Use these functions to return a specific property of a microorganism based on the latest accepted taxonomy. All input values will be evaluated internally with as.mo(), which makes it possible to use microbial abbreviations, codes and names as input. See Examples.
Usage
mo_name(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_fullname(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_shortname(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_subspecies(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_species(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_genus(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_family(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_order(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_class(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_phylum(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_kingdom(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_domain(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_type(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_status(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_pathogenicity(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_gramstain(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_is_gram_negative(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_is_gram_positive(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_is_yeast(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_is_intrinsic_resistant(
x,
ab,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_oxygen_tolerance(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_is_anaerobic(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_snomed(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_ref(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_authors(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_year(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_lpsn(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_gbif(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_rank(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_taxonomy(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_synonyms(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_current(x, language = get_AMR_locale(), ...)
mo_group_members(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_info(
x,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_url(
x,
open = FALSE,
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)
mo_property(
x,
property = "fullname",
language = get_AMR_locale(),
keep_synonyms = getOption("AMR_keep_synonyms", FALSE),
...
)Arguments
- x
any character (vector) that can be coerced to a valid microorganism code with
as.mo(). Can be left blank for auto-guessing the column containing microorganism codes if used in a data set, see Examples.- language
language to translate text like "no growth", which defaults to the system language (see
get_AMR_locale())- keep_synonyms
a logical to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. The default is
FALSE, which will return a note if old taxonomic names were processed. The default can be set with the package optionAMR_keep_synonyms, i.e.options(AMR_keep_synonyms = TRUE)oroptions(AMR_keep_synonyms = FALSE).- ...
other arguments passed on to
as.mo(), such as 'minimum_matching_score', 'ignore_pattern', and 'remove_from_input'- ab
any (vector of) text that can be coerced to a valid antibiotic drug code with
as.ab()- open
browse the URL using
browseURL()- property
one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed", or must be
"shortname"
Value
An integer in case of
mo_year()An ordered factor in case of
mo_pathogenicity()A list in case of
mo_taxonomy(),mo_synonyms(),mo_snomed()andmo_info()A named character in case of
mo_url()A character in all other cases
Details
All functions will, at default, not keep old taxonomic properties, as synonyms are automatically replaced with the current taxonomy. Take for example Enterobacter aerogenes, which was initially named in 1960 but renamed to Klebsiella aerogenes in 2017:
mo_genus("Enterobacter aerogenes")will return"Klebsiella"(with a note about the renaming)mo_genus("Enterobacter aerogenes", keep_synonyms = TRUE)will return"Enterobacter"(with a once-per-session warning that the name is outdated)mo_ref("Enterobacter aerogenes")will return"Tindall et al., 2017"(with a note)mo_ref("Enterobacter aerogenes", keep_synonyms = TRUE)will return"Hormaeche et al., 1960"(with a warning)
The short name (mo_shortname()) returns the first character of the genus and the full species, such as "E. coli", for species and subspecies. Exceptions are abbreviations of staphylococci (such as "CoNS", Coagulase-Negative Staphylococci) and beta-haemolytic streptococci (such as "GBS", Group B Streptococci). Please bear in mind that e.g. E. coli could mean Escherichia coli (kingdom of Bacteria) as well as Entamoeba coli (kingdom of Protozoa). Returning to the full name will be done using as.mo() internally, giving priority to bacteria and human pathogens, i.e. "E. coli" will be considered Escherichia coli. As a result, mo_fullname(mo_shortname("Entamoeba coli")) returns "Escherichia coli".
Since the top-level of the taxonomy is sometimes referred to as 'kingdom' and sometimes as 'domain', the functions mo_kingdom() and mo_domain() return the exact same results.
Determination of human pathogenicity (mo_pathogenicity()) is strongly based on Bartlett et al. (2022, doi:10.1099/mic.0.001269
). This function returns a factor with the levels Pathogenic, Potentially pathogenic, Non-pathogenic, and Unknown.
Determination of the Gram stain (mo_gramstain()) will be based on the taxonomic kingdom and phylum. Originally, Cavalier-Smith defined the so-called subkingdoms Negibacteria and Posibacteria (2002, PMID 11837318), and only considered these phyla as Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, and Tenericutes. These phyla were later renamed to Actinomycetota, Chloroflexota, Bacillota, and Mycoplasmatota (2021, PMID 34694987). Bacteria in these phyla are considered Gram-positive in this AMR package, except for members of the class Negativicutes (within phylum Bacillota) which are Gram-negative. All other bacteria are considered Gram-negative. Species outside the kingdom of Bacteria will return a value NA. Functions mo_is_gram_negative() and mo_is_gram_positive() always return TRUE or FALSE (or NA when the input is NA or the MO code is UNKNOWN), thus always return FALSE for species outside the taxonomic kingdom of Bacteria.
Determination of yeasts (mo_is_yeast()) will be based on the taxonomic kingdom and class. Budding yeasts are fungi of the phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes). True yeasts are aggregated into the underlying order Saccharomycetales. Thus, for all microorganisms that are member of the taxonomic class Saccharomycetes, the function will return TRUE. It returns FALSE otherwise (or NA when the input is NA or the MO code is UNKNOWN).
Determination of intrinsic resistance (mo_is_intrinsic_resistant()) will be based on the intrinsic_resistant data set, which is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). The mo_is_intrinsic_resistant() function can be vectorised over both argument x (input for microorganisms) and ab (input for antibiotics).
Determination of bacterial oxygen tolerance (mo_oxygen_tolerance()) will be based on BacDive, see Source. The function mo_is_anaerobic() only returns TRUE if the oxygen tolerance is "anaerobe", indicting an obligate anaerobic species or genus. It always returns FALSE for species outside the taxonomic kingdom of Bacteria.
The function mo_url() will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
SNOMED codes (mo_snomed()) are from the version of July 1st, 2021. See Source and the microorganisms data set for more info.
Old taxonomic names (so-called 'synonyms') can be retrieved with mo_synonyms() (which will have the scientific reference as name), the current taxonomic name can be retrieved with mo_current(). Both functions return full names.
All output will be translated where possible.
Matching Score for Microorganisms
This function uses as.mo() internally, which uses an advanced algorithm to translate arbitrary user input to valid taxonomy using a so-called matching score. You can read about this public algorithm on the MO matching score page.
Source
Berends MS et al. (2022). AMR: An R Package for Working with Antimicrobial Resistance Data. Journal of Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03
Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13
Becker K et al. (2019). Implications of identifying the recently defined members of the S. aureus complex, S. argenteus and S. schweitzeri: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028
Becker K et al. (2020). Emergence of coagulase-negative staphylococci. Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813
Lancefield RC (1933). A serological differentiation of human and other groups of hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571
Berends MS et al. (2022). Trends in Occurrence and Phenotypic Resistance of Coagulase-Negative Staphylococci (CoNS) Found in Human Blood in the Northern Netherlands between 2013 and 2019/ Micro.rganisms 10(9), 1801; doi:10.3390/microorganisms10091801
Parte, AC et al. (2020). List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed from https://lpsn.dsmz.de on December 11th, 2022.
GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed from https://www.gbif.org on January 8th, 2024.
Reimer, LC et al. (2022). BacDive in 2022: the knowledge base for standardized bacterial and archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed from https://bacdive.dsmz.de on May 12th, 2023.
Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov
Bartlett A et al. (2022). A comprehensive list of bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269
Reference Data Publicly Available
All data sets in this AMR package (about microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit our website for the download links. The actual files are of course available on our GitHub repository.
See also
Data set microorganisms
Examples
# taxonomic tree -----------------------------------------------------------
mo_kingdom("Klebsiella pneumoniae")
#> [1] "Bacteria"
mo_phylum("Klebsiella pneumoniae")
#> [1] "Pseudomonadota"
mo_class("Klebsiella pneumoniae")
#> [1] "Gammaproteobacteria"
mo_order("Klebsiella pneumoniae")
#> [1] "Enterobacterales"
mo_family("Klebsiella pneumoniae")
#> [1] "Enterobacteriaceae"
mo_genus("Klebsiella pneumoniae")
#> [1] "Klebsiella"
mo_species("Klebsiella pneumoniae")
#> [1] "pneumoniae"
mo_subspecies("Klebsiella pneumoniae")
#> [1] ""
# full names and short names -----------------------------------------------
mo_name("Klebsiella pneumoniae")
#> [1] "Klebsiella pneumoniae"
mo_fullname("Klebsiella pneumoniae")
#> [1] "Klebsiella pneumoniae"
mo_shortname("Klebsiella pneumoniae")
#> [1] "K. pneumoniae"
# other properties ---------------------------------------------------------
mo_pathogenicity("Klebsiella pneumoniae")
#> [1] Pathogenic
#> Levels: Pathogenic < Potentially pathogenic < Non-pathogenic < Unknown
mo_gramstain("Klebsiella pneumoniae")
#> [1] "Gram-negative"
mo_snomed("Klebsiella pneumoniae")
#> [[1]]
#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009"
#> [5] "56415008" "714315002" "713926009"
#>
mo_type("Klebsiella pneumoniae")
#> [1] "Bacteria"
mo_rank("Klebsiella pneumoniae")
#> [1] "species"
mo_url("Klebsiella pneumoniae")
#> Klebsiella pneumoniae
#> "https://lpsn.dsmz.de/species/klebsiella-pneumoniae"
mo_is_yeast(c("Candida", "Trichophyton", "Klebsiella"))
#> [1] TRUE FALSE FALSE
mo_group_members("Streptococcus group A")
#> [1] "Streptococcus pyogenes"
mo_group_members(c("Streptococcus group C",
"Streptococcus group G",
"Streptococcus group L"))
#> $`Streptococcus Group C`
#> [1] "Streptococcus dysgalactiae"
#> [2] "Streptococcus dysgalactiae dysgalactiae"
#> [3] "Streptococcus dysgalactiae equisimilis"
#> [4] "Streptococcus equi"
#> [5] "Streptococcus equi equi"
#> [6] "Streptococcus equi ruminatorum"
#> [7] "Streptococcus equi zooepidemicus"
#>
#> $`Streptococcus Group G`
#> [1] "Streptococcus canis"
#> [2] "Streptococcus dysgalactiae"
#> [3] "Streptococcus dysgalactiae dysgalactiae"
#> [4] "Streptococcus dysgalactiae equisimilis"
#>
#> $`Streptococcus Group L`
#> [1] "Streptococcus dysgalactiae"
#> [2] "Streptococcus dysgalactiae dysgalactiae"
#> [3] "Streptococcus dysgalactiae equisimilis"
#>
# scientific reference -----------------------------------------------------
mo_ref("Klebsiella aerogenes")
#> [1] "Tindall et al., 2017"
mo_authors("Klebsiella aerogenes")
#> [1] "Tindall et al."
mo_year("Klebsiella aerogenes")
#> [1] 2017
mo_lpsn("Klebsiella aerogenes")
#> [1] "777146"
mo_gbif("Klebsiella aerogenes")
#> [1] "9281703"
mo_synonyms("Klebsiella aerogenes")
#> Hormaeche et al., 1960 Bascomb et al., 1971
#> "Enterobacter aerogenes" "Klebsiella mobilis"
# abbreviations known in the field -----------------------------------------
mo_genus("MRSA")
#> [1] "Staphylococcus"
mo_species("MRSA")
#> [1] "aureus"
mo_shortname("VISA")
#> [1] "S. aureus"
mo_gramstain("VISA")
#> [1] "Gram-positive"
mo_genus("EHEC")
#> [1] "Escherichia"
mo_species("EIEC")
#> [1] "coli"
mo_name("UPEC")
#> [1] "Escherichia coli"
# known subspecies ---------------------------------------------------------
mo_fullname("K. pneu rh")
#> [1] "Klebsiella pneumoniae rhinoscleromatis"
mo_shortname("K. pneu rh")
#> [1] "K. pneumoniae"
# \donttest{
# Becker classification, see ?as.mo ----------------------------------------
mo_fullname("Staph epidermidis")
#> [1] "Staphylococcus epidermidis"
mo_fullname("Staph epidermidis", Becker = TRUE)
#> [1] "Coagulase-negative Staphylococcus (CoNS)"
mo_shortname("Staph epidermidis")
#> [1] "S. epidermidis"
mo_shortname("Staph epidermidis", Becker = TRUE)
#> [1] "CoNS"
# Lancefield classification, see ?as.mo ------------------------------------
mo_fullname("Strep agalactiae")
#> [1] "Streptococcus agalactiae"
mo_fullname("Strep agalactiae", Lancefield = TRUE)
#> [1] "Streptococcus Group B"
mo_shortname("Strep agalactiae")
#> [1] "S. agalactiae"
mo_shortname("Strep agalactiae", Lancefield = TRUE)
#> [1] "GBS"
# language support --------------------------------------------------------
mo_gramstain("Klebsiella pneumoniae", language = "de") # German
#> [1] "Gramnegativ"
mo_gramstain("Klebsiella pneumoniae", language = "nl") # Dutch
#> [1] "Gram-negatief"
mo_gramstain("Klebsiella pneumoniae", language = "es") # Spanish
#> [1] "Gram negativo"
mo_gramstain("Klebsiella pneumoniae", language = "el") # Greek
#> [1] "Αρνητικό κατά Gram"
mo_gramstain("Klebsiella pneumoniae", language = "uk") # Ukrainian
#> [1] "Грамнегативні"
# mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated
mo_kingdom("Klebsiella pneumoniae")
#> [1] "Bacteria"
mo_type("Klebsiella pneumoniae")
#> [1] "Bacteria"
mo_kingdom("Klebsiella pneumoniae", language = "zh") # Chinese, no effect
#> [1] "Bacteria"
mo_type("Klebsiella pneumoniae", language = "zh") # Chinese, translated
#> [1] "细菌"
mo_fullname("S. pyogenes", Lancefield = TRUE, language = "de")
#> [1] "Streptococcus Gruppe A"
mo_fullname("S. pyogenes", Lancefield = TRUE, language = "uk")
#> [1] "Streptococcus Група A"
# other --------------------------------------------------------------------
# gram stains and intrinsic resistance can be used as a filter in dplyr verbs
if (require("dplyr")) {
example_isolates %>%
filter(mo_is_gram_positive()) %>%
count(mo_genus(), sort = TRUE)
}
#> ℹ Using column 'mo' as input for mo_is_gram_positive()
#> ℹ Using column 'mo' as input for mo_genus()
#> # A tibble: 18 × 2
#> `mo_genus()` n
#> <chr> <int>
#> 1 Staphylococcus 840
#> 2 Streptococcus 275
#> 3 Enterococcus 83
#> 4 Corynebacterium 17
#> 5 Micrococcus 6
#> 6 Gemella 3
#> 7 Aerococcus 2
#> 8 Cutibacterium 1
#> 9 Dermabacter 1
#> 10 Fusibacter 1
#> 11 Globicatella 1
#> 12 Granulicatella 1
#> 13 Lactobacillus 1
#> 14 Leuconostoc 1
#> 15 Listeria 1
#> 16 Paenibacillus 1
#> 17 Rothia 1
#> 18 Schaalia 1
if (require("dplyr")) {
example_isolates %>%
filter(mo_is_intrinsic_resistant(ab = "vanco")) %>%
count(mo_genus(), sort = TRUE)
}
#> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant()
#> ℹ Using column 'mo' as input for mo_genus()
#> # A tibble: 20 × 2
#> `mo_genus()` n
#> <chr> <int>
#> 1 Escherichia 467
#> 2 Klebsiella 77
#> 3 Proteus 39
#> 4 Pseudomonas 30
#> 5 Serratia 25
#> 6 Enterobacter 23
#> 7 Citrobacter 11
#> 8 Haemophilus 8
#> 9 Acinetobacter 6
#> 10 Morganella 6
#> 11 Pantoea 4
#> 12 Salmonella 3
#> 13 Neisseria 2
#> 14 Stenotrophomonas 2
#> 15 Campylobacter 1
#> 16 Enterococcus 1
#> 17 Hafnia 1
#> 18 Lactobacillus 1
#> 19 Leuconostoc 1
#> 20 Pseudescherichia 1
# get a list with the complete taxonomy (from kingdom to subspecies)
mo_taxonomy("Klebsiella pneumoniae")
#> $kingdom
#> [1] "Bacteria"
#>
#> $phylum
#> [1] "Pseudomonadota"
#>
#> $class
#> [1] "Gammaproteobacteria"
#>
#> $order
#> [1] "Enterobacterales"
#>
#> $family
#> [1] "Enterobacteriaceae"
#>
#> $genus
#> [1] "Klebsiella"
#>
#> $species
#> [1] "pneumoniae"
#>
#> $subspecies
#> [1] ""
#>
# get a list with the taxonomy, the authors, Gram-stain,
# SNOMED codes, and URL to the online database
mo_info("Klebsiella pneumoniae")
#> $mo
#> [1] "B_KLBSL_PNMN"
#>
#> $kingdom
#> [1] "Bacteria"
#>
#> $phylum
#> [1] "Pseudomonadota"
#>
#> $class
#> [1] "Gammaproteobacteria"
#>
#> $order
#> [1] "Enterobacterales"
#>
#> $family
#> [1] "Enterobacteriaceae"
#>
#> $genus
#> [1] "Klebsiella"
#>
#> $species
#> [1] "pneumoniae"
#>
#> $subspecies
#> [1] ""
#>
#> $status
#> [1] "accepted"
#>
#> $synonyms
#> NULL
#>
#> $gramstain
#> [1] "Gram-negative"
#>
#> $oxygen_tolerance
#> [1] "aerobe"
#>
#> $url
#> [1] "https://lpsn.dsmz.de/species/klebsiella-pneumoniae"
#>
#> $ref
#> [1] "Trevisan, 1887"
#>
#> $snomed
#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009"
#> [5] "56415008" "714315002" "713926009"
#>
#> $lpsn
#> [1] "777151"
#>
#> $gbif
#> [1] "3221874"
#>
#> $group_members
#> character(0)
#>
# }