Transform to microorganism ID

Use this function to determine a valid microorganism ID (mo). Determination is done using Artificial Intelligence (AI) and the complete taxonomic kingdoms Archaea, Bacteria, Protozoa, Viruses and most microbial species from the kingdom Fungi (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 (see section Microbial prevalence of pathogens in humans)
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 "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 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.
"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 info about 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.

Microbial prevalence of pathogens in humans

The artificial intelligence takes into account microbial prevalence of pathogens in humans. It uses three groups and every (sub)species is in the group it matches first. These groups are:

1 (most prevalent): class is Gammaproteobacteria or genus is one of: Enterococcus, Staphylococcus, Streptococcus.
2: phylum is one of: Proteobacteria, Firmicutes, Actinobacteria, Sarcomastigophora or genus is one of: Aspergillus, Bacteroides, Candida, Capnocytophaga, Chryseobacterium, Cryptococcus, Elisabethkingia, Flavobacterium, Fusobacterium, Giardia, Leptotrichia, Mycoplasma, Prevotella, Rhodotorula, Treponema, Trichophyton, Ureaplasma.
3 (least prevalent): all others.

Group 1 contains all common Gram negatives, like all Enterobacteriaceae and e.g. Pseudomonas and Legionella.

Group 2 probably contains all microbial pathogens ever found in humans.

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 taxonomic database), www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).

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 ~3,500 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales and Tremellales. This covers the most relevant microbial fungi (like all species of Aspergillus, Candida, Cryptococcus, Histplasma, Pneumocystis, Saccharomyces and Trichophyton).
All ~15,000 previously accepted names of included (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.

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)))
# }