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 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), AN (Animalia), B (Bacteria), C (Chromista),
                            F (Fungi), P (Protozoa), PL (Plantae) or V (Viruses)

Values that cannot be coered will be considered 'unknown' and have an MO code UNKNOWN.

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

Intelligent rules
This function uses intelligent rules 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 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 a data.frame 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.

Microbial prevalence of pathogens in humans
The intelligent rules 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 other 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 (~60,000 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. The Catalogue of Life releases updates annually; check which version was included in this package with catalogue_of_life_version().

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)))
# }

Arguments

Value

Details

Source

Catalogue of Life

Read more on our website!

See also

Examples

Contents