edges() and nodes() identify edges or nodes in the
data base.
db_gene_variants() locates variants associated with a
(Ensembl) gene identifier.
Usage
db_edges(
username = rigvf_config$get("username"),
password = rigvf_config$get("password")
)
db_nodes(
username = rigvf_config$get("username"),
password = rigvf_config$get("password")
)
db_gene_variants(
gene_id,
threshold,
username = rigvf_config$get("username"),
password = rigvf_config$get("password")
)Arguments
- username
character(1) ArangoDB user name. Default: "guest".
- password
character(1) ArangoDB password. Default: "guestigvfcatalog". A better practice is to use an environment variable to record the password, rather than encoding in a script, so
password = Sys.getenv("RIGVF_ARANGODB_PASSWORD").- gene_id
character(1) Ensembl gene identifier.
- threshold
numeric(1) minimum score associated with the variant.
Value
edges() and nodes() return a tibble with the edge or
node name and count of occurrences in the database.
db_gene_variants() returns a tibble summarizing variants
associated with the gene.
Examples
db_edges()
#> # A tibble: 36 × 2
#> name count
#> <chr> <dbl>
#> 1 variants_variants 5979387121
#> 2 coding_variants_proteins 239108968
#> 3 variants_coding_variants 239108968
#> 4 variants_genes_terms 95728528
#> 5 variants_genes 95728528
#> 6 regulatory_regions_genes 30197460
#> 7 regulatory_regions_genes_biosamples_donors 21075288
#> 8 regulatory_regions_genes_biosamples 19703741
#> 9 genes_genes 3553549
#> 10 variants_proteins 2927144
#> # ℹ 26 more rows
db_nodes()
#> # A tibble: 17 × 2
#> name count
#> <chr> <dbl>
#> 1 variants 1304691412
#> 2 coding_variants 239107830
#> 3 mm_variants 101894574
#> 4 regulatory_regions 17138182
#> 5 mm_regulatory_regions 3162620
#> 6 ontology_terms 728868
#> 7 proteins 294001
#> 8 transcripts 274031
#> 9 mm_transcripts 149547
#> 10 genes 69222
#> 11 mm_genes 56941
#> 12 studies 22690
#> 13 drugs 4613
#> 14 pathways 2629
#> 15 complexes 1527
#> 16 motifs 401
#> 17 donors 231
db_gene_variants("ENSG00000106633", 0.85)
#> # A tibble: 40 × 9
#> `_key` `_id` `_from` `_to` `_rev` `score:long` source source_url
#> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 genic_chr7_4415452… regu… regula… gene… _g5CU… 0.989 ENCOD… https://w…
#> 2 promoter_chr7_4415… regu… regula… gene… _g5CU… 0.869 ENCOD… https://w…
#> 3 genic_chr7_4414584… regu… regula… gene… _g5CU… 0.948 ENCOD… https://w…
#> 4 promoter_chr7_4415… regu… regula… gene… _g5CU… 1.00 ENCOD… https://w…
#> 5 promoter_chr7_4415… regu… regula… gene… _g5CU… 1.00 ENCOD… https://w…
#> 6 intergenic_chr7_44… regu… regula… gene… _g5CU… 0.959 ENCOD… https://w…
#> 7 genic_chr7_4415544… regu… regula… gene… _g5CV… 0.942 ENCOD… https://w…
#> 8 promoter_chr7_4415… regu… regula… gene… _g5CV… 0.929 ENCOD… https://w…
#> 9 intergenic_chr7_44… regu… regula… gene… _g5CV… 0.936 ENCOD… https://w…
#> 10 promoter_chr7_4415… regu… regula… gene… _g5CW… 0.966 ENCOD… https://w…
#> # ℹ 30 more rows
#> # ℹ 1 more variable: biological_context <chr>