335677 0 0 0 0
#> experimental database textmining combined_score
#> 1 835 0 343 890
#> 2 810 0 84 818
#> 3 550 0 875 941
#> 4 653 0 64 664
#> 5 987 900 475 999
#> 6 476 0 87 501
aliases_file = system.file("extdata", "STRING11.5_9606_aliases_raw.txt.gz", package = "STRINGDatabaseManipulation")
ppi_aliases = process_string_aliases(aliases_file)
head(ppi_aliases)
#> string other type
#> 1 9606.ENSP00000001008 2288 Ensembl_HGNC_Entrez_Gene_ID
#> 2 9606.ENSP00000001008 2288 Ensembl_HGNC_Entrez_Gene_ID(supplied_by_NCBI)
#> 3 9606.ENSP00000001008 5.2.1.8 BLAST_UniProt_DE_RecName_EC
#> 4 9606.ENSP00000001008 FKBP4 Ensembl_EntrezGene
#> 5 9606.ENSP00000001008 FKBP51 Ensembl_EntrezGene_synonym
#> 6 9606.ENSP00000001008 FKBP52 Ensembl_EntrezGene_synonymNow lets actually do something with the STRING data. The most common analysis we want to do is find proteins that interact (directly or indirectly) with one or more query proteins.
We will use the example data from the package.
To filter the data, we can use dplyr to choose which evidence or set
of evidences to use a filter. Here we will use combined_score >= 400
(it’s actually already filtered, but this is to show how we can use it).
ppi_filtered = ppi_data |>
dplyr::filter(combined_score >= 400)
dim(ppi_data)
#> [1] 21070 10
dim(ppi_filtered)
#> [1] 21070 10In this case it doesn’t change the number of interactions, because the data was pre-filtered to make it tractable for inclusion in the package.
Lets take everyone’s favorite cancer gene, TP53, and look for those proteins that experimentally are known to interact with it. We will find the STRING-db ID, and then query everything that is connected to it.
ppi_graph = string_2_tidygraph(ppi_data)
tp53_alias = ppi_aliases |>
dplyr::filter(other %in% "TP53")
tp53_alias
#> string other type
#> 1 9606.ENSP00000269305 TP53 Ensembl_EntrezGeneNow we can go fetch the neighbors of our query protein (n_hops = 0), and
find everything that it interacts with. Notice, n_hops = 0! This is
because the hops refers to hops over other proteins. To find just the
interacting pairs, we are doing 0 hops.
tp53_interactions = find_nodes_n_hops(ppi_graph, n_hops = 0, start_nodes = tp53_alias$string)
tp53_interactions$graph
#> # A tbl_graph: 322 nodes and 479 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 322 × 1 (active)
#> name
#> <chr>
#> 1 9606.ENSP00000005340
#> 2 9606.ENSP00000011619
#> 3 9606.ENSP00000025008
#> 4 9606.ENSP00000156084
#> 5 9606.ENSP00000212015
#> 6 9606.ENSP00000215754
#> # … with 316 more rows
#> #
#> # Edge Data: 479 × 3
#> from to weight
#> <int> <int> <dbl>
#> 1 1 51 2
#> 2 1 68 2
#> 3 2 68 2
#> # … with 476 more rowsHere we can see that the returned interactions with TP53 includes another 321 proteins. This is in stark contrast to the maximum of 50 first shell entries returned by the STRING web tool. In addition to the large number of interacting proteins, notice that many of them are connected to each other as well, given the number of edges compared to the number of nodes.
We can also examine, for this limited network, how many genes TP53 interacts with when we allow one protein in between. For this example, this is likely to be lower than the real number, as it is a limited dataset. In reality, TP53 interactors with a single hop is an incredibly large number of interactors that you will want to watch your memory usage.
tp53_onehop = find_nodes_n_hops(ppi_graph, n_hops = 1, start_nodes = tp53_alias$string)
tp53_onehop$graph
#> # A tbl_graph: 1275 nodes and 2461 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 1,275 × 1 (active)
#> name
#> <chr>
#> 1 9606.ENSP00000001008
#> 2 9606.ENSP00000003084
#> 3 9606.ENSP00000005340
#> 4 9606.ENSP00000011619
#> 5 9606.ENSP00000011653
#> 6 9606.ENSP00000025008
#> # … with 1,269 more rows
#> #
#> # Edge Data: 2,461 × 3
#> from to weight
#> <int> <int> <dbl>
#> 1 1 676 2
#> 2 2 386 2
#> 3 3 149 2
#> # … with 2,458 more rowsEven with the limited network, we end up with a whopping 1274 proteins that interact one hop out from TP53!
There are several improvements we have in mind to make to make this package more useful:
- functions to easily add protein identifiers to the returned network.
- take pathway data from the
graphitepackage and work with it, we aren’t actually limited to STRING data. - visualization examples.
- tests. We really need more tests.
- could we use
sqlitedatabases to enable analysis of things with really large numbers of interactors?
Note that the ‘STRINGDatabaseManipulation’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.