Introduction

STRING is a comprehensive database of known and predicted protein-protein associations. It integrates direct physical interactions and indirect functional associations from experimental evidence, curated databases, text mining, co-expression, and computational predictions. rbioapi provides a consistent R interface to STRING’s mapping, network, homology, annotation, and enrichment API resources.

The species argument

Most rbioapi STRING functions accept a species argument containing an NCBI Taxonomy identifier, such as 9606 for human. Supplying it is recommended because it helps STRING resolve ambiguous identifiers.

For the network, interaction-partner, homology, PPI-enrichment, network-image, and enrichment-image functions, species is required when the input contains more than 10 unique IDs. Mapping, functional-enrichment, and functional-annotation requests do not have this local restriction. The limit is based on unique IDs rather than the length of the original vector.

Map your IDs to STRING IDs

Although STRING recognizes a variety of identifiers, mapping them to STRING IDs before using other rbioapi STRING functions makes subsequent requests faster and less ambiguous.

## 1. Create a vector of protein identifiers
proteins <- c(
  "p53", "BRCA1", "cdk2", "Q99835", "CDC42", "CDK1", "KIF23",
  "PLK1", "RAC2", "RACGAP1", "RHOA", "RHOB", "PHF14", "RBM3"
)

## 2. Map the protein identifiers
proteins_mapped_df <- rba_string_map_ids(ids = proteins, species = 9606)

## 3. Use the mapped STRING IDs in the remaining examples
proteins_mapped <- proteins_mapped_df$stringId

Get interaction network of a protein set

You can retrieve interactions among the proteins in your set, together with the scores for each STRING evidence channel. Use required_score to set the minimum interaction score and network_type to select functional or physical associations.

See the Value section of the rba_string_interactions_network() manual for descriptions of the returned columns.

int_net <- rba_string_interactions_network(
  ids = proteins_mapped,
  species = 9606,
  required_score = 500
)

Instead of supplying protein identifiers, you can retrieve the network formed by proteins annotated with a STRING functional term. In this mode, set ids = NULL and supply both network_term_id and species. rba_string_functional_terms() can search for functional terms using either an identifier or descriptive text:

tcr_terms <- rba_string_functional_terms(
  term_text = "T cell receptor signaling pathway",
  species = 9606
)

The first match for this search is GO:0050852, which identifies the GO term “T cell receptor signaling pathway”:

term_net <- rba_string_interactions_network(
  ids = NULL,
  network_term_id = "GO:0050852",
  species = 9606,
  required_score = 900
)

The proteinCount column contains the complete number of proteins annotated with each term, while preferredNames and stringIds are returned as list-columns. The ids and network_term_id input modes cannot be combined.

Get interaction partners of a protein set

The previous example returned interactions among the input proteins. To retrieve proteins that interact with one or more input proteins, including proteins outside the input set, use rba_string_interaction_partners():

## This example uses one protein (CD40), but `ids` can contain multiple proteins
int_partners <- rba_string_interaction_partners(
  ids = "9606.ENSP00000361359",
  species = 9606,
  required_score = 900
)

Get network image of a protein set

STRING can render the interaction network as a static image. The rba_string_network_image() function supports several options for controlling the network contents and appearance; see the function manual for details. It also supports the same network_term_id input mode described above.

## Example 1:
graph_ppi1 <- rba_string_network_image(
  ids = proteins_mapped,
  image_format = "image",
  species = 9606,
  save_image = FALSE,
  required_score = 500,
  network_flavor = "confidence"
)

For example, the network for a functional term can be rendered with:

term_image <- rba_string_network_image(
  ids = NULL,
  network_term_id = "GO:0050852",
  species = 9606,
  required_score = 900,
  save_image = FALSE
)

Network images - Example 1

## Example 2:
graph_ppi2 <- rba_string_network_image(
  ids = proteins_mapped,
  image_format = "image",
  species = 9606,
  save_image = FALSE,
  required_score = 500,
  add_color_nodes = 5,
  add_white_nodes = 5,
  network_flavor = "actions"
)

Network images - Example 2

Enrichment analysis using STRING

STRING supports two complementary enrichment analyses: functional enrichment and protein-protein interaction enrichment. See the STRING publication for methodological details.

enriched <- rba_string_enrichment(
  ids = proteins_mapped,
  species = 9606
)

str(enriched, max.level = 1)
#> List of 14
#>  $ COMPARTMENTS     :'data.frame':   25 obs. of  10 variables:
#>  $ Component        :'data.frame':   17 obs. of  10 variables:
#>  $ DISEASES         :'data.frame':   11 obs. of  10 variables:
#>  $ Function         :'data.frame':   12 obs. of  10 variables:
#>  $ InterPro         :'data.frame':   3 obs. of  10 variables:
#>  $ KEGG             :'data.frame':   45 obs. of  10 variables:
#>  $ Keyword          :'data.frame':   14 obs. of  10 variables:
#>  $ NetworkNeighborAL:'data.frame':   5 obs. of  10 variables:
#>  $ PMID             :'data.frame':   100 obs. of  10 variables:
#>  $ Process          :'data.frame':   148 obs. of  10 variables:
#>  $ RCTM             :'data.frame':   59 obs. of  10 variables:
#>  $ SMART            :'data.frame':   1 obs. of  10 variables:
#>  $ TISSUES          :'data.frame':   12 obs. of  10 variables:
#>  $ WikiPathways     :'data.frame':   46 obs. of  10 variables:

Functional enrichment plot

The enrichment-figure endpoint summarizes one enrichment category as a plot. Use rba_string_enrichment_image() to control the category, grouping, palette, ranking variable, output format, and number of displayed terms. This example visualizes the KEGG enrichment for the same protein set:

graph_enrich <- rba_string_enrichment_image(
  ids = proteins_mapped,
  species = 9606,
  category = "KEGG",
  image_format = "image",
  save_image = FALSE,
  group_by_similarity = 0.6
)

Visualization of enrichment analysis results

rba_string_enrichment_ppi(
  ids = proteins_mapped,
  species = 9606
)
#> $number_of_nodes
#> [1] 14
#> 
#> $number_of_edges
#> [1] 40
#> 
#> $average_node_degree
#> [1] 5.71
#> 
#> $local_clustering_coefficient
#> [1] 0.694
#> 
#> $expected_number_of_edges
#> [1] 19
#> 
#> $p_value
#> [1] 1.35e-05

Get functional annotations

STRING maps proteins to multiple annotation resources. Use rba_string_annotations() to retrieve all annotations assigned to the input proteins rather than only the subset returned by enrichment analysis. PubMed annotations are excluded by default because they can make the response very large.

annotations <- rba_string_annotations(
  ids = "9606.ENSP00000269305",
  species = 9606
)

## Annotation responses can be large, so the result is not shown here

Set allow_pubmed = TRUE to include PubMed annotations with the other categories, or set only_pubmed = TRUE to retrieve only PubMed annotations. only_pubmed takes precedence if both arguments are TRUE.

pubmed_annotations <- rba_string_annotations(
  ids = "9606.ENSP00000269305",
  species = 9606,
  only_pubmed = TRUE
)

See also in Functions’ manuals

Some rbioapi STRING functions were not covered in this vignette, please check their manuals:

• rba_string_homology_intra()

• rba_string_homology_inter()

• rba_string_version()

How to cite

To cite STRING (Please see https://string-db.org/cgi/about?footer_active_subpage=references):

• Damian Szklarczyk, Rebecca Kirsch, Mikaela Koutrouli, Katerina Nastou, Farrokh Mehryary, Radja Hachilif, Annika L Gable, Tao Fang, Nadezhda T Doncheva, Sampo Pyysalo, Peer Bork, Lars J Jensen, Christian von Mering, The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest, Nucleic Acids Research, Volume 51, Issue D1, 6 January 2023, Pages D638–D646, https://doi.org/10.1093/nar/gkac1000

To cite rbioapi:

• Moosa Rezwani, Ali Akbar Pourfathollah, Farshid Noorbakhsh, rbioapi: user-friendly R interface to biologic web services’ API, Bioinformatics, Volume 38, Issue 10, 15 May 2022, Pages 2952–2953, https://doi.org/10.1093/bioinformatics/btac172

Links

• This article in rbioapi documentation site

• Functions references in rbioapi documentation site

• rbioapi vignette index

Session info

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