Transform to microorganism ID

Use this function to determine a valid microorganism ID (mo). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see Source). 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. Please see 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()

clear_mo_history(...)

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. (1,2). Note that this does not include species that were newly named after these publications, like S. caeli. 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 (3). 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 number between `0` (or `"none"`) and `3` (or `"all"`), or `TRUE` (= `2`) or `FALSE` (= `0`) to indicate whether the input should be checked for less probable results, please 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).
...	other parameters passed on to functions

Value

A character vector with class mo

Details

General info

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

  Code               Full name
  ---------------    --------------------------------------
  B_KLBSL            Klebsiella
  B_KLBSL_PNMN       Klebsiella pneumoniae
  B_KLBSL_PNMN_RHNS  Klebsiella pneumoniae rhinoscleromatis
  |   |    |    |
  |   |    |    |
  |   |    |     ---> subspecies, a 4-5 letter acronym
  |   |     ----> species, a 4-5 letter acronym
  |    ----> genus, a 5-7 letter acronym
   ----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),
                            C (Chromista), F (Fungi), P (Protozoa)

Values that cannot be coered will be considered 'unknown' and will get the MO code UNKNOWN.

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

The algorithm uses data from the Catalogue of Life (see below) and from one other source (see microorganisms).

The as.mo() function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order:

Human pathogenic prevalence: the function starts with more prevalent microorganisms, followed by less prevalent ones;
Taxonomic kingdom: the function starts with determining Bacteria, then Fungi, then Protozoa, then others;
Breakdown of input values to identify possible matches.

This will lead to the effect that e.g. "E. coli" (a highly prevalent microorganism found in humans) will return the microbial ID of Escherichia coli and not Entamoeba coli (a less prevalent microorganism in humans), although the latter would alphabetically come first.

Coping with uncertain results

In addition, the as.mo() function can differentiate four levels of uncertainty to guess valid results:

Uncertainty level 0: no additional rules are applied;
Uncertainty level 1: allow previously accepted (but now invalid) taxonomic names and minor spelling errors;
Uncertainty level 2: allow all of level 1, strip values between brackets, inverse the words of the input, strip off text elements from the end keeping at least two elements;
Uncertainty level 3: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name.

This leads to e.g.:

"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_GRPB) 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_AURS) 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_GNRR) needs review.

The level of uncertainty can be set using the argument allow_uncertain. The default is allow_uncertain = TRUE, which is equal to uncertainty level 2. Using allow_uncertain = FALSE is equal to uncertainty level 0 and will skip all rules. You can also use e.g. as.mo(..., allow_uncertain = 1) to only allow up to level 1 uncertainty.

There are three helper functions that can be run after then as.mo() function:

Use mo_uncertainties() to get a data.frame with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as \((n - 0.5 * L) / n\), where n is the number of characters of the returned full name of the microorganism, and L is the Levenshtein distance between that full name and the user input.
Use mo_failures() to get a vector with all values that could not be coerced to a valid value.
Use mo_renamed() to get a data.frame with all values that could be coerced based on an old, previously accepted taxonomic name.

Microbial prevalence of pathogens in humans

The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the prevalence columns in the microorganisms and microorganisms.old data sets. The grouping into prevalence groups is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence.

Group 1 (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is Enterococcus, Staphylococcus or Streptococcus. This group consequently contains all common Gram-negative bacteria, such as Pseudomonas and Legionella and all species within the order Enterobacteriales.

Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is Aspergillus, Bacteroides, Candida, Capnocytophaga, Chryseobacterium, Cryptococcus, Elisabethkingia, Flavobacterium, Fusobacterium, Giardia, Leptotrichia, Mycoplasma, Prevotella, Rhodotorula, Treponema, Trichophyton or Ureaplasma.

Group 3 (least prevalent microorganisms) consists of all other microorganisms.

Self-learning algorithm

The as.mo() function gains experience from previously determined microorganism IDs and learns from it. This drastically improves both speed and reliability. Use clear_mo_history() to reset the algorithms. Only experience from your current AMR package version is used. This is done because in the future the taxonomic tree (which is included in this package) may change for any organism and it consequently has to rebuild its knowledge.

Usually, any guess after the first try runs 80-95% faster than the first try.

This resets with every update of this AMR package since results are saved to your local package library folder.

Source

Becker K et al. Coagulase-Negative Staphylococci. 2014. Clin Microbiol Rev. 27(4): 870–926. https://dx.doi.org/10.1128/CMR.00109-13
Becker K et al. 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). 2019. Clin Microbiol Infect. https://doi.org/10.1016/j.cmi.2019.02.028
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
Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).

Stable lifecycle

The lifecycle of this function is stable. In a stable function, we are largely happy with the unlying code, and major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; we will avoid removing arguments or changing the meaning of existing arguments.

If the unlying code needs breaking changes, they will occur gradually. To begin with, the function or argument will be deprecated; it will continue to work but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.

Catalogue of Life

This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.

Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().

Examples

# \donttest{
# These examples all return "B_STPHY_AURS", the ID of S. aureus:
as.mo("sau") # WHONET code
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("Zthafilokkoockus oureuz") # handles incorrect spelling
as.mo("MRSA")    # Methicillin Resistant S. aureus
as.mo("VISA")    # Vancomycin Intermediate S. aureus
as.mo("VRSA")    # Vancomycin Resistant S. aureus
as.mo(22242419)  # Catalogue of Life ID
as.mo(115329001) # SNOMED CT code

# Dyslexia is no problem - these all work:
as.mo("Ureaplasma urealyticum")
as.mo("Ureaplasma urealyticus")
as.mo("Ureaplasmium urealytica")
as.mo("Ureaplazma urealitycium")

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

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

# All mo_* functions use as.mo() internally too (see ?mo_property):
mo_genus("E. coli")           # returns "Escherichia"
mo_gramstain("E. coli")       # returns "Gram negative"

# }
if (FALSE) {
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)))
}