Use this function to determine a valid microorganism ID (mo). Determination is done using Artificial Intelligence (AI) and the complete taxonomic kingdoms Bacteria, Fungi and Protozoa (see Source), so the input can be almost anything: a full name (like "Staphylococcus aureus"), an abbreviated name (like "S. aureus"), an abbreviation known in the field (like "MRSA"), or just a genus. You could also select a genus and species column, zie Examples.

as.mo(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = TRUE,
  reference_df = get_mo_source())

is.mo(x)

mo_failures()

mo_uncertainties()

mo_renamed()

Arguments

x

a character vector or a data.frame with one or two columns

Becker

a logical to indicate whether Staphylococci should be categorised into Coagulase Negative Staphylococci ("CoNS") and Coagulase Positive Staphylococci ("CoPS") instead of their own species, according to Karsten Becker et al. [1].

This excludes Staphylococcus aureus at default, use Becker = "all" to also categorise S. aureus as "CoPS".

Lancefield

a logical to indicate whether beta-haemolytic Streptococci should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [2]. These Streptococci will be categorised in their first group, e.g. Streptococcus dysgalactiae will be group C, although officially it was also categorised into groups G and L.

This excludes Enterococci at default (who are in group D), use Lancefield = "all" to also categorise all Enterococci as group D.

allow_uncertain

a logical to indicate whether the input should be checked for less possible results, see Details

reference_df

a data.frame to use for extra reference when translating x to a valid mo. See set_mo_source and get_mo_source to automate the usage of your own codes (e.g. used in your analysis or organisation).

Value

Character (vector) with class "mo". Unknown values will return NA.

Details

A microbial ID from this package (class: mo) typically looks like these examples:

  Code              Full name
  ---------------   --------------------------------------
  B_KLBSL           Klebsiella
  B_KLBSL_PNE       Klebsiella pneumoniae
  B_KLBSL_PNE_RHI   Klebsiella pneumoniae rhinoscleromatis
  |   |    |   |
  |   |    |   |
  |   |    |    ----> subspecies, a 3-4 letter acronym
  |   |     ----> species, a 3-4 letter acronym
  |    ----> genus, a 5-7 letter acronym, mostly without vowels
   ----> taxonomic kingdom: A (Archaea), B (Bacteria), C (Chromista),
                            F (Fungi), P (Protozoa) or V (Viruses)

Use the mo_property functions to get properties based on the returned code, see Examples.

This function uses Artificial Intelligence (AI) to help getting fast and logical results. It tries to find matches in this order:

  • Taxonomic kingdom: it first searches in Bacteria, then Fungi, then Protozoa

  • Human pathogenic prevalence: it first searches in more prevalent microorganisms, then less prevalent ones

  • Valid MO codes and full names: it first searches in already valid MO code and known genus/species combinations

  • Breakdown of input values: from here it starts to breakdown input values to find possible matches

A couple of effects because of these rules:

  • "E. coli" will return the ID of Escherichia coli and not Entamoeba coli, although the latter would alphabetically come first

  • "H. influenzae" will return the ID of Haemophilus influenzae and not Haematobacter influenzae for the same reason

  • Something like "p aer" will return the ID of Pseudomonas aeruginosa and not Pasteurella aerogenes

  • Something like "stau" or "S aur" will return the ID of Staphylococcus aureus and not Staphylococcus auricularis

This means that looking up human pathogenic microorganisms takes less time than looking up human non-pathogenic microorganisms.

UNCERTAIN RESULTS
When using allow_uncertain = TRUE (which is the default setting), it will use additional rules if all previous AI rules failed to get valid results. These are:

  • It tries to look for previously accepted (but now invalid) taxonomic names

  • It strips off values between brackets and the brackets itself, and re-evaluates the input with all previous rules

  • It strips off words from the end one by one and re-evaluates the input with all previous rules

  • It strips off words from the start one by one and re-evaluates the input with all previous rules

  • It tries to look for some manual changes which are not yet published to the Catalogue of Life (like Propionibacterium not yet being Cutibacterium)

Examples:

  • "Streptococcus group B (known as S. agalactiae)". The text between brackets will be removed and a warning will be thrown that the result Streptococcus group B (B_STRPT_GRB) needs review.

  • "S. aureus - please mind: MRSA". The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result Staphylococcus aureus (B_STPHY_AUR) needs review.

  • "D. spartina". This is the abbreviation of an old taxonomic name: Didymosphaeria spartinae (the last "e" was missing from the input). This fungus was renamed to Leptosphaeria obiones, so a warning will be thrown that this result (F_LPTSP_OBI) needs review.

  • "Fluoroquinolone-resistant Neisseria gonorrhoeae". The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result Neisseria gonorrhoeae (B_NESSR_GON) needs review.

Use mo_failures() to get a vector with all values that could not be coerced to a valid value.

Use mo_uncertainties() to get a vector with all values that were coerced to a valid value, but with uncertainty.

Use mo_renamed() to get a vector with all values that could be coerced based on an old, previously accepted taxonomic name.

Source

[1] Becker K et al. Coagulase-Negative Staphylococci. 2014. Clin Microbiol Rev. 27(4): 870–926. https://dx.doi.org/10.1128/CMR.00109-13

[2] Lancefield RC A serological differentiation of human and other groups of hemolytic streptococci. 1933. J Exp Med. 57(4): 571–95. https://dx.doi.org/10.1084/jem.57.4.571

[3] Catalogue of Life: Annual Checklist (public online database), www.catalogueoflife.org.

Catalogue of Life


This package contains the complete taxonomic tree of almost all microorganisms from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). This data is updated annually - check the included version with catalogue_of_life_version.

Included are:

  • All ~55,000 (sub)species from the kingdoms of Archaea, Bacteria, Protozoa and Viruses

  • All ~3,000 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales and Schizosaccharomycetales. The kingdom of Fungi is a very large taxon with almost 300,000 different species, of which most are not microbial. Including everything tremendously slows down our algortihms, and not all fungi fit the scope of this package. By only including the aforementioned taxonomic orders, the most relevant species are covered (like genera Aspergillus, Candida, Pneumocystis, Saccharomyces and Trichophyton).

  • All ~15,000 previously accepted names of inckuded (sub)species that have been taxonomically renamed

  • The complete taxonomic tree of all included (sub)species: from kingdom to subspecies

  • The responsible author(s) and year of scientific publication

The Catalogue of Life (http://www.catalogueoflife.org) is the most comprehensive and authoritative global index of species currently available. It holds essential information on the names, relationships and distributions of over 1.6 million species. The Catalogue of Life is used to support the major biodiversity and conservation information services such as the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL) and the International Union for Conservation of Nature Red List. It is recognised by the Convention on Biological Diversity as a significant component of the Global Taxonomy Initiative and a contribution to Target 1 of the Global Strategy for Plant Conservation.

The syntax used to transform the original data to a cleansed R format, can be found here: https://gitlab.com/msberends/AMR/blob/master/reproduction_of_microorganisms.R.

Read more on our website!


On our website https://msberends.gitlab.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.

See also

microorganisms for the data.frame that is being used to determine ID's.
The mo_property functions (like mo_genus, mo_gramstain) to get properties based on the returned code.

Examples

# NOT RUN {
# These examples all return "B_STPHY_AUR", the ID of S. aureus:
as.mo("stau")
as.mo("STAU")
as.mo("staaur")
as.mo("S. aureus")
as.mo("S aureus")
as.mo("Staphylococcus aureus")
as.mo("Staphylococcus aureus (MRSA)")
as.mo("MRSA") # Methicillin Resistant S. aureus
as.mo("VISA") # Vancomycin Intermediate S. aureus
as.mo("VRSA") # Vancomycin Resistant S. aureus

as.mo("Streptococcus group A")
as.mo("GAS") # Group A Streptococci
as.mo("GBS") # Group B Streptococci

as.mo("S. epidermidis")                 # will remain species: B_STPHY_EPI
as.mo("S. epidermidis", Becker = TRUE)  # will not remain species: B_STPHY_CNS

as.mo("S. pyogenes")                    # will remain species: B_STRPT_PYO
as.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRA

# Use mo_* functions to get a specific property based on `mo`
Ecoli <- as.mo("E. coli")     # returns `B_ESCHR_COL`
mo_genus(Ecoli)               # returns "Escherichia"
mo_gramstain(Ecoli)           # returns "Gram negative"
# but it uses as.mo internally too, so you could also just use:
mo_genus("E. coli")           # returns "Escherichia"


# }# NOT RUN {
df$mo <- as.mo(df$microorganism_name)

# the select function of tidyverse is also supported:
library(dplyr)
df$mo <- df %>%
  select(microorganism_name) %>%
  as.mo()

# and can even contain 2 columns, which is convenient for genus/species combinations:
df$mo <- df %>%
  select(genus, species) %>%
  as.mo()
# although this works easier and does the same:
df <- df %>%
  mutate(mo = as.mo(paste(genus, species)))
# }