README.md

In frequena/rbiolink: Interface with BioLink API using R

rbiolink

Overview

The package rbiolink provides an interface to the BioLink API.

Installation

You can install the development version from Github.

# install.packages("devtools")
devtools::install_github("frequena/rbiolink")

Examples

Get biological entities similar to your query

library(rbiolink)

biolink_bioentity(ref_id = 'gene', id = 'HGNC:2865', query_id = 'anatomy')
#> # A tibble: 20 x 16
#>    id    type  subject$taxon$id $$label $id   $label $iri  $category
#>    <chr> <lgl> <chr>            <chr>   <chr> <chr>  <chr> <list>   
#>  1 4fde… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#>  2 d7de… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#>  3 8002… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#>  4 cba3… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#>  5 351c… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#>  6 d8d3… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#>  7 bbb2… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#>  8 2ae7… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#>  9 0726… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 10 abbd… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 11 b18f… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 12 52bb… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 13 c3f2… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 14 42bf… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 15 9a4d… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 16 9712… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 17 532c… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 18 cba8… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 19 e84b… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> 20 e0cd… NA    NCBITaxon:9606   Homo s… HGNC… DHH    http… <chr [1]>
#> # … with 23 more variables: subject_eq <lgl>, subject_extensions <lgl>,
#> #   object$taxon$id <chr>, $$label <chr>, $id <chr>, $label <chr>,
#> #   $iri <chr>, $category <list>, object_eq <lgl>,
#> #   object_extensions <lgl>, relation$inverse <lgl>, $id <chr>,
#> #   $label <chr>, $iri <chr>, $category <lgl>, slim <lgl>, negated <lgl>,
#> #   qualifiers <lgl>, evidence_graph$nodes <list>, $edges <list>,
#> #   evidence_types <list>, provided_by <list>, publications <list>

Named entities recognition

This is a basic example which shows you how to solve a common problem:

abstract_input <- 'Marfan syndrome is a multisystemic genetic condition affecting connective tissue. It carries a reduced life expectancy, largely dependent on cardiovascular complications. More common cardiac manifestations such as aortic dissection and aortic valve incompetence have been widely documented in the literature. Mitral valve prolapse (MVP), however, has remained poorly documented. This article aims at exploring the existing literature on the pathophysiology and diagnosis of MVP in patients with Marfan syndrome, defining its current management and outlining the future developments surrounding it.'

result <- biolink_annotate('nlp/annotate', content = abstract_input) 

result$content
#> # A tibble: 3 x 3
#>   name              id             category             
#>   <chr>             <chr>          <chr>                
#> 1 "\"Tissue"        WBbt:0005729   anatomical entity    
#> 2 connective tissue UBERON:0002384 anatomical entity    
#> 3 tissue            UBERON:0000479 "anatomical entity\""

Search phenotypically similar disease and genes

biolink_search_similar(id = c('HP:0001166', 'HP:0030029'), metric = 'symmetric_resnik')
#> # A tibble: 100 x 8
#>    rank  score significance pairwise_match type  taxon$id $label id   
#>    <chr> <dbl>        <dbl> <list>         <chr> <chr>    <chr>  <chr>
#>  1 1      4.93          NaN <df[,3] [1 × … dise… NCBITax… Homo … MOND…
#>  2 1      4.93          NaN <df[,3] [1 × … dise… NCBITax… Homo … MOND…
#>  3 1      4.93          NaN <df[,3] [1 × … dise… NCBITax… Homo … MOND…
#>  4 1      4.93          NaN <df[,3] [1 × … dise… NCBITax… Homo … MOND…
#>  5 1      4.93          NaN <df[,3] [1 × … dise… NCBITax… Homo … MOND…
#>  6 1      4.93          NaN <df[,3] [1 × … dise… NCBITax… Homo … MOND…
#>  7 1      4.93          NaN <df[,3] [1 × … dise… <NA>     <NA>   OMIA…
#>  8 1      4.93          NaN <df[,3] [1 × … dise… NCBITax… Homo … MOND…
#>  9 1      4.93          NaN <df[,3] [1 × … dise… NCBITax… Homo … MOND…
#> 10 2      4.89          NaN <df[,3] [1 × … dise… NCBITax… Homo … MOND…
#> # … with 90 more rows, and 1 more variable: label <chr>

Calculate phenotypic similarity between two set of list

biolink_similarity(ref_id = c('HP:0001166', 'HP:0030029'),
                           query_id = c('HP:0001627', 'HP:0004095'),
                           metric = 'symmetric_resnik')$lcs_ic
#> [1] 4.934727

frequena/rbiolink documentation built on May 16, 2020, 10:20 p.m.