mirror of https://github.com/msberends/AMR.git
310 lines
14 KiB
R
310 lines
14 KiB
R
# Catalogue of Life
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# Data retrieved from Encyclopaedia of Life:
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# https://opendata.eol.org/dataset/catalogue-of-life/
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# unzip and extract taxon.tab (around 1.5 GB), then:
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taxon <- data.table::fread("Downloads/taxon.tab")
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# result is over 3.7M rows:
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library(dplyr)
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library(AMR)
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taxon %>% freq(kingdom)
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# Item Count Percent Cum. Count Cum. Percent
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# --- ---------- ---------- -------- ----------- -------------
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# 1 Animalia 2,225,627 59.1% 2,225,627 59.1%
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# 2 Plantae 1,177,412 31.3% 3,403,039 90.4%
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# 3 Fungi 290,145 7.7% 3,693,184 98.1%
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# 4 Chromista 47,126 1.3% 3,740,310 99.3%
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# 5 Bacteria 14,478 0.4% 3,754,788 99.7%
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# 6 Protozoa 6,060 0.2% 3,760,848 99.9%
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# 7 Viruses 3,827 0.1% 3,764,675 100.0%
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# 8 Archaea 610 0.0% 3,765,285 100.0%
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MOs <- taxon %>%
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# tibble for future transformations
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as_tibble() %>%
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filter(
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(
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# we only want all microorganisms and viruses
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!kingdom %in% c("Animalia", "Plantae")
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# and no entries above genus - they all already have a taxonomic tree
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& !taxonRank %in% c("kingdom", "phylum", "superfamily", "class", "order", "family")
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# not all fungi: Aspergillus, Candida, Trichphyton and Pneumocystis are the most important,
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# so only keep these orders from the fungi:
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& !(kingdom == "Fungi"
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& !order %in% c("Eurotiales", "Saccharomycetales", "Schizosaccharomycetales", "Tremellales", "Onygenales", "Pneumocystales"))
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)
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# or the genus has to be one of the genera we found in our hospitals last decades
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| genus %in% c("Absidia", "Acremonium", "Actinotignum", "Alternaria", "Anaerosalibacter", "Ancylostoma", "Anisakis", "Apophysomyces",
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"Arachnia", "Ascaris", "Aureobacterium", "Aureobasidium", "Balantidum", "Bilophilia", "Branhamella", "Brochontrix",
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"Brugia", "Calymmatobacterium", "Catabacter", "Cdc", "Chilomastix", "Chryseomonas", "Cladophialophora", "Cladosporium",
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"Clonorchis", "Cordylobia", "Curvularia", "Demodex", "Dermatobia", "Diphyllobothrium", "Dracunculus", "Echinococcus",
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"Enterobius", "Euascomycetes", "Exophiala", "Fasciola", "Fusarium", "Hendersonula", "Hymenolepis", "Kloeckera",
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"Koserella", "Larva", "Leishmania", "Lelliottia", "Loa", "Lumbricus", "Malassezia", "Metagonimus", "Molonomonas",
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"Mucor", "Nattrassia", "Necator", "Novospingobium", "Onchocerca", "Opistorchis", "Paragonimus", "Paramyxovirus",
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"Pediculus", "Phoma", "Phthirus", "Pityrosporum", "Pseudallescheria", "Pulex", "Rhizomucor", "Rhizopus", "Rhodotorula",
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"Salinococcus", "Sanguibacteroides", "Schistosoma", "Scopulariopsis", "Scytalidium", "Sporobolomyces", "Stomatococcus",
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"Strongyloides", "Syncephalastraceae", "Taenia", "Torulopsis", "Trichinella", "Trichobilharzia", "Trichomonas",
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"Trichosporon", "Trichuris", "Trypanosoma", "Wuchereria")
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) %>%
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# remove text if it contains 'Not assigned' like phylum in viruses
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mutate_all(funs(gsub("Not assigned", "", .))) %>%
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# Transform 'Smith, Jones, 2011' to 'Smith et al., 2011':
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mutate(authors2 = iconv(scientificNameAuthorship, from = "UTF-8", to = "ASCII//TRANSLIT"),
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# remove leading and trailing brackets
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authors2 = gsub("^[(](.*)[)]$", "\\1", authors2),
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# only take part after brackets if there's a name
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authors2 = ifelse(grepl(".*[)] [a-zA-Z]+.*", authors2),
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gsub(".*[)] (.*)", "\\1", authors2),
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authors2),
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# get year from last 4 digits
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lastyear = as.integer(gsub(".*([0-9]{4})$", "\\1", authors2)),
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# can never be later than now
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lastyear = ifelse(lastyear > as.integer(format(Sys.Date(), "%Y")),
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NA,
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lastyear),
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# get authors without last year
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authors = gsub("(.*)[0-9]{4}$", "\\1", authors2),
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# remove nonsense characters from names
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authors = gsub("[^a-zA-Z,'& -]", "", authors),
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# remove trailing and leading spaces
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authors = trimws(authors),
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# only keep first author and replace all others by 'et al'
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authors = gsub("(,| and| &| ex| emend\\.?) .*", " et al.", authors),
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# et al. always with ending dot
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authors = gsub(" et al\\.?", " et al.", authors),
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authors = gsub(" ?,$", "", authors),
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# don't start with 'sensu' or 'ehrenb'
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authors = gsub("^(sensu|Ehrenb.?) ", "", authors, ignore.case = TRUE),
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# no initials, only surname
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authors = gsub("^([A-Z]+ )+", "", authors, ignore.case = FALSE),
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# combine author and year if year is available
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ref = ifelse(!is.na(lastyear),
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paste0(authors, ", ", lastyear),
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authors),
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# fix beginning and ending
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ref = gsub(", $", "", ref),
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ref = gsub("^, ", "", ref)
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)
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# remove non-ASCII characters (not allowed by CRAN)
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MOs <- MOs %>%
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lapply(iconv, from = "UTF-8", to = "ASCII//TRANSLIT") %>%
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as_tibble(stringsAsFactors = FALSE)
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# split old taxonomic names - they refer to a new `taxonID` with `acceptedNameUsageID`
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MOs.old <- MOs %>%
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filter(!is.na(acceptedNameUsageID),
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ref != "") %>%
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transmute(col_id = taxonID,
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col_id_new = acceptedNameUsageID,
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fullname =
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trimws(
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gsub("(.*)[(].*", "\\1",
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stringr::str_replace(
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string = scientificName,
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pattern = stringr::fixed(ref),
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replacement = ""))),
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ref = ref) %>%
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filter(!is.na(fullname)) %>%
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distinct(fullname, .keep_all = TRUE) %>%
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arrange(col_id)
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MOs <- MOs %>%
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filter(is.na(acceptedNameUsageID)) %>%
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transmute(col_id = taxonID,
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fullname = trimws(ifelse(kingdom == "Viruses",
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paste(specificEpithet, infraspecificEpithet),
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paste(genus, specificEpithet, infraspecificEpithet))),
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kingdom,
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phylum,
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class,
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order,
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family,
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genus = gsub(":", "", genus),
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species = specificEpithet,
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subspecies = infraspecificEpithet,
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rank = taxonRank,
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ref = ref,
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species_id = gsub(".*/([a-f0-9]+)", "\\1", furtherInformationURL)) %>%
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distinct(fullname, .keep_all = TRUE) %>%
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filter(!grepl("unassigned", fullname, ignore.case = TRUE))
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# only old names of species that are in MOs:
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MOs.old <- MOs.old %>% filter(col_id_new %in% MOs$col_id)
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# add abbreviations so we can easily know which ones are which ones
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MOs <- MOs %>%
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group_by(kingdom) %>%
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# abbreviations may be same for genera between kingdoms,
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# because each abbreviation starts with the the first character of the kingdom
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mutate(abbr_genus = abbreviate(genus,
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minlength = 5,
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use.classes = TRUE,
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method = "both.sides",
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strict = FALSE)) %>%
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ungroup() %>%
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group_by(genus) %>%
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# species abbreviations may be the same between genera
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# because the genus abbreviation is part of the abbreviation
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mutate(abbr_species = abbreviate(species,
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minlength = 3,
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use.classes = FALSE,
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method = "both.sides")) %>%
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ungroup() %>%
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group_by(genus, species) %>%
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mutate(abbr_subspecies = abbreviate(subspecies,
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minlength = 3,
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use.classes = FALSE,
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method = "both.sides")) %>%
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ungroup() %>%
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# remove trailing underscores
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mutate(mo = gsub("_+$", "",
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toupper(paste(ifelse(kingdom == "Animalia",
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"AN",
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ifelse(kingdom == "Plantae",
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"PL",
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substr(kingdom, 1, 1))),
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abbr_genus,
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abbr_species,
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abbr_subspecies,
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sep = "_")))) %>%
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mutate(mo = ifelse(duplicated(.$mo),
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paste0(mo, "1"),
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mo),
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fullname = ifelse(fullname == "",
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trimws(paste(genus, species, subspecies)),
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fullname)) %>%
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select(mo, everything(), -abbr_genus, -abbr_species, -abbr_subspecies)
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# add non-taxonomic entries
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MOs <- MOs %>%
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bind_rows(
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# CoNS
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MOs %>%
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filter(genus == "Staphylococcus", species == "epidermidis") %>% .[1,] %>%
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mutate(mo = gsub("EPI", "CNS", mo),
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col_id = NA_integer_,
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species = "coagulase negative",
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fullname = "Coagulase Negative Staphylococcus (CoNS)",
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ref = NA_character_),
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# CoPS
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MOs %>%
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filter(genus == "Staphylococcus", species == "epidermidis") %>% .[1,] %>%
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mutate(mo = gsub("EPI", "CPS", mo),
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col_id = NA_integer_,
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species = "coagulase positive",
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fullname = "Coagulase Positive Staphylococcus (CoPS)",
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ref = NA_character_),
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# Streptococci groups A, B, C, F, H, K
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MOs %>%
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filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
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mutate(mo = gsub("AGA", "GRA", mo),
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col_id = NA_integer_,
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species = "group A" ,
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fullname = "Streptococcus group A"),
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MOs %>%
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filter(genus == "Streptococcus", species == "dysgalactiae") %>% .[1,] %>%
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mutate(mo = gsub("DYS", "GRB", mo),
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col_id = NA_integer_,
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species = "group B" ,
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fullname = "Streptococcus group B"),
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MOs %>%
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filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
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mutate(mo = gsub("AGA", "GRC", mo),
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col_id = NA_integer_,
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species = "group C" ,
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fullname = "Streptococcus group C",
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ref = NA_character_),
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MOs %>%
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filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
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mutate(mo = gsub("AGA", "GRD", mo),
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col_id = NA_integer_,
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species = "group D" ,
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fullname = "Streptococcus group D",
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ref = NA_character_),
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MOs %>%
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filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
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mutate(mo = gsub("AGA", "GRF", mo),
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col_id = NA_integer_,
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species = "group F" ,
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fullname = "Streptococcus group F",
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ref = NA_character_),
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MOs %>%
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filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
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mutate(mo = gsub("AGA", "GRG", mo),
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col_id = NA_integer_,
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species = "group F" ,
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fullname = "Streptococcus group G",
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ref = NA_character_),
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MOs %>%
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filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
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mutate(mo = gsub("AGA", "GRH", mo),
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col_id = NA_integer_,
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species = "group H" ,
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fullname = "Streptococcus group H",
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ref = NA_character_),
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MOs %>%
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filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
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mutate(mo = gsub("AGA", "GRK", mo),
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col_id = NA_integer_,
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species = "group K" ,
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fullname = "Streptococcus group K",
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ref = NA_character_),
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# Beta haemolytic Streptococci
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MOs %>%
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filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
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mutate(mo = gsub("AGA", "HAE", mo),
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col_id = NA_integer_,
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species = "beta-haemolytic" ,
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fullname = "Beta-haemolytic Streptococcus",
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ref = NA_character_),
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# unknowns
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data.frame(mo = "B_GRAMN",
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col_id = NA_integer_,
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fullname = "(unknown Gram negatives)",
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kingdom = "Bacteria",
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phylum = NA_character_,
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class = NA_character_,
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order = NA_character_,
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family = NA_character_,
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genus = "(unknown Gram negatives)",
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species = NA_character_,
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subspecies = NA_character_,
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rank = "species",
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ref = NA_character_,
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stringsAsFactors = FALSE),
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data.frame(mo = "B_GRAMP",
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col_id = NA_integer_,
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fullname = "(unknown Gram positives)",
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kingdom = "Bacteria",
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phylum = NA_character_,
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class = NA_character_,
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order = NA_character_,
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family = NA_character_,
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genus = "(unknown Gram positives)",
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species = NA_character_,
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subspecies = NA_character_,
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rank = "species",
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ref = NA_character_,
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stringsAsFactors = FALSE)
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)
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# everything distinct?
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sum(duplicated(MOs$mo))
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# save it
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MOs <- as.data.frame(MOs %>% arrange(mo), stringsAsFactors = FALSE)
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MOs.old <- as.data.frame(MOs.old, stringsAsFactors = FALSE)
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class(MOs$mo) <- "mo"
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saveRDS(MOs, "microorganisms.rds")
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saveRDS(MOs.old, "microorganisms.old.rds")
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# on the server:
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usethis::use_data(microorganisms, overwrite = TRUE)
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usethis::use_data(microorganisms.old, overwrite = TRUE)
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rm(microorganisms)
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rm(microorganisms.old)
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