taxize is a taxonomic toolbelt for R.
taxize wraps APIs for a large suite of taxonomic databases
availab on the web.
First, install and load taxize into the R session.
install.packages("taxize")Advanced users can also download and install the latest development copy from GitHub (https://github.com/ropensci/taxize)
This is a common task in biology. We often have a list of species names and we want to know a) if we have the most up to date names, b) if our names are spelled correctly, and c) the scientific name for a common name. One way to resolve names is via the Global Names Resolver (GNR) service provided by the Encyclopedia of Life. Here, we are searching for two misspelled names:
temp <- gnr_resolve(c("Helianthos annus", "Homo saapiens"))
head(temp)
#> # A tibble: 6 x 5
#> user_supplied_name submitted_name matched_name data_source_title score
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 Helianthos annus Helianthos ann… Helianthus annus uBio NameBank 0.75
#> 2 Helianthos annus Helianthos ann… Helianthus annu… Catalogue of Life 0.75
#> 3 Helianthos annus Helianthos ann… Helianthus annu… ITIS 0.75
#> 4 Helianthos annus Helianthos ann… Helianthus annu… NCBI 0.75
#> 5 Helianthos annus Helianthos ann… Helianthus annu… GRIN Taxonomy for P… 0.75
#> 6 Helianthos annus Helianthos ann… Helianthus annu… Union 4 0.75The correct spellings are Helianthus annuus and Homo sapiens.
taxize takes the approach that the user should be able to make decisions about what resource to trust, rather than making the decision. The GNR service provides data from a variety of data sources. The user may trust a specific data source, thus may want to use the names from that data source. In the future, we may provide the ability for taxize to suggest the best match from a variety of sources.
Another common use case is when there are many synonyms for a species. In this example, we have three synonyms of the currently accepted name for a species.
mynames <- c("Helianthus annuus ssp. jaegeri", "Helianthus annuus ssp. lenticularis", "Helianthus annuus ssp. texanus")
(tsn <- get_tsn(mynames, accepted = FALSE))
══ 3 queries ═══════════════
✔ Found: Helianthus annuus ssp. jaegeri
✔ Found: Helianthus annuus ssp. lenticularis
✔ Found: Helianthus annuus ssp. texanus
══ Results ═════════════════
● Total: 3
● Found: 3
● Not Found: 0
[1] "525928" "525929" "525930"
attr(,"class")
[1] "tsn"
attr(,"match")
[1] "found" "found" "found"
attr(,"multiple_matches")
[1] FALSE FALSE FALSE
attr(,"pattern_match")
[1] FALSE FALSE FALSE
attr(,"uri")
[1] "https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=525928"
[2] "https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=525929"
[3] "https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=525930"
lapply(tsn, itis_acceptname)
[[1]]
submittedtsn acceptedname acceptedtsn author
1 525928 Helianthus annuus 36616 L.
[[2]]
submittedtsn acceptedname acceptedtsn author
1 525929 Helianthus annuus 36616 L.
[[3]]
submittedtsn acceptedname acceptedtsn author
1 525930 Helianthus annuus 36616 L.Another task biologists often face is getting higher taxonomic names for a taxa list. Having the higher taxonomy allows you to put into context the relationships of your species list. For example, you may find out that species A and species B are in Family C, which may lead to some interesting insight, as opposed to not knowing that Species A and B are closely related. This also makes it easy to aggregate/standardize data to a specific taxonomic level (e.g., family level) or to match data to other databases with different taxonomic resolution (e.g., trait databases).
A number of data sources in taxize provide the capability to retrieve higher taxonomic names, but we will highlight two of the more useful ones: Integrated Taxonomic Information System (ITIS) and National Center for Biotechnology Information (NCBI). First, we’ll search for two species, Abies procera} and Pinus contorta* within ITIS.
specieslist <- c("Abies procera","Pinus contorta")
classification(specieslist, db = 'itis')
#> ══ 2 queries ═══════════════
#> ✔ Found: Abies procera
#> ✔ Found: Pinus contorta
#> ══ Results ═════════════════
#>
#> ● Total: 2
#> ● Found: 2
#> ● Not Found: 0
#> $`Abies procera`
#> name rank id
#> 1 Plantae kingdom 202422
#> 2 Viridiplantae subkingdom 954898
#> 3 Streptophyta infrakingdom 846494
#> 4 Embryophyta superdivision 954900
#> 5 Tracheophyta division 846496
#> 6 Spermatophytina subdivision 846504
#> 7 Pinopsida class 500009
#> 8 Pinidae subclass 954916
#> 9 Pinales order 500028
#> 10 Pinaceae family 18030
#> 11 Abies genus 18031
#> 12 Abies procera species 181835
#>
#> $`Pinus contorta`
#> name rank id
#> 1 Plantae kingdom 202422
#> 2 Viridiplantae subkingdom 954898
#> 3 Streptophyta infrakingdom 846494
#> 4 Embryophyta superdivision 954900
#> 5 Tracheophyta division 846496
#> 6 Spermatophytina subdivision 846504
#> 7 Pinopsida class 500009
#> 8 Pinidae subclass 954916
#> 9 Pinales order 500028
#> 10 Pinaceae family 18030
#> 11 Pinus genus 18035
#> 12 Pinus contorta species 183327
#>
#> attr(,"class")
#> [1] "classification"
#> attr(,"db")
#> [1] "itis"It turns out both species are in the family Pinaceae. You can also
get this type of information from the NCBI by doing
classification(specieslist, db = 'ncbi').
Instead of a full classification, you may only want a single name,
say a family name for your species of interest. The function
tax_name is built just for this purpose. As with the
classification function you can specify the data source
with the db argument, either ITIS or NCBI.
tax_name("Helianthus annuus", get = "family", db = "ncbi")
#> ══ 1 queries ═══════════════
#> ✔ Found: Helianthus+annuus
#> ══ Results ═════════════════
#>
#> ● Total: 1
#> ● Found: 1
#> ● Not Found: 0
#> db query family
#> 1 ncbi Helianthus annuus AsteraceaeIt may happen that a data source does not provide information on the queried species, than one could take the result from another source and union the results from the different sources.
As mentioned most databases use a numeric code to reference a species. A general workflow in taxize is: Retrieve Code for the queried species and then use this code to query more data/information.
Below are a few examples. When you run these examples in R, you are presented with a command prompt asking for the row that contains the name you would like back; that output is not printed below for brevity. In this example, the search term has many matches. The function returns a data frame of the matches, and asks for the user to input what row number to accept.
get_uid("Pinus")
#> ══ 1 queries ═══════════════
#> ✔ Found: Pinus
#> ══ Results ═════════════════
#>
#> ● Total: 1
#> ● Found: 1
#> ● Not Found: 0
#> [1] "3337"
#> attr(,"class")
#> [1] "uid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] FALSE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "https://www.ncbi.nlm.nih.gov/taxonomy/3337"In another example, you can pass in a long character vector of taxonomic names (although this one is rather short for demo purposes):
splist <- c("annona cherimola", 'annona muricata', "quercus robur")
get_tsn(splist, searchtype = "scientific")
#> ══ 3 queries ═══════════════
#> ✔ Found: annona cherimola
#> ✔ Found: annona muricata
#> ✔ Found: quercus robur
#> ══ Results ═════════════════
#>
#> ● Total: 3
#> ● Found: 3
#> ● Not Found: 0
#> [1] "506198" "18098" "19405"
#> attr(,"class")
#> [1] "tsn"
#> attr(,"match")
#> [1] "found" "found" "found"
#> attr(,"multiple_matches")
#> [1] FALSE FALSE TRUE
#> attr(,"pattern_match")
#> [1] FALSE FALSE TRUE
#> attr(,"uri")
#> [1] "https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=506198"
#> [2] "https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=18098"
#> [3] "https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=19405"There are functions for many other sources
Sometimes with these functions you get a lot of data back. In these
cases you may want to limit your choices. Soon we will incorporate the
ability to filter using regex to limit matches, but for
now, we have a new parameter, rows, which lets you select
certain rows. For example, you can select the first row of each given
name, which means there is no interactive component:
get_nbnid(c("Zootoca vivipara","Pinus contorta"), rows = 1)
#> ══ 2 queries ═══════════════
#> ✔ Found: Zootoca vivipara
#> ✔ Found: Pinus contorta
#> ══ Results ═════════════════
#>
#> ● Total: 2
#> ● Found: 2
#> ● Not Found: 0
#> [1] "NHMSYS0001706186" "NBNSYS0000004786"
#> attr(,"class")
#> [1] "nbnid"
#> attr(,"match")
#> [1] "found" "found"
#> attr(,"multiple_matches")
#> [1] TRUE TRUE
#> attr(,"pattern_match")
#> [1] FALSE FALSE
#> attr(,"uri")
#> [1] "https://species.nbnatlas.org/species/NHMSYS0001706186"
#> [2] "https://species.nbnatlas.org/species/NBNSYS0000004786"Or you can select a range of rows
get_nbnid(c("Zootoca vivipara","Pinus contorta"), rows = 1:3)
#> ══ 2 queries ═══════════════
#> ✔ Found: Zootoca vivipara
#> ✔ Found: Pinus contorta
#> ══ Results ═════════════════
#>
#> ● Total: 2
#> ● Found: 2
#> ● Not Found: 0
#> [1] "NHMSYS0001706186" "NBNSYS0000004786"
#> attr(,"class")
#> [1] "nbnid"
#> attr(,"match")
#> [1] "found" "found"
#> attr(,"multiple_matches")
#> [1] TRUE TRUE
#> attr(,"pattern_match")
#> [1] TRUE TRUE
#> attr(,"uri")
#> [1] "https://species.nbnatlas.org/species/NHMSYS0001706186"
#> [2] "https://species.nbnatlas.org/species/NBNSYS0000004786"In addition, in case you don’t want to do interactive name selection
in the case where there are a lot of names, you can get all data back
with functions of the form, e.g., get_tsn_(), and likewise
for other data sources. For example:
out <- get_nbnid_("Poa annua")
NROW(out$`Poa annua`)
#> [1] 25That’s a lot of data, so we can get only certain rows back
get_nbnid_("Poa annua", rows = 1:10)
#> $`Poa annua`
#> guid scientificName rank taxonomicStatus
#> 1 NBNSYS0000002544 Poa annua species accepted
#> 2 NBNSYS0200001901 Bellis annua species accepted
#> 3 NBNSYS0200003392 Triumfetta annua species accepted
#> 4 NBNSYS0200002555 Lonas annua species accepted
#> 5 NHMSYS0000456951 Carrichtera annua species accepted
#> 6 NHMSYS0000461807 Poa labillardierei species accepted
#> 7 NHMSYS0000461808 Poa ligularis species accepted
#> 8 NHMSYS0000461817 Poa sieberiana species accepted
#> 9 NHMSYS0000461805 Poa gunnii species accepted
#> 10 NHMSYS0000461801 Poa costiniana species acceptedWe’ve also introduced in v0.5 the ability to coerce
numerics and alphanumerics to taxonomic ID classes that are usually only
retrieved via get_*() functions.
For example, adfafd
as.gbifid(get_gbifid("Poa annua")) # already a uid, returns the same
#> ══ 1 queries ═══════════════
#> gbifid scientificname rank status matchtype
#> 1 2704179 Poa annua L. species ACCEPTED EXACT
#> 2 8422205 Poa annua Cham. & Schltdl. species SYNONYM EXACT
#> 3 7730008 Poa annua Steud. species DOUBTFUL EXACT
#> ✖ Not Found: Poa annua
#> ══ Results ═════════════════
#>
#> ● Total: 1
#> ● Found: 0
#> ● Not Found: 1
#> [1] NA
#> attr(,"class")
#> [1] "gbifid"
#> attr(,"match")
#> [1] "not found"
#> attr(,"multiple_matches")
#> [1] TRUE
#> attr(,"pattern_match")
#> [1] FALSE
as.gbifid(2704179) # numeric
#> [1] "2704179"
#> attr(,"class")
#> [1] "gbifid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] FALSE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "https://www.gbif.org/species/2704179"
as.gbifid("2704179") # character
#> [1] "2704179"
#> attr(,"class")
#> [1] "gbifid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] FALSE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "https://www.gbif.org/species/2704179"
as.gbifid(list("2704179","2435099","3171445")) # list, either numeric or character
#> [1] "2704179" "2435099" "3171445"
#> attr(,"class")
#> [1] "gbifid"
#> attr(,"match")
#> [1] "found" "found" "found"
#> attr(,"multiple_matches")
#> [1] FALSE FALSE FALSE
#> attr(,"pattern_match")
#> [1] FALSE FALSE FALSE
#> attr(,"uri")
#> [1] "https://www.gbif.org/species/2704179"
#> [2] "https://www.gbif.org/species/2435099"
#> [3] "https://www.gbif.org/species/3171445"These as.*() functions do a quick check of the web
resource to make sure it’s a real ID. However, you can turn this check
off, making this coercion much faster:
system.time( replicate(3, as.gbifid(c("2704179","2435099","3171445"), check=TRUE)) )
#> user system elapsed
#> 0.092 0.003 4.850
system.time( replicate(3, as.gbifid(c("2704179","2435099","3171445"), check=FALSE)) )
#> user system elapsed
#> 0.002 0.000 0.002If someone is not a taxonomic specialist on a particular taxon he likely does not know what children taxa are within a family, or within a genus. This task becomes especially unwieldy when there are a large number of taxa downstream. You can of course go to a website like Wikispecies or Encyclopedia of Life to get downstream names. However, taxize provides an easy way to programatically search for downstream taxa for the Integrated Taxonomic Information System.
apis_itis_id <- 154395 # id for Apis, fetched beforehand to save time here
downstream(apis_itis_id, downto = "species", db = "itis")
#> $`154395`
#> tsn parentname parenttsn rankname taxonname rankid
#> 1 1128092 Apis 154395 species Apis laboriosa 220
#> 2 154396 Apis 154395 species Apis mellifera 220
#> 3 763550 Apis 154395 species Apis andreniformis 220
#> 4 763551 Apis 154395 species Apis cerana 220
#> 5 763552 Apis 154395 species Apis dorsata 220
#> 6 763553 Apis 154395 species Apis florea 220
#> 7 763554 Apis 154395 species Apis koschevnikovi 220
#> 8 763555 Apis 154395 species Apis nigrocincta 220
#>
#> attr(,"class")
#> [1] "downstream"
#> attr(,"db")
#> [1] "itis"You may sometimes only want the direct children. We got you covered on that front, with methods for ITIS and NCBI.
The direct children (genera in this case) of Pinaceae using NCBI data:
children("Pinaceae", db = "ncbi")
#> $Pinaceae
#> childtaxa_id childtaxa_name childtaxa_rank
#> 1 123600 Nothotsuga genus
#> 2 64685 Cathaya genus
#> 3 3358 Tsuga genus
#> 4 3356 Pseudotsuga genus
#> 5 3354 Pseudolarix genus
#> 6 3337 Pinus genus
#> 7 3328 Picea genus
#> 8 3325 Larix genus
#> 9 3323 Keteleeria genus
#> 10 3321 Cedrus genus
#> 11 3319 Abies genus
#>
#> attr(,"class")
#> [1] "children"
#> attr(,"db")
#> [1] "ncbi"With accession numbers
genbank2uid(id = 'AJ748748')
#> [[1]]
#> [1] "282199"
#> attr(,"class")
#> [1] "uid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] FALSE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "https://www.ncbi.nlm.nih.gov/taxonomy/282199"
#> attr(,"name")
#> [1] "Nereida ignava 16S rRNA gene, type strain 2SM4T"With gi numbers
genbank2uid(id = 62689767)
#> [[1]]
#> [1] "282199"
#> attr(,"class")
#> [1] "uid"
#> attr(,"match")
#> [1] "found"
#> attr(,"multiple_matches")
#> [1] FALSE
#> attr(,"pattern_match")
#> [1] FALSE
#> attr(,"uri")
#> [1] "https://www.ncbi.nlm.nih.gov/taxonomy/282199"
#> attr(,"name")
#> [1] "Nereida ignava 16S rRNA gene, type strain 2SM4T"Biologist often need to match different sets of data tied to species. For example, trait-based approaches are a promising tool in ecology. One problem is that abundance data must be matched with trait databases. These two data tables may contain species information on different taxonomic levels and possibly data must be aggregated to a joint taxonomic level, so that the data can be merged. taxize can help in this data-cleaning step, providing a reproducible workflow:
We can use the mentioned classification-function to
retrieve the taxonomic hierarchy and then search the hierarchies up- and
downwards for matches. Here is an example to match a species with names
on three different taxonomic levels.
A <- "gammarus roeseli"
B1 <- "gammarus roeseli"
B2 <- "gammarus"
B3 <- "gammaridae"
A_clas <- classification(A, db = 'ncbi')
#> ══ 1 queries ═══════════════
#> ✔ Found: gammarus+roeseli
#> ══ Results ═════════════════
#>
#> ● Total: 1
#> ● Found: 1
#> ● Not Found: 0
B1_clas <- classification(B1, db = 'ncbi')
#> ══ 1 queries ═══════════════
#> ✔ Found: gammarus+roeseli
#> ══ Results ═════════════════
#>
#> ● Total: 1
#> ● Found: 1
#> ● Not Found: 0
B2_clas <- classification(B2, db = 'ncbi')
#> ══ 1 queries ═══════════════
#> ✔ Found: gammarus
#> ══ Results ═════════════════
#>
#> ● Total: 1
#> ● Found: 1
#> ● Not Found: 0
B3_clas <- classification(B3, db = 'ncbi')
#> ══ 1 queries ═══════════════
#> ✔ Found: gammaridae
#> ══ Results ═════════════════
#>
#> ● Total: 1
#> ● Found: 1
#> ● Not Found: 0
B1[match(A, B1)]
#> [1] "gammarus roeseli"
A_clas[[1]]$rank[tolower(A_clas[[1]]$name) %in% B2]
#> [1] "genus"
A_clas[[1]]$rank[tolower(A_clas[[1]]$name) %in% B3]
#> [1] "family"If we find a direct match (here Gammarus roeseli), we are lucky. But we can also match Gammaridae with Gammarus roeseli, but on a lower taxonomic level. A more comprehensive and realistic example (matching a trait table with an abundance table) is given in the vignette on matching.