Install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("brendanf/phylotax")The PHYLOTAX algorithm takes as input taxonomic annotations from one or more primary taxonomic assignment algorithms, and refines them using a taxonomic tree. The refinements are of two basic types:
- Tips of the tree which are unassigned are assigned to a taxon if the tree supports their inclusion in that taxon.
- Conflicting assignments between multiple primary algorithms are resolved using the tree.
The phylotax package also includes the wrapper function taxonomy()
which assigns taxonomy to sequences using
DADA2,
IDTAXA, or
SINTAX, along
the taxtable() function which converts the results to a uniform
format.
Here is an example of a tree:
library(phylotax)
plot(example_tree(), show.node.label = TRUE)
Here is a set of taxonomic assignments for the tips of the tree, based on two hypothetical primary assignment algorithms “XTAX” and “YTAX”. The required columns are “label”, “rank”, and “taxon”, which identify individual OTUs, taxonomic ranks, and taxonomic assignments. The OTU labels are the same as the tip labels on the tree, in this case the letters A-F, although some are missing from the taxonomy table because the algorithms could not place them. Only assignments at the rank of genus are present in this small example, and the genera in question are called “Tax1” and “Tax2”. Our example also has a “method” column, which PHYLOTAX uses to identify which assignments come from the same source.
example_taxa()
#> # A tibble: 7 x 4
#> label method rank taxon
#> <chr> <chr> <chr> <chr>
#> 1 B XTAX genus Tax1
#> 2 C XTAX genus Tax2
#> 3 D XTAX genus Tax2
#> 4 B YTAX genus Tax2
#> 5 C YTAX genus Tax2
#> 6 D YTAX genus Tax1
#> 7 F YTAX genus Tax1If we sort by the taxon label, we can see that XTAX and YTAX disagree about the assignment of tips B and D. Neither algorithm has placed tips A and E, and XTAX also failed to place tip F.
dplyr::arrange(example_taxa(), label)
#> # A tibble: 7 x 4
#> label method rank taxon
#> <chr> <chr> <chr> <chr>
#> 1 B XTAX genus Tax1
#> 2 B YTAX genus Tax2
#> 3 C XTAX genus Tax2
#> 4 C YTAX genus Tax2
#> 5 D XTAX genus Tax2
#> 6 D YTAX genus Tax1
#> 7 F YTAX genus Tax1Use PHYLOTAX to resolve conflicts and assign additional tips where the tree supports it.
phylotax_out <- phylotax(tree = example_tree(), taxa = example_taxa())
#> INFO [2021-03-03 07:03:38] Assigned node 9 (label: 3) and its 2 descendant(s) to genus Tax2.
#> INFO [2021-03-03 07:03:38] Assigned node 10 (label: 4) and its 3 descendant(s) to genus Tax1.PHYLOTAX returns a list of class “phylotax” containing the tree, taxa
assignments for tips and internal nodes, as well as tables dividing the
primary assignments into those which were rejected, those which were
retained, and those which were missing from the input tree.
phylotax_out$assigned
#> # A tibble: 5 x 4
#> label method rank taxon
#> <chr> <chr> <ord> <chr>
#> 1 B PHYLOTAX genus Tax2
#> 2 C PHYLOTAX genus Tax2
#> 3 E PHYLOTAX genus Tax1
#> 4 F PHYLOTAX genus Tax1
#> 5 D PHYLOTAX genus Tax1phylotax_out$retained
#> # A tibble: 5 x 4
#> label method rank taxon
#> <chr> <chr> <ord> <chr>
#> 1 C XTAX genus Tax2
#> 2 B YTAX genus Tax2
#> 3 C YTAX genus Tax2
#> 4 D YTAX genus Tax1
#> 5 F YTAX genus Tax1phylotax_out$rejected
#> # A tibble: 2 x 4
#> label method rank taxon
#> <chr> <chr> <ord> <chr>
#> 1 B XTAX genus Tax1
#> 2 D XTAX genus Tax2PHYLOTAX has used the following logic:
- It’s not possible to decide what the root (node 1) is, because one of its direct children (tip A) is completely unassigned.
- It’s not possible to decide node 2, because there are differences between assignments for two of its descendents (tip C and tip F).
- All of the descendents of node 3 (tip B and tip C) have at least one assignment of Tax2. PHYLOTAX removes all conflicting assignments (XTAX’s assignment of Tax1 to tip B) and gives its own assignment of Tax2 to node 3 and all its children.
- All of the descendents of node 4 (tips D, E, and F) either have an assignment of Tax1 (D and F) or are unassigned (E). Furthermore, both branches coming from node 4 (tip D and node 5) do have some assignments. PHYLOTAX removes the conflicting assignments (XTAX’s assignment of Tax2 to tip D) and gives its own assignment of Tax1 to node 4 and all its children.
- At node 5, there is nothing to do, because PHYLOTAX already assigned it to Tax1 in step 4.
If you are continuing on with analysis using the
phyloseq package, then
you can easily create a phyloseq object from a phylotax object and
an OTU table.
To demonstrate, we first create a random presence/absence OTU table for 5 samples and the 6 OTUS present in our example dataset. Each species has a 50% chance to be present in each sample.
library(phyloseq)
set.seed(1)
otus <- matrix(rbinom(30, 1, 0.5),nrow = 5, dimnames = list(1:5, LETTERS[1:6]))
otus <- otu_table(otus, taxa_are_rows = FALSE)Now we can easily create the phyloseq object.
physeq <- phylotax_to_phyloseq(phylotax_out, otus)And use it for plots or whatever further analysis is needed.
plot_tree(physeq, color = "genus")
Now we’ll try a slightly longer example. Some downloaded reference
sequences in two database formats, as well as “unknowns”, are included
as example data in phylotax. This example goes through the process of
aligning, building a tree, performing taxonomic identification by
several algorithms, and then refining the assignments using the tree.
The data in question are LSU rDNA sequences belonging to the fungal order Sebacinales, taken from the RDP fungal training set. At the time of the publication of the RDP training set, much of the order Sebacinales was grouped into a large, polyphyletic genus, Sebacina. Since the work of Oberwinkler et al.1, the order has been split in two families, Serendipitaceae and Sebacinaceae, and several additional genera. For sequences in the database which are annotated as a particular species, we can look up the current name and classification for that species. However, for sequences originally annotated as “Sebacina sp.”, “uncultured Sebacina”, etc., there is way to decide what family/genus it should belong in now, except by re-identifying the sequences.
This is a very small example, with only 15 reference sequences and 39 “unknown” sequences, so it will run quickly. The references are the sequences from the dataset that were annnotated all the way to genus level, while the unknowns are the ones that weren’t.
unknowns <- system.file("extdata/unknowns.fasta.gz", package = "phylotax")
unknowns <- Biostrings::readDNAStringSet(unknowns)Some sequences from Dacrymycetes are also included to use as an outgroup in the tree.
outgroup <- system.file("extdata/dacrymycetes.fasta.gz", package = "phylotax")
outgroup <- Biostrings::readDNAStringSet(outgroup)We’ll do a quick alignment and phylogeny in R using DECIPHER.
suppressPackageStartupMessages(library(DECIPHER))
aln <- AlignSeqs(c(unknowns, outgroup))
#> Determining distance matrix based on shared 9-mers:
#> ================================================================================
#>
#> Time difference of 0.04 secs
#>
#> Clustering into groups by similarity:
#> ================================================================================
#>
#> Time difference of 0.02 secs
#>
#> Aligning Sequences:
#> ================================================================================
#>
#> Time difference of 0.58 secs
#>
#> Iteration 1 of 2:
#>
#> Determining distance matrix based on alignment:
#> ================================================================================
#>
#> Time difference of 0.01 secs
#>
#> Reclustering into groups by similarity:
#> ================================================================================
#>
#> Time difference of 0.02 secs
#>
#> Realigning Sequences:
#> ================================================================================
#>
#> Time difference of 0.41 secs
#>
#> Iteration 2 of 2:
#>
#> Determining distance matrix based on alignment:
#> ================================================================================
#>
#> Time difference of 0.01 secs
#>
#> Reclustering into groups by similarity:
#> ================================================================================
#>
#> Time difference of 0.02 secs
#>
#> Realigning Sequences:
#> ================================================================================
#>
#> Time difference of 0.03 secs
#>
#> Refining the alignment:
#> ================================================================================
#>
#> Time difference of 0.09 secsTrim the ends where there are a lot of gaps.
colgaps <- colSums(as.matrix(aln) == "-")
start <- min(which(cummin(colgaps) == 0))
end <- max(which(rev(cummin(rev(colgaps)) == 0)))
aln <- Biostrings::subseq(aln, start = start, end = end)Now we can make a tree.
distmat <- DistanceMatrix(aln, correction = "Jukes-Cantor")
#> ================================================================================
#>
#> Time difference of 0 secs
dendro <- IdClusters(distmat, method = "ML", type = "dendrogram", myXStringSet = aln)
#> Constructing initial neighbor-joining tree:
#> ================================================================================
#>
#> JC69: -ln(L)=6479, AICc=13297, BIC=13567
#> JC69+G4: -ln(L)=5960, AICc=12262, BIC=12533
#> K80: -ln(L)=6234, AICc=12812, BIC=13082
#> K80+G4: -ln(L)=5731, AICc=11810, BIC=12082
#> F81: -ln(L)=6491, AICc=13336, BIC=13608
#> F81+G4: -ln(L)=5979, AICc=12316, BIC=12588
#> HKY85: -ln(L)=6249, AICc=12856, BIC=13129
#> HKY85+G4: -ln(L)=5741, AICc=11845, BIC=12118
#> T92: -ln(L)=6235, AICc=12818, BIC=13090
#> T92+G4: -ln(L)=5732, AICc=11817, BIC=12089
#> TN93: -ln(L)=6162, AICc=12687, BIC=12960
#> TN93+G4: -ln(L)=5707, AICc=11782, BIC=12056
#>
#> The selected model was: TN93+G4
#>
#> Maximizing Likelihood of Tree:
#> -ln(Likelihood) = 5707 (0.00% improvement), 0 NNIs -ln(Likelihood) = 5705 (0.03% improvement), 0 NNIs -ln(Likelihood) = 5699 (0.13% improvement), 0 NNIs -ln(Likelihood) = 5693 (0.23% improvement), 0 NNIs -ln(Likelihood) = 5689 (0.32% improvement), 0 NNIs -ln(Likelihood) = 5667 (0.70% improvement), 0 NNIs -ln(Likelihood) = 5661 (0.81% improvement), 0 NNIs -ln(Likelihood) = 5655 (0.90% improvement), 0 NNIs -ln(Likelihood) = 5650 (0.99% improvement), 0 NNIs -ln(Likelihood) = 5648 (1.02% improvement), 0 NNIs -ln(Likelihood) = 5646 (1.06% improvement), 0 NNIs -ln(Likelihood) = 5646 (1.07% improvement), 0 NNIs -ln(Likelihood) = 5645 (1.08% improvement), 1 NNI -ln(Likelihood) = 5641 (1.16% improvement), 2 NNIs -ln(Likelihood) = 5638 (1.21% improvement), 3 NNIs -ln(Likelihood) = 5637 (1.22% improvement), 4 NNIs -ln(Likelihood) = 5636 (1.23% improvement), 5 NNIs -ln(Likelihood) = 5634 (1.27% improvement), 6 NNIs -ln(Likelihood) = 5634 (1.27% improvement), 7 NNIs -ln(Likelihood) = 5623 (1.47% improvement), 7 NNIs -ln(Likelihood) = 5622 (1.49% improvement), 7 NNIs -ln(Likelihood) = 5621 (1.50% improvement), 7 NNIs -ln(Likelihood) = 5620 (1.51% improvement), 7 NNIs -ln(Likelihood) = 5617 (1.56% improvement), 8 NNIs -ln(Likelihood) = 5617 (1.57% improvement), 9 NNIs -ln(Likelihood) = 5617 (1.57% improvement), 9 NNIs -ln(Likelihood) = 5617 (1.57% improvement), 9 NNIs
#>
#> Model parameters:
#> Frequency(A) = 0.256
#> Frequency(C) = 0.196
#> Frequency(G) = 0.302
#> Frequency(T) = 0.247
#> Rate A <-> G = 1.800
#> Rate C <-> T = 5.294
#> Transversion rates = 1
#> Alpha = 0.535
#>
#> Time difference of 6.87 secsPHYLOTAX needs the tree to be a phylo object from the ape package.
We can convert from the dendrogram in a two-step process.
library(ape)
#>
#> Attaching package: 'ape'
#> The following object is masked from 'package:Biostrings':
#>
#> complement
tree <- as.phylo(as.hclust(dendro))How does it look?
plot(tree, cex = 0.5)
The tree is already rooted correctly, so we can move on.
DADA2 and SINTAX require the taxonomy to be presented in a different format in the sequence headers, so the reference file exists in two versions.
dada_ref <- system.file("extdata/sebacinales.dada2.fasta.gz", package = "phylotax")
sintax_ref <- system.file("extdata/sebacinales.sintax.fasta.gz", package = "phylotax")Let’s identify the unknown sequences using a few different assignment
algorithms. Different algorithms come from different people, and so they
use different interfaces and give results in different formats. Some are
implemented in R packages, others aren’t. However, the taxonomy() and
taxtable() functions in phylotax simplify this process by making a
single wrapper that calls different external packages.
For starters, we’ll used the RDP naïve Bayesian classifier, which is
also implemented in the DADA2 R package.
dada2_result <- taxonomy(unknowns, reference = dada_ref, method = "dada2")
#> Loading required namespace: dada2
#> Finished processing reference fasta.DADA2 returns taxonomy as a list of matrices.
str(dada2_result)
#> List of 2
#> $ tax : chr [1:39, 1:6] "Fungi" "Fungi" "Fungi" "Fungi" ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : Named chr [1:39] "GCGGAGGAAAAGAAACTAACAAGGATTCCCCTAGTAACGGCGAGTGAAGAGGGAATAGCTCAAATTTTGAATCTGGCACCTTTGGTGTCCGAATTGTAATCTAGAGAAGTG"| __truncated__ "AACGGCGAGTGAAGAGGGAAGAGCTCAAATTTTGAATCTGGCGTCTTTGGCGTCCGAATTGTAATCTAGAGAAGTGTTTTCCGTGACGGACCGTGCATAAGTCTCCTGGAA"| __truncated__ "CCCGCTGAACTTAAGCATATCAATAAGCGGAGGAAAAGAAACTAACAAGGATTCCCCTAGTAACGGCGAGTGAAGAGGGAAGAGCTCAAATTTTGAATCTGGCGTCTCTGG"| __truncated__ "GTAACGGCGAGTGAAGAGGGAAGAGCTCAAATTTTGAATCTGGCACCTCTGGTGTCTGAGTTGTAATCTAGAGAAGTGCTTTCTGTGGCGGACCGTGCACAAGTCTCCTGG"| __truncated__ ...
#> .. .. ..- attr(*, "names")= chr [1:39] "AF291363" "AJ534930" "AJ534931" "AJ534932" ...
#> .. ..$ : chr [1:6] "Kingdom" "Phylum" "Class" "Order" ...
#> $ boot: int [1:39, 1:6] 100 100 100 100 100 100 100 100 100 100 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : Named chr [1:39] "GCGGAGGAAAAGAAACTAACAAGGATTCCCCTAGTAACGGCGAGTGAAGAGGGAATAGCTCAAATTTTGAATCTGGCACCTTTGGTGTCCGAATTGTAATCTAGAGAAGTG"| __truncated__ "AACGGCGAGTGAAGAGGGAAGAGCTCAAATTTTGAATCTGGCGTCTTTGGCGTCCGAATTGTAATCTAGAGAAGTGTTTTCCGTGACGGACCGTGCATAAGTCTCCTGGAA"| __truncated__ "CCCGCTGAACTTAAGCATATCAATAAGCGGAGGAAAAGAAACTAACAAGGATTCCCCTAGTAACGGCGAGTGAAGAGGGAAGAGCTCAAATTTTGAATCTGGCGTCTCTGG"| __truncated__ "GTAACGGCGAGTGAAGAGGGAAGAGCTCAAATTTTGAATCTGGCACCTCTGGTGTCTGAGTTGTAATCTAGAGAAGTGCTTTCTGTGGCGGACCGTGCACAAGTCTCCTGG"| __truncated__ ...
#> .. .. ..- attr(*, "names")= chr [1:39] "AF291363" "AJ534930" "AJ534931" "AJ534932" ...
#> .. ..$ : chr [1:6] "Kingdom" "Phylum" "Class" "Order" ...Next, we’ll use another R-friendly taxonomic assignment algorithm,
IDTAXA from the DECIPHER package. IDTAXA requires that a model be
trained using the reference database, which can then be used over and
over again to identify different sequences. Although using the functions
provided in DECIPHER lets you tweak your training, phylotax also has
a wrapper for this, which takes the reference database in the same
format as SINTAX.
idtaxa_model <- train_idtaxa(sintax_ref)
#> ================================================================================
#>
#> Time difference of 0.6 secsNow that the model is trained, we can identify the unknown sequences.
idtaxa_result <- taxonomy(unknowns, reference = idtaxa_model, method = "idtaxa")
#> ================================================================================
#>
#> Time difference of 0.12 secsIDTAXA gives its result as a list of lists. Here are the first three elements:
str(idtaxa_result[1:3])
#> List of 3
#> $ AF291363:List of 3
#> ..$ taxon : chr [1:2] "Root" "unclassified_Root"
#> ..$ confidence: num [1:2] 45.1 45.1
#> ..$ rank : chr [1:2] "rootrank" "kingdom"
#> $ AJ534930:List of 3
#> ..$ taxon : chr [1:7] "Root" "Fungi" "Basidiomycota" "Agaricomycetes" ...
#> ..$ confidence: num [1:7] 56 56 56 56 56 ...
#> ..$ rank : chr [1:7] "rootrank" "kingdom" "phylum" "class" ...
#> $ AJ534931:List of 3
#> ..$ taxon : chr [1:7] "Root" "Fungi" "Basidiomycota" "Agaricomycetes" ...
#> ..$ confidence: num [1:7] 52.8 52.8 52.8 52.8 52.8 ...
#> ..$ rank : chr [1:7] "rootrank" "kingdom" "phylum" "class" ...
#> - attr(*, "class")= chr [1:2] "Taxa" "Test"Finally, we’ll use SINTAX, an algorithm which is implemented in USEARCH
and its open-source reimplementation, VSEARCH. For this to work, one of
these two must be installed. I’m using VSEARCH; to use USEARCH give
exec = "usearch" as an argument.
sintax_result <- taxonomy(unknowns, reference = sintax_ref, method = "sintax")SINTAX produces output as a delimited file, which taxonomy() has
parsed as a tibble. However this is still not the format we need for
PHYLOTAX.
str(sintax_result)
#> tibble [39 × 5] (S3: tbl_df/tbl/data.frame)
#> $ label : chr [1:39] "AF291363" "AJ534930" "AJ534931" "AJ534932" ...
#> $ seq : chr [1:39] "GCGGAGGAAAAGAAACTAACAAGGATTCCCCTAGTAACGGCGAGTGAAGAGGGAATAGCTCAAATTTTGAATCTGGCACCTTTGGTGTCCGAATTGTAATCTAGAGAAGTG"| __truncated__ "AACGGCGAGTGAAGAGGGAAGAGCTCAAATTTTGAATCTGGCGTCTTTGGCGTCCGAATTGTAATCTAGAGAAGTGTTTTCCGTGACGGACCGTGCATAAGTCTCCTGGAA"| __truncated__ "CCCGCTGAACTTAAGCATATCAATAAGCGGAGGAAAAGAAACTAACAAGGATTCCCCTAGTAACGGCGAGTGAAGAGGGAAGAGCTCAAATTTTGAATCTGGCGTCTCTGG"| __truncated__ "GTAACGGCGAGTGAAGAGGGAAGAGCTCAAATTTTGAATCTGGCACCTCTGGTGTCTGAGTTGTAATCTAGAGAAGTGCTTTCTGTGGCGGACCGTGCACAAGTCTCCTGG"| __truncated__ ...
#> $ hit : chr [1:39] "k:Fungi(1.00),p:Basidiomycota(1.00),c:Agaricomycetes(1.00),o:Sebacinales(1.00),f:Sebacinaceae(1.00),g:Sebacina(0.98)" "k:Fungi(1.00),p:Basidiomycota(1.00),c:Agaricomycetes(1.00),o:Sebacinales(1.00),f:Sebacinaceae(1.00),g:Sebacina(0.98)" "k:Fungi(1.00),p:Basidiomycota(1.00),c:Agaricomycetes(1.00),o:Sebacinales(1.00),f:Sebacinaceae(0.99),g:Sebacina(0.86)" "k:Fungi(1.00),p:Basidiomycota(1.00),c:Agaricomycetes(1.00),o:Sebacinales(1.00),f:Sebacinaceae(0.97),g:Sebacina(0.91)" ...
#> $ strand: chr [1:39] "+" "+" "+" "+" ...
#> $ c12n : chr [1:39] "k:Fungi,p:Basidiomycota,c:Agaricomycetes,o:Sebacinales,f:Sebacinaceae,g:Sebacina" "k:Fungi,p:Basidiomycota,c:Agaricomycetes,o:Sebacinales,f:Sebacinaceae,g:Sebacina" "k:Fungi,p:Basidiomycota,c:Agaricomycetes,o:Sebacinales,f:Sebacinaceae,g:Sebacina" "k:Fungi,p:Basidiomycota,c:Agaricomycetes,o:Sebacinales,f:Sebacinaceae,g:Sebacina" ...Fortunately, the taxtable() function turns any of these three formats
into the format PHYLOTAX needs:
dada2_taxonomy <- taxtable(dada2_result, min_confidence = 0.6, names = names(unknowns))
idtaxa_taxonomy <- taxtable(idtaxa_result, min_confidence = 0.6)
sintax_taxonomy <- taxtable(sintax_result, min_confidence = 0.6)Now they are all in the same format:
head(dada2_taxonomy)
#> # A tibble: 6 x 4
#> label rank taxon confidence
#> <chr> <ord> <chr> <dbl>
#> 1 AF291363 kingdom Fungi 1
#> 2 AF291363 phylum Basidiomycota 1
#> 3 AF291363 class Agaricomycetes 1
#> 4 AF291363 order Sebacinales 1
#> 5 AF291363 family Sebacinaceae 1
#> 6 AF291363 genus Sebacina 0.82
head(idtaxa_taxonomy)
#> # A tibble: 6 x 4
#> label rank taxon confidence
#> <chr> <ord> <chr> <dbl>
#> 1 DQ521408 kingdom Fungi 0.635
#> 2 DQ521408 phylum Basidiomycota 0.635
#> 3 DQ521408 class Agaricomycetes 0.635
#> 4 DQ521408 order Sebacinales 0.635
#> 5 DQ521408 family Sebacinaceae 0.635
#> 6 DQ521408 genus Sebacina 0.635
head(sintax_taxonomy)
#> # A tibble: 6 x 4
#> label rank taxon confidence
#> <chr> <ord> <chr> <dbl>
#> 1 AF291363 kingdom Fungi 1
#> 2 AF291363 phylum Basidiomycota 1
#> 3 AF291363 class Agaricomycetes 1
#> 4 AF291363 order Sebacinales 1
#> 5 AF291363 family Sebacinaceae 1
#> 6 AF291363 genus Sebacina 0.98However, they differ in how many assignments they each made:
nrow(dada2_taxonomy)
#> [1] 224
nrow(idtaxa_taxonomy)
#> [1] 42
nrow(sintax_taxonomy)
#> [1] 230Keep in mind that assigning one sequence from kingdom to genus is a total of six assignments.
Now, we can add a “method” column to each of the taxonomy tables and merge them.
dada2_taxonomy$method = "DADA2"
idtaxa_taxonomy$method = "IDTAXA"
sintax_taxonomy$method = "SINTAX"
combined_taxonomy = rbind(dada2_taxonomy, idtaxa_taxonomy, sintax_taxonomy)The PHYLOTAX algorithm doesn’t really make sense in an unrooted tree. In a future version of this README, I’ll include some outgroup sequences.
The make_taxon_labels() and relabel_tree() functions let us
summarize the state of the assignments before and after PHYLOTAX. The
abbrev=TRUE argument makes some abbreviations of common elements of
fungal taxa, to make these easier to read.
pre_labels <- make_taxon_labels(combined_taxonomy, abbrev = TRUE)
pre_tree <- relabel_tree(tree, pre_labels$old, pre_labels$new)
plot.phylo(pre_tree, cex = 0.5)
For each rank, we have a number followed by an abbreviated taxon name. The number is the number of different algorithms that assigned that taxon. When there’s a conflict, we see the alternates in angle brackets. For an example of interpretation, look at Seq4. Out of a total 3 possible, it got:
- 3 assignments for kingdom F(ungi)
- 3 assignments for phylum B(asidiomycota)
- 3 assignments for class Agarico(mycetes)
- 3 assignments for order Sebacin(ales)
- 3 assignments for family Sebacin(aceae)
- 1 assignment each for the genera Sebacina and Tremellodendron.
Now that we have a tree and a set of primary taxonomic assignments, it’s time for PHYLOTAX.
p <- phylotax(tree = tree, taxa = combined_taxonomy)
#> INFO [2021-03-03 07:03:54] Assigned node 58 and its 39 descendant(s) to kingdom Fungi.
#> INFO [2021-03-03 07:03:54] Assigned node 58 and its 39 descendant(s) to phylum Basidiomycota.
#> INFO [2021-03-03 07:03:54] Assigned node 58 and its 39 descendant(s) to class Agaricomycetes.
#> INFO [2021-03-03 07:03:54] Assigned node 58 and its 39 descendant(s) to order Sebacinales.
#> INFO [2021-03-03 07:03:55] Assigned node 62 and its 21 descendant(s) to family Serendipitaceae.
#> INFO [2021-03-03 07:03:55] Assigned node 62 and its 21 descendant(s) to genus Serendipita.
#> INFO [2021-03-03 07:03:55] Assigned node 63 and its 18 descendant(s) to family Sebacinaceae.
#> INFO [2021-03-03 07:03:55] Assigned node 21 and its 1 descendant(s) to genus Tremellodendron.
#> INFO [2021-03-03 07:03:55] Assigned node 81 and its 3 descendant(s) to genus Sebacina.
#> INFO [2021-03-03 07:03:55] Assigned node 78 and its 14 descendant(s) to genus Sebacina.We can look at the tree again to see how PHYLOTAX has cleaned up the assignments.
post_labels <- make_taxon_labels(rbind(p$retained, p$assigned), abbrev = TRUE)
post_tree <- relabel_tree(tree, post_labels$old, post_labels$new)
plot.phylo(post_tree, cex = 0.5)
Compare this to the original annotations!
old_tree <- relabel_tree(tree, names(sebacinales_oldnames), sebacinales_oldnames)
plot(old_tree, cex = 0.5)
1 Oberwinkler, F., Riess, K., Bauer, R. et al. Morphology and molecules: the Sebacinales, a case study. Mycol Progress 13, 445–470 (2014). https://doi.org/10.1007/s11557-014-0983-1 ↩