hgnc

The HUGO Gene Nomenclature Committee (HGNC) is a committee of the Human Genome Organisation (HUGO) that sets the standards for human gene nomenclature.

The HGNC approves a unique and meaningful name for every known human gene, based on a group of experts. In addition, the HGNC also provides the mapping between gene symbols to gene entries in other popular databases or resources: the HGNC complete gene set.

The goal of {hgnc} is to easily download and import the latest HGNC complete gene data set into R.

This data set provides a useful mapping of HGNC symbols to gene entries in other popular databases or resources, such as, the Entrez gene identifier or the UCSC gene identifier, among many others. Check the documentation of the function import_hgnc_dataset() for a description of the several fields available.

Installation

Install {hgnc} from CRAN:

install.packages("hgnc")

You can install the development version of {hgnc} like so:

# install.packages("remotes")
remotes::install_github("ramiromagno/hgnc")

Usage

Basic usage

To import the latest HGNC gene data set in tabular format directly into memory as a tibble do as follows:

library(hgnc)

# Date of HGNC last update
last_update()
#> [1] "2023-08-31 02:04:51 UTC"

# Set the HGNC archive file to use for the remainder of the R-session
use_hgnc_file(file = latest_archive_url())
#> using hgnc file: https://ftp.ebi.ac.uk/pub/databases/genenames/hgnc/tsv/hgnc_complete_set.txt

# Import the data set in tidy tabular format
# NB: Multiple-value columns are kept as list-columns
hgnc_dataset <- import_hgnc_dataset()

dplyr::glimpse(hgnc_dataset)
#> Rows: 43,718
#> Columns: 55
#> $ hgnc_id                  <chr> "HGNC:5", "HGNC:37133", "HGNC:24086", "HGNC:7…
#> $ hgnc_id2                 <int> 5, 37133, 24086, 7, 27057, 23336, 41022, 4152…
#> $ symbol                   <chr> "A1BG", "A1BG-AS1", "A1CF", "A2M", "A2M-AS1",…
#> $ name                     <chr> "alpha-1-B glycoprotein", "A1BG antisense RNA…
#> $ locus_group              <chr> "protein-coding gene", "non-coding RNA", "pro…
#> $ locus_type               <chr> "gene with protein product", "RNA, long non-c…
#> $ status                   <chr> "Approved", "Approved", "Approved", "Approved…
#> $ location                 <chr> "19q13.43", "19q13.43", "10q11.23", "12p13.31…
#> $ location_sortable        <chr> "19q13.43", "19q13.43", "10q11.23", "12p13.31…
#> $ alias_symbol             <list> NA, "FLJ23569", <"ACF", "ASP", "ACF64", "ACF…
#> $ alias_name               <list> NA, NA, NA, NA, NA, NA, NA, NA, NA, <"iGb3 s…
#> $ prev_symbol              <list> NA, <"NCRNA00181", "A1BGAS", "A1BG-AS">, NA,…
#> $ prev_name                <list> NA, <"non-protein coding RNA 181", "A1BG ant…
#> $ gene_group               <list> "Immunoglobulin like domain containing", "An…
#> $ gene_group_id            <list> "594", "1987", "725", "2148", "1987", "2148"…
#> $ date_approved_reserved   <date> 1989-06-30, 2009-07-20, 2007-11-23, 1986-01-…
#> $ date_symbol_changed      <date> NA, 2010-11-25, NA, NA, NA, 2005-09-01, NA, …
#> $ date_name_changed        <date> NA, 2012-08-15, NA, NA, 2018-03-21, 2016-03-…
#> $ date_modified            <date> 2023-01-20, 2013-06-27, 2023-01-20, 2023-01-…
#> $ entrez_id                <int> 1, 503538, 29974, 2, 144571, 144568, 10087410…
#> $ ensembl_gene_id          <chr> "ENSG00000121410", "ENSG00000268895", "ENSG00…
#> $ vega_id                  <chr> "OTTHUMG00000183507", "OTTHUMG00000183508", "…
#> $ ucsc_id                  <chr> "uc002qsd.5", "uc002qse.3", "uc057tgv.1", "uc…
#> $ ena                      <list> NA, "BC040926", "AF271790", <"BX647329", "X6…
#> $ refseq_accession         <list> "NM_130786", "NR_015380", "NM_014576", "NM_0…
#> $ ccds_id                  <list> "CCDS12976", NA, <"CCDS7242", "CCDS73133", "…
#> $ uniprot_ids              <list> "P04217", NA, "Q9NQ94", "P01023", NA, "A8K2U…
#> $ pubmed_id                <list> "2591067", NA, <"11815617", "11072063">, <"2…
#> $ mgd_id                   <list> "MGI:2152878", NA, "MGI:1917115", "MGI:24491…
#> $ rgd_id                   <list> "RGD:69417", NA, "RGD:619834", "RGD:2004", N…
#> $ lsdb                     <chr> NA, NA, NA, "LRG_591|http://ftp.ebi.ac.uk/pub…
#> $ cosmic                   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ omim_id                  <list> "138670", NA, "618199", "103950", NA, "61062…
#> $ mirbase                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ homeodb                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ snornabase               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ bioparadigms_slc         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ orphanet                 <chr> NA, NA, NA, NA, NA, "410627", NA, NA, NA, NA,…
#> $ pseudogene.org           <chr> NA, NA, NA, NA, NA, NA, NA, NA, "PGOHUM000002…
#> $ horde_id                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ merops                   <chr> "I43.950", NA, NA, "I39.001", NA, "I39.007", …
#> $ imgt                     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ iuphar                   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ kznf_gene_catalog        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ `mamit-trnadb`           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ cd                       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ lncrnadb                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ enzyme_id                <list> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "2.4…
#> $ intermediate_filament_db <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ rna_central_ids          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ lncipedia                <chr> NA, "A1BG-AS1", NA, NA, "A2M-AS1", NA, "A2ML1…
#> $ gtrnadb                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ agr                      <chr> "HGNC:5", "HGNC:37133", "HGNC:24086", "HGNC:7…
#> $ mane_select              <list> <"ENST00000263100.8", "NM_130786.4">, NA, <"…
#> $ gencc                    <chr> NA, NA, NA, "HGNC:7", NA, "HGNC:23336", NA, N…

The original data set does not contain the column hgnc_id2, which is added as a convenience by {hgnc}; this is because although the HGNC identifiers should formally contain the prefix "HGNC:", it is often found elsewhere that they are stripped of this prefix, so the column hgnc_id2 is also provided whose values only contain the integer part.

hgnc_dataset %>%
  dplyr::select(c('hgnc_id', 'hgnc_id2'))
#> # A tibble: 43,718 × 2
#>    hgnc_id    hgnc_id2
#>    <chr>         <int>
#>  1 HGNC:5            5
#>  2 HGNC:37133    37133
#>  3 HGNC:24086    24086
#>  4 HGNC:7            7
#>  5 HGNC:27057    27057
#>  6 HGNC:23336    23336
#>  7 HGNC:41022    41022
#>  8 HGNC:41523    41523
#>  9 HGNC:8            8
#> 10 HGNC:30005    30005
#> # … with 43,708 more rows

Locus groups

The HGNC defines a group name (locus_group) for a set of related locus types. Here’s how you can quickly check how many gene entries there are per locus group.

hgnc_dataset %>%
  dplyr::count(locus_group, sort = TRUE)
#> # A tibble: 4 × 2
#>   locus_group             n
#>   <chr>               <int>
#> 1 protein-coding gene 19278
#> 2 pseudogene          14362
#> 3 non-coding RNA       9087
#> 4 other                 991

locus_type provides a finer classification:

hgnc_dataset %>%
  dplyr::group_by(locus_group) %>%
  dplyr::count(locus_type, sort = TRUE) %>%
  dplyr::arrange(locus_group) %>%
  print(n = Inf)
#> # A tibble: 23 × 3
#> # Groups:   locus_group [4]
#>    locus_group         locus_type                     n
#>    <chr>               <chr>                      <int>
#>  1 non-coding RNA      RNA, long non-coding        5750
#>  2 non-coding RNA      RNA, micro                  1912
#>  3 non-coding RNA      RNA, transfer                591
#>  4 non-coding RNA      RNA, small nucleolar         568
#>  5 non-coding RNA      RNA, cluster                 119
#>  6 non-coding RNA      RNA, ribosomal                60
#>  7 non-coding RNA      RNA, small nuclear            50
#>  8 non-coding RNA      RNA, misc                     29
#>  9 non-coding RNA      RNA, vault                     4
#> 10 non-coding RNA      RNA, Y                         4
#> 11 other               immunoglobulin gene          230
#> 12 other               T cell receptor gene         206
#> 13 other               readthrough                  147
#> 14 other               fragile site                 116
#> 15 other               endogenous retrovirus        109
#> 16 other               complex locus constituent     69
#> 17 other               unknown                       68
#> 18 other               region                        38
#> 19 other               virus integration site         8
#> 20 protein-coding gene gene with protein product  19278
#> 21 pseudogene          pseudogene                 14122
#> 22 pseudogene          immunoglobulin pseudogene    203
#> 23 pseudogene          T cell receptor pseudogene    37

Setting the archive version

By default {hgnc} will use the latest version of HGNC archive dataset, as returned by the function latest_archive_url(). Besides the latest archive, the HUGO Gene Nomenclature Committee (HGNC) website also provides monthly and quarterly updates. You can conveniently get the latest monthly and quarterly updates by running latest_monthly_url() or latest_quarterly_url(). Use list_archives() to list the all currently available for download archives. The column url contains the direct download link that you can pass to use_hgnc_file() and import_hgnc_dataset() to import the data into R.

list_archives()
#> # A tibble: 107 × 7
#>    series  dataset           file     date       size  last_modified       url  
#>    <chr>   <chr>             <chr>    <date>     <chr> <dttm>              <chr>
#>  1 monthly hgnc_complete_set hgnc_co… 2021-03-01 14M   2023-05-01 00:05:00 http…
#>  2 monthly hgnc_complete_set hgnc_co… 2021-04-01 15M   2023-05-01 00:05:00 http…
#>  3 monthly hgnc_complete_set hgnc_co… 2021-05-01 15M   2023-05-01 00:05:00 http…
#>  4 monthly hgnc_complete_set hgnc_co… 2021-06-01 15M   2023-05-01 00:05:00 http…
#>  5 monthly hgnc_complete_set hgnc_co… 2021-07-01 15M   2023-05-01 00:05:00 http…
#>  6 monthly hgnc_complete_set hgnc_co… 2021-08-01 15M   2023-05-01 00:05:00 http…
#>  7 monthly hgnc_complete_set hgnc_co… 2021-09-01 15M   2023-05-01 00:05:00 http…
#>  8 monthly hgnc_complete_set hgnc_co… 2021-10-01 15M   2023-05-01 00:05:00 http…
#>  9 monthly hgnc_complete_set hgnc_co… 2021-11-01 15M   2023-05-01 00:05:00 http…
#> 10 monthly hgnc_complete_set hgnc_co… 2021-12-01 15M   2023-05-01 00:05:00 http…
#> # … with 97 more rows

use_hgnc_file(file = latest_monthly_url())
#> using hgnc file: https://ftp.ebi.ac.uk/pub/databases/genenames/hgnc/archive/monthly/tsv/hgnc_complete_set_2023-08-01.txt

Downloading to disk

If you prefer to download the data set as a file to disk first, you can use download_archive(). Then, you can use import_hgnc_dataset() to import the downloaded file into R.

Convert between gene identifiers

Two convenience functions are provided to convert between different gene identifiers in the HGNC dataset: hgnc_join takes as input a data frame and adds an additional column from the HGNC dataset, and hgnc_convert converts a vector from one identifier to another.

dplyr::tibble(hgnc_id = c("HGNC:44948", "HGNC:43240", "HGNC:23357", "HGNC:1855", "HGNC:39400")) %>% 
  # add in gene symbol
  hgnc_join(by = 'hgnc_id', column = 'symbol') %>% 
  # add in entrez_id
  hgnc_join(by = 'hgnc_id', column = 'entrez_id')
#> # A tibble: 5 × 3
#>   hgnc_id    symbol    entrez_id
#>   <chr>      <chr>         <int>
#> 1 HGNC:44948 GOLGA2P6     729668
#> 2 HGNC:43240 RNA5SP340 100873603
#> 3 HGNC:23357 MCTS1         28985
#> 4 HGNC:1855  CENPCP1        1061
#> 5 HGNC:39400 ASNSP4    100419423

# convert a set of hgnc_ids to symbols
hgnc_ids <- c("HGNC:44948", "HGNC:43240", "HGNC:23357", "HGNC:1855", "HGNC:39400")
hgnc_convert(hgnc_ids, from = 'hgnc_id', to = 'symbol')
#> [1] "GOLGA2P6"  "RNA5SP340" "MCTS1"     "CENPCP1"   "ASNSP4"

# convert a set of entrez_ids to ensembl_gene_ids
entrez_ids <- c(79933, 109623458, 158471, 54987, 81631)
hgnc_convert(entrez_ids, from = 'entrez_id', to = 'ensembl_gene_id')
#> [1] "ENSG00000166317" NA                "ENSG00000106772" "ENSG00000162384"
#> [5] "ENSG00000140941"

Caching

By default {hgnc} will use memory-based caching through the package {memoise}. This will ensure that time consuming downloads are not rerun unnecessarily during a single R-session. Persistent disk based caching can also be enabled using the function use_cache_dir.

use_cache_dir(cache_dir =  './hgnc_cache')
#> using hgnc cache dir: ./hgnc_cache

Motivation

You could go to www.genenames.org and download the files yourself. So why the need for this R package?

{hgnc} really is just a convenience package. The main advantage is that the function import_hgnc_dataset() reads in the data in tabular format with all the columns with the appropriate type (so you don’t have to specify it yourself). As an extra step, those variables that contain multiple values are encoded as list-columns.

Remember that list-columns can be expanded with tidyr::unnest(). E.g., alias_symbol is a list-column containing multiple alternative aliases to the standard symbol:

hgnc_dataset %>%
  dplyr::filter(symbol == 'TP53') %>%
  dplyr::select(c('symbol', 'alias_symbol'))
#> # A tibble: 1 × 2
#>   symbol alias_symbol
#>   <chr>  <list>      
#> 1 TP53   <chr [2]>

hgnc_dataset %>%
  dplyr::filter(symbol == 'TP53') %>%
  dplyr::select(c('symbol', 'alias_symbol')) %>%
  tidyr::unnest(cols = 'alias_symbol')
#> # A tibble: 2 × 2
#>   symbol alias_symbol
#>   <chr>  <chr>       
#> 1 TP53   p53         
#> 2 TP53   LFS1

In addition, we also provide the function filter_by_keyword() that allows filtering the data set based on a keyword or regular expression. By default this function will look into all columns that contain gene symbols or names (symbol, name, alias_symbol, alias_name, prev_symbol and prev_name). It works automatically with list-columns too.

Look for entries in the data set that contain the keyword "TP53":

hgnc_dataset %>%
  filter_by_keyword('TP53') %>%
  dplyr::select(1:4)
#> # A tibble: 66 × 4
#>    hgnc_id    hgnc_id2 symbol      name                                         
#>    <chr>         <int> <chr>       <chr>                                        
#>  1 HGNC:49685    49685 ABHD15-AS1  ABHD15 antisense RNA 1                       
#>  2 HGNC:20679    20679 ANO9        anoctamin 9                                  
#>  3 HGNC:40093    40093 BCAR3-AS1   BCAR3 antisense RNA 1                        
#>  4 HGNC:13276    13276 EI24        EI24 autophagy associated transmembrane prot…
#>  5 HGNC:3345      3345 ENC1        ectodermal-neural cortex 1                   
#>  6 HGNC:27919    27919 ERVMER61-1  endogenous retrovirus group MER61 member 1   
#>  7 HGNC:56226    56226 FAM169A-AS1 FAM169A antisense RNA 1                      
#>  8 HGNC:4136      4136 GAMT        guanidinoacetate N-methyltransferase         
#>  9 HGNC:54868    54868 KLRK1-AS1   KLRK1 antisense RNA 1                        
#> 10 HGNC:6568      6568 LGALS7      galectin 7                                   
#> # … with 56 more rows

Restrict the search to the symbol column:

hgnc_dataset %>%
  filter_by_keyword('TP53', cols = 'symbol') %>%
  dplyr::select(1:4)
#> # A tibble: 23 × 4
#>    hgnc_id    hgnc_id2 symbol    name                                           
#>    <chr>         <int> <chr>     <chr>                                          
#>  1 HGNC:11998    11998 TP53      tumor protein p53                              
#>  2 HGNC:29984    29984 TP53AIP1  tumor protein p53 regulated apoptosis inducing…
#>  3 HGNC:11999    11999 TP53BP1   tumor protein p53 binding protein 1            
#>  4 HGNC:12000    12000 TP53BP2   tumor protein p53 binding protein 2            
#>  5 HGNC:16328    16328 TP53BP2P1 tumor protein p53 binding protein 2 pseudogene…
#>  6 HGNC:43652    43652 TP53COR1  tumor protein p53 pathway corepressor 1        
#>  7 HGNC:19373    19373 TP53I3    tumor protein p53 inducible protein 3          
#>  8 HGNC:16842    16842 TP53I11   tumor protein p53 inducible protein 11         
#>  9 HGNC:25102    25102 TP53I13   tumor protein p53 inducible protein 13         
#> 10 HGNC:18022    18022 TP53INP1  tumor protein p53 inducible nuclear protein 1  
#> # … with 13 more rows

Search for the whole word "TP53" exactly by taking advantage of regular expressions:

hgnc_dataset %>%
  filter_by_keyword('^TP53$', cols = 'symbol') %>%
  dplyr::select(1:4)
#> # A tibble: 1 × 4
#>   hgnc_id    hgnc_id2 symbol name             
#>   <chr>         <int> <chr>  <chr>            
#> 1 HGNC:11998    11998 TP53   tumor protein p53

Citing the HGNC

To cite HGNC nomenclature resources use:

Tweedie S, Braschi B, Gray KA, Jones TEM, Seal RL, Yates B, Bruford EA. Genenames.org: the HGNC and VGNC resources in 2021. Nucleic Acids Res. 49, D939–D946 (2021). doi: 10.1093/nar/gkaa980

To cite data within the database use the following format:

HGNC Database, HUGO Gene Nomenclature Committee (HGNC), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom www.genenames.org.

Please include the month and year you retrieved the data cited.

Name		Name	Last commit message	Last commit date
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R		R
inst		inst
man		man
pkgdown/favicon		pkgdown/favicon
tests		tests
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
_pkgdown.yml		_pkgdown.yml
cran-comments.md		cran-comments.md
hgnc.Rproj		hgnc.Rproj

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hgnc

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Locus groups

Setting the archive version

Downloading to disk

Convert between gene identifiers

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Motivation

Citing the HGNC

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hgnc

Installation

Usage

Basic usage

Locus groups

Setting the archive version

Downloading to disk

Convert between gene identifiers

Caching

Motivation

Citing the HGNC

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