# Introduction to queryup

    library(queryup)

The purpose of `queryup` is to retrieve protein information using queries to the UniProtKB REST API.

## Queries

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 :

    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 unreviewed

Available 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"

## Columns

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)

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"

## Combining query fields

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 reviewed

## Multiple items per query field

It 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 reviewed

## Queries with invalid entries

If 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   reviewed

## Long queries

Because 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 ENSMUST00000161226.11;ENSMUST00000161374.8 [A0A087WPF7-3];
    #> 3                                      ENSMUST00000182006.4;
    #> 4 ENSMUST00000048068.15;ENSMUST00000118110.3 [A0A0B4J1F4-2];
    #> 5                                      ENSMUST00000078825.5;
    #> 6                                                 A0A0G2JDV3
    #> 7                                      ENSMUST00000206435.2;

## Protein-protein interactions

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
    #> 2   O35681
    #> 23  A2A259
    #> 3   O54943
    #> 21  O54943
    #> 22  O08785
    #> 211 A2AG06
    #>                                                                                                                                                 Interacts with
    #> 2                                                                                                                               Q9R0N4; O35681; Q9R0N8; Q9R0N9
    #> 23                                                                                                                                              Q2EG98; A2A259
    #> 3                                                       Q9WTL8; Q91VJ2; Q3TQ03; O08785; P97784; Q9R194; Q9JMK2; Q8C4V4; O35973; O54943; Q60953; Q8N365; P20393
    #> 21                                                      Q9WTL8; Q91VJ2; Q3TQ03; O08785; P97784; Q9R194; Q9JMK2; Q8C4V4; O35973; O54943; Q60953; Q8N365; P20393
    #> 22  Q9WTL8; Q9WTL8-2; Q9WTL8-4; P97784; Q9JMK2; Q3U1J4; O54943; P20444; Q923E4; P67870; Q03164; Q14995; P62136; P62140; P36873; P30154; Q14738; Q92753; P51449
    #> 211                                                                                                                                             B2RR83; Q9H6S0