Guillaume Voisinne 2023 - 02 - 08
The queryup R package aims to facilitate retrieving
information from the UniProt database using R. Programmatic access to
the UniProt database is performed by submitting queries to the UniProt website REST
API.
You can install the package from CRAN using:
install.packages("queryup")Alternatively, you may also install the package from github using devtools:
devtools::install_github("VoisinneG/queryup")Queries combine different fields to identify matching database
entries. Here, queries are submitted using the function
query_uniprot(). In the queryup R package, a
query must be formatted as a list containing character vectors named
after existing UniProt fields (available query fields can be found in
the API
documentation or in the package data
query_fields$field). Different query fields must be matched
simultaneously. For instance, the following query uses the fields
gene_exact to return the UniProt entries of all proteins
encoded by gene Pik3r1 :
library(queryup)query <- list("gene_exact" = "Pik3r1")
df <- query_uniprot(query, show_progress = FALSE)
head(df)## Entry Entry Name Gene Names Organism (ID) Reviewed
## 2 A0A096MNU6 A0A096MNU6_PAPAN PIK3R1 9555 unreviewed
## 3 A0A0D9RTM6 A0A0D9RTM6_CHLSB PIK3R1 60711 unreviewed
## 4 A0A1S3F3Z7 A0A1S3F3Z7_DIPOR Pik3r1 10020 unreviewed
## 5 A0A1U7Q814 A0A1U7Q814_MESAU Pik3r1 10036 unreviewed
## 6 A0A287DCB8 A0A287DCB8_ICTTR PIK3R1 43179 unreviewed
## 7 A0A2I2ZTD7 A0A2I2ZTD7_GORGO PIK3R1 9595 unreviewedAvailable query fields can be listed using the package data
query_fields:
query_fields$field## [1] "accession"
## [2] "active"
## [3] "Refer to the page: Sequence Annotations"
## [4] "lit_author"
## [5] "protein_name"
## [6] "chebi"
## [7] "uniprot_id (/uniref), then uniref_cluster_90 (/uniprotkb)"
## [8] "xrefcount_pdb (or xref_count)"
## [9] "date_created"
## [10] "database, xref"
## [11] "ec"
## [12] "Refer to the pages: Comments or Sequence Annotations"
## [13] "existence"
## [14] "family"
## [15] "fragment"
## [16] "gene"
## [17] "gene_exact"
## [18] "go"
## [19] "virus_host_name, virus_host_id"
## [20] "accession_id"
## [21] "inchikey"
## [22] "protein_name"
## [23] "interactor"
## [24] "keyword"
## [25] "length"
## [26] "mass"
## [27] "cc_mass_spectrometry"
## [28] "date_modified"
## [29] "protein_name"
## [30] "organelle"
## [31] "organism_name, organism_id"
## [32] "plasmid"
## [33] "proteome"
## [34] "proteomecomponent"
## [35] "sec_acc"
## [36] "reviewed"
## [37] "scope"
## [38] "sec_acc"
## [39] "sequence"
## [40] "date_sequence_modified"
## [41] "strain"
## [42] "taxonomy_name, taxonomy_id"
## [43] "tissue"
## [44] "cc_webresource"By default, query_uniprot() returns a data.frame with
UniProt accession IDs, gene names, organism and Swiss-Prot review
status. You can choose which data columns to retrieve using the
columns parameter.
df <- query_uniprot(query,
columns = c("id", "sequence", "keyword", "gene_primary"),
show_progress = FALSE)## Warning in (function (..., deparse.level = 1) : number of columns of result is
## not a multiple of vector length (arg 881)See the API
documentation or the package data return_fields for all
available columns. Available returned fields can be listed using the
package data return_fields:
head(return_fields)## field label
## 1 accession Entry
## 2 id Entry name
## 3 gene_names Gene names
## 4 gene_primary Gene names (primary)
## 5 gene_synonym Gene names (synonym)
## 6 gene_oln Gene names (ordered locus)Note that the parameter columns and the name of the
corresponding column in the output data frame do not necessarily match
(they correspond to columns “field” and “label” respectively in the
package data return_fields).
names(df)## [1] "Entry" "Entry Name" "Sequence"
## [4] "Keywords" "Gene Names (primary)"Let’s check the sequence and the UniProt keywords corresponding to the first entry :
as.character(df$Sequence[1])## [1] "MSAEGYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFSDGQEARPEEIGWLNGYNETTGERGDFPGTYVEYIGRKKISPPTPKPRPPRPLPVAPGSSKTEADVEQQALTLPDLAEQFAPPDVAPPLLIKLVEAIEKKGLECSTLYRTQSSGNLAELRQLLDCDTASVDLEMIDVHILADAFKRYLLDLPNPVIPAAVYSEMISLAQEVQSSEEYIQLLKKLIRSPSIPHQYWLTLQYLLKHFFKLSQTSSKNLLNARVLSEIFSPMLFRFSAASSDNTENLIKVIEILISTEWNERQPAPALPPKPPKPTTVANNGMNNNMSLQDAEWYWGDISREEVNEKLRDTADGTFLVRDASTKMHGDYTLTLRKGGNNKLIKIFHRDGKYGFSDPLTFNSVVELINHYRNESLAQYNPKLDVKLLYPVSKYQQDQVVKEDNIEAVGKKLHEYNTQFQEKSREYDRLYEEYTRTSQEIQMKRTAIEAFNETIKIFEEQCQTQERYSKEYIEKFKREGNEKEIQRIMHNYDKLKSRISEIIDSRRRLEEDLKKQAAEYREIDKRMNSIKPDLIQLRKTRDQYLMWLTQKGVRQKKLNEWLGNENTEDQYSLVEDDEDLPHHDEKTWNVGSSNRNKAENLLRGKRDGTFLVRESSKQGCYACSVVVDGEVKHCVINKTATGYGFAEPYNLYSSLKELVLHYQHTSLVQHNDSLNVTLAYPVYAQDSYFIFQGNMGRMHGNGHSM"as.character(df$Keywords[1])## [1] "Coiled coil;Protein transport;Reference proteome;Repeat;SH2 domain;SH3 domain;Stress response;Transport"Our first query returned many matches. We can build more specific queries by using more than one query field. By default, matching entries must satisfy all query fields simultaneously. Let’s retrieve the only Swiss-Prot reviewed protein entry encoded by gene Pik3r1 in Homo sapiens (taxon: 9606):
query <- list("gene_exact" = "Pik3r1",
"reviewed" = "true",
"organism_id" = "9606")
df <- query_uniprot(query, show_progress = FALSE)
print(df)## Entry Entry Name Gene Names Organism (ID) Reviewed
## 2 P27986 P85A_HUMAN PIK3R1 GRB1 9606 reviewedIt is also possible to look for entries that match different items within a single query field. Items from a given query field are looked for independently. Hence, the following query will return all Swiss-Prot reviewed proteins encoded by either Pik3r1 or Pik3r2 in either Mus musculus (taxon: 10090) or Homo sapiens (taxon: 9606):
query <- list("gene_exact" = c("Pik3r1", "Pik3r2"),
"reviewed" = "true",
"organism_id" = c("9606", "10090"))
df <- query_uniprot(query, show_progress = FALSE)
print(df)## Entry Entry Name Gene Names Organism (ID) Reviewed
## 2 O00459 P85B_HUMAN PIK3R2 9606 reviewed
## 3 O08908 P85B_MOUSE Pik3r2 10090 reviewed
## 4 P26450 P85A_MOUSE Pik3r1 10090 reviewed
## 5 P27986 P85A_HUMAN PIK3R1 GRB1 9606 reviewedIf a query containing invalid entries is sent to the UniProt REST
API, an error message is returned and no information about the other
potentially valid entries can be retrieved. To overcome this limitation,
queryup parses the error messages and remove invalid
entries from the query. Hence, query_uniprot() will return
information for valid entries only :
invalid_ids <- c("P226", "CON_P22682", "REV_P47941")
valid_ids <- c("A0A0U1ZFN5", "P22682")
ids <- c(invalid_ids, valid_ids)
query <- list("accession_id" = ids)
query_uniprot(query)## 3 invalid values were found (P226, CON_P22682, REV_P47941) and removed from the query.
## Entry Entry Name Gene Names Organism (ID) Reviewed
## 2 A0A0U1ZFN5 A0A0U1ZFN5_RAT Cbl c-Cbl 10116 unreviewed
## 3 P22682 CBL_MOUSE Cbl 10090 reviewedBecause UniProt REST API limits the size of queries, long queries
containing more than a few hundreds entries cannot be passed in a single
request. To overcome this limitation, the queryup package
splits long queries into smaller ones. For instance, the dataset
uniprot_entries that is bundled with the
queryup package contains information for 1000 UniProt
entries. We could retrieve the ENSEMBL ids corresponding to these
entries using :
ids <- uniprot_entries$Entry
query <- list("accession_id" = ids)
columns <- c("gene_names", "xref_ensembl")
df <- query_uniprot(query, columns = columns, show_progress = FALSE)
head(df)## Entry Gene Names
## 2 A0A087WPF7 Auts2 Kiaa0442
## 3 A0A088MLT8 Iqcj-Schip1 Iqschfp Schip1
## 4 A0A0B4J1F4 Arrdc4
## 5 A0A0B4J1G0 Fcgr4 Fcgr3a
## 6 A0A0G2JDV3 Gbp6 Mpa2l
## 7 A0A0U1RPR8 Gucy2d
## Ensembl
## 2 ENSMUST00000161226 [A0A087WPF7-1];ENSMUST00000161374 [A0A087WPF7-3];
## 3 ENSMUST00000182006 [A0A088MLT8-1];
## 4 ENSMUST00000048068 [A0A0B4J1F4-1];ENSMUST00000118110 [A0A0B4J1F4-2];
## 5 ENSMUST00000078825;
## 6 A0A0G2JDV3
## 7 ENSMUST00000206435;Another usage could be to retrieve protein-protein interactions among a set of UniProt entries:
ids <- sample(uniprot_entries$Entry, 400)
query <- list("accession_id" = ids,
"interactor" = ids)
columns <- "cc_interaction"
df <- query_uniprot(query = query, columns = columns, show_progress = FALSE)
head(df)## Entry Interacts with
## 2 A2A259 Q2EG98; A2A259
## 3 O88273 O88273
## 4 O88522 Q60680; O88351; O88522; Q924T7; P62991; P0CG48; P24772
## 21 E9Q401 Q6PHZ2; Q9Z2I2; Q8K4S1; E9Q401; P23327