bactaxR

Bacterial taxonomy construction and evaluation in R

Overview

bactaxR is an R package which contains functions to aid in average-nucleotide identity (ANI)-centric bacterial taxonomy construction and evaluation. Specific functions include:

• Parsing output from fastANI
• Identification of ANI-based genomospecies breakpoints
• ANI-based dendrogram construction
• Identification of medoid genomes using selected genomospecies thresholds
• Mapping discrete traits (e.g., genomospecies, presence or absence of a phenotypic trait) to phylogenies

Post issues at https://github.com/lmc297/bactaxR/issues

Citation

If you found bactaxR and/or its source code to be useful, please cite:

Carroll, Laura M., Martin Wiedmann, Jasna Kovac. 2020. "Proposal of a Taxonomic Nomenclature for the Bacillus cereus Group Which Reconciles Genomic Definitions of Bacterial Species with Clinical and Industrial Phenotypes." mBio 11(1): e00034-20; DOI: 10.1128/mBio.00034-20.

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Installation

1. Download R, if necessary: https://www.r-project.org/

2. Dowload R Studio, if necessary: https://www.rstudio.com/products/rstudio/download/

3. Open R Studio, and install the devtools package, if necessary, by typing the following command into R Studio's console:

install.packages("devtools")

4. Install ggtree from Bioconductor, if necessary, by running the following commands from R Studio's console:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("ggtree")

5. Load devtools by typing the following command into R Studio's console:

library(devtools)

6. Install bactaxR by typing the following command into R Studio's console:

install_github("lmc297/bactaxR")

Note: Users who get an error when installing bactaxR should run the following command before attempting to install bactaxR again: Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS"=TRUE)

7. Load bactaxR by typing the following command into R Studio's console:

library(bactaxR)

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Tutorials

Tutorial 1: Construct a histogram and dendrogram using pairwise ANI values, identify medoid genomes, and visualize medoid-based clusters in a graph

1. For this tutorial, we're going to use pairwise ANI values that were calculated between 36 B. cereus group genomes using FastANI (a subset of the original data set, which will save time and memory; for all 2,231 genomes used in the full data set, see Supplementary Table S1 of the paper).

Click here to download the data set. This tab-separated file was produced by FastANI, with query genomes in the first column, reference genomes in the second column, and ANI values in the third column.

Feel free to save this file in the directory of your choice; if you would like to follow along with this tutorial exactly, using identical path/file names, save this file in your home directory as bactaxR_fastani_output.txt.

2. Open RStudio; if you have not already installed bactaxR, follow the installation instructions above.

3. If you have not already done so, load bactaxR using the following command:

library(bactaxR)

4. Let's store our pairwise ANI information as a bactaxRObject; this will allow us to construct dendrograms and graphs and identify medoid genomes, exactly as was done in the paper. To do so, run the following command (replace ~/bactaxR_fastani_output.txt with the path to your own file, if necessary):

ani <- read.ANI(file = "~/bactaxR_fastani_output.txt")

This command:

• Uses the read.ANI function in bactaxR to read bactaxR_fastani_output.txt, an output file produed by FastANI
• Stores genome names and pairwise comparisons as a bactaxRObject, assigning it to the variable name ani

Note: bactaxR can use pairwise ANI values calculated using any ANI tool, not just FastANI; you can use read.ANI with any headerless, tab-delimited file where query genome is in the first column, reference genome is in the second, and ANI values (ranging from 0 to 100) are in the third. Additionally, if you already have a data frame of query genomes/reference genomes/ANI values loaded into R, you can use the load.ANI function to store it as a bactaxRObject. Note that both of these functions check to make sure that these ANI values are pairwise all-vs-all ANI values (i.e., all query genome names must be identically present in the reference genome column, and vice-versa). Additionally, your ANI values should be between 0 and 100 (i.e., if they are between 0 and 1, multiply them by 100; for example, 0.95 ANI becomes 95 ANI, 0.971 ANI becomes 97.1 ANI). See ?read.ANI and ?load.ANI for more information.

We can obtain a summary of our bactaxR object using the following command:

summary(ani)

This should tell us that our data set has 36 genomes and 1,296 total comparisons; this makes sense, because 36^2 = 1,296 (i.e., these are pairwise comparisons).

5. Next, we'll construct a histogram using our pairwise ANI values. To build a histogram and store it as a variable h, run the following command:

h <- ANI.histogram(bactaxRObject = ani, bindwidth = 0.1)

This command:

• Builds a histogram using pairwise ANI values stored in a bactaxRObject (here, we're using our bactaxRObject which we named ani)
• Uses a histogram bin width of 0.1

To view the histogram, just run:

h

For more options for annotating/displaying your histogram, see ?ANI.histogram

6. Next, we will construct a dendrogram and identify medoid genomes with a single command. Most researchers have relied on a genomospecies threshold of 95, so let's use that as a threshold for identifying medoid genomes here. To build a dendrogram and identify medoid genomes at a 95 ANI genomospecies threshold, run the following command:

dend <- ANI.dendrogram(bactaxRObject = ani, ANI_threshold = 95, xline = c(4,5,6,7.5), xlinecol = c("#ffc425", "#f37735", "deeppink4", "black"), label_size = 0.5)

This command:

• Constructs a dendrogram, using the methods described in the paper, with ANI dissimilarity plotted along the X-axis
• Identifies medoid genomes at a 95 ANI threshold, using the ANI_threshold parameter
• Annotates the dendrogram using vertical lines at the specified ANI dissimilarity (i.e., X-axis) threshold(s), using the xline parameter for X-axis position and the xlinecol parameter for color information (here, we have vertical lines at dissimilarity values of 4, 5, 6, and 7.5, which correspond to ANI values of 96, 95, 94, and 92.5, respectively; these parameters are just for annotating the dendrogram plot, and have no analytical value/effect on the identification of medoid genomes or dendrogram construction)
• Annotates the dendrogram using tip labels with size 0.5 (label_size = 0.5; by default, this is set to an arbitrarily small number so that tip labels are hidden)

See ?ANI.dendrogram for a complete list of options.

We can see the medoid genomes identified at our specified ANI threshold (i.e., 95) by running dend$medoid_genomes

We can see the clusters to which all of our genomes were assigned at our specified ANI threshold using dend$cluster_assignments

7. Let's construct an ANI similarity graph using our pairwise ANI values, and color it using the 95 ANI cluster assignments we produced in step 6. If we look at ?ANI.graph, we can see that we need to supply metadata (i.e., the discrete attributes which we will use to color our graph; in our case, cluster assignment) in the form of a named vector.

To do this, we'll create a vector, metadata, which contains our cluster assinments:

metadata <- dend$cluster_assignments$Cluster

7. Next, we'll name our vector of cluster assignments with their associated genome labels:

names(metadata) <- dend$cluster_assignments$Genome

8. Now we can construct our graph as follows (we'll use a 95 ANI threshold like we did before):

ANI.graph(bactaxRObject = ani, ANI_threshold = 95,
          metadata = metadata,
          legend_pos_x = -1.5, show_legend = T, graphout_niter = 1000000, 
          legend_ncol = 1, edge_color = "black")

This command:

• Constructs a graph, drawing an edge between any two genomes which share an ANI value greater than or equal to ANI_threshold (here, we set this to 95)
• Colors nodes (i.e., points) using the named vector metadata (here, we used clusters identified in step 6 at a 95 ANI threshold)
• Annotate and color the graph according to various user-supplied parameters (see ?ANI.graph for more details)

Tutorial 2: Annotate a phylogeny using discrete traits

1. For this tutorial, we're going to annotate a phylogeny constructed using 79 marker genes identified in 2,231 B. cereus group genomes, using discrete metadata (i.e., species assignments and presence/absence of phenotypic traits).

Click here to download the phylogeny (in Newick format).

Feel free to save this file in the directory of your choice; if you would like to follow along with this tutorial exactly, using identical path/file names, save this file in your home directory as bactaxR_phylogeny.nwk.

2. Click here, and click "Download" to download a Microsoft Excel file which contains metadata for all 2,231 genomes (i.e., Supplementary Table S1 from the paper).

Feel free to save this file in the directory of your choice; if you would like to follow along with this tutorial exactly, using identical path/file names, save this file in your home directory as sup_table_s1_genomes_ani.xlsx (note that if you download the file as described here, it will likely be stored in your Downloads directory; please move it to your home directory if you would like to use exact path names).

3. Open RStudio; if you have not already installed bactaxR, follow the installation instructions above.

4. If you have not already done so, load bactaxR using the following command:

library(bactaxR)

5. If you have not already done so, load the phytools package using the following command:

library(phytools)

6. If you have not already done so, load the readxl package using the following command:

library(readxl)

7. Read the phylogeny into R using the read.newick function in phytools, via the following command (replace ~/bactaxR_phylogeny.nwk with the path to your own file, if necessary):

tree <- read.newick(file = "~/bactaxR_phylogeny.nwk")

If we run the command tree, we can see that our phylogeny has 2,231 tips (i.e., genomes) and is unrooted.

8. Read the metadata into R using the read_excel funtion in the readxl package, via the following command (replace ~/sup_table_s1_genomes_ani.xlsx with the path to your own file, if necessary):

x <- read_excel(path = "~/sup_table_s1_genomes_ani.xlsx", skip = 1)

We can get a summary of our metadata by running summary(x)

9. We need to make sure our tree tip labels match the isolate names in our metadata file (i.e., those in the Study ID column); if we type table(tree$tip.label%in%x$`Study IDa,b`), we can see see that some of our tree tip labels cannot be found in StudyID column in our metadata table. This is because some of our tree tip labels have dashes instead of underscores. To replace all dashes in our tree tip labels with underscores, run the following command:

tree$tip.label <- gsub(pattern = "-", replacement = "_", x = tree$tip.label)

If we type table(tree$tip.label%in%x$`Study IDa,b`), we can now see that all of our tip labels can be found in our metadata Study ID column.

10. Let's treat "B. manliponensis", the most distant member of the group, as an outgroup along which we can root our tree. To do so, run the following command:

tree <- root(tree, outgroup = "B_manliponensis_JCM_15802_TYPE_STRAIN.fna")

If we run tree, we should see that our tree is now rooted.

11. Let's color our tree using the phylo.discrete_trait_OTU function in bactaxR and genomospecies assignments produced at a 92.5 ANI threshold (see the column of our metadata titled "Closest Medoid Genome at 92.5 ANI Threshold (ANI)").

If we use the command ?phylo.discrete_trait_OTU, we can see that we need to supply a named list to trait_list, where the names correspond to the traits (i.e., genomospecies assignments), and the vectors under each name correspond to the taxa associated with each name (i.e., each genome assigned to a particular genomospecies).

Let's run the following command to create a vector named metadata.92_5; this vector is identical to our column "Closest Medoid Genome at 92.5 ANI Threshold (ANI)", except we are discarding all of the information after a "(" character (i.e., we are removing the ANI values appended to each medoid genome):

metadata.92_5 <- unlist(lapply(strsplit(x = x$`Closest Medoid Genome at 92.5 ANI Threshold (ANI)`, split = "\\("), "[[", 1))

If we run table(metadata.92_5), we can see the number of genomes assigned to each genomospecies.

12. Let's name the vector of genomospecies assignments (metadata.92_5) using the corresponding Study IDs (which match tip labels found in the tree):

names(metadata.92_5) <- x$`Study IDa,b`

13. To make the list more interpretable, run each of the following commands below; these will replace the genomospecies medoid genome names with their respective names proposed in the manuscript:

metadata.92_5 <- gsub(pattern = "Bacillus_anthracis_GCF_001683155_1_ASM168315v1_genomic.fna", replacement = "B. mosaicus ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "B_bingmayongensis_FJAT-13831_TYPE_STRAIN.fna", replacement = "B. bingmayongensis ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_cereus_GCF_002568015_1_ASM256801v1_genomic.fna", replacement = "B. cereus s.s. ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_cytotoxicus_GCF_002251005_2_ASM225100v2_genomic.fna", replacement = "B. cytotoxicus ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "B_gaemokensis_KCTC_13318_TYPE_STRAIN.fna", replacement = "B. gaemokensis ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_cereus_GCF_002559215_1_ASM255921v1_genomic.fna", replacement = "B. luti ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "B_manliponensis_JCM_15802_TYPE_STRAIN.fna", replacement = "B. manliponensis ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_cereus_GCF_002550135_1_ASM255013v1_genomic.fna", replacement = "B. mycoides ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_cereus_GCF_002576795_1_ASM257679v1_genomic.fna", replacement = "B. paramycoides ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_pseudomycoides_GCF_002588885_1_ASM258888v1_genomic.fna", replacement = "B. pseudomycoides ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "B_toyonensis_BCT_7112_TYPE_STRAIN.fna", replacement = "B. toyonensis ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_cereus_GCF_002583515_1_ASM258351v1_genomic.fna", replacement = "Unknown B. cereus group Species 13", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_cereus_GCF_002550855_1_ASM255085v1_genomic.fna", replacement = "Unknown B. cereus group Species 14 ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_cereus_GCF_002559665_1_ASM255966v1_genomic.fna", replacement = "Unknown B. cereus group Species 15 ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_cereus_GCF_002578045_1_ASM257804v1_genomic.fna", replacement = "Unknown B. cereus group Species 16 ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_cereus_GCF_002584535_1_ASM258453v1_genomic.fna", replacement = "Unknown B. cereus group Species 17 ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_mycoides_GCF_000746925_1_BHP_1_genomic.fna", replacement = "B. clarus ", x = metadata.92_5)
metadata.92_5 <- gsub(pattern = "Bacillus_pseudomycoides_GCF_002552625_1_ASM255262v1_genomic.fna", replacement = "Unknown B. cereus group Species 18", x = metadata.92_5)

14. Remove the NA value (produced due to the footer in the metadata Excel sheet) using the following function:

metadata.92_5 <- na.omit(metadata.92_5)

If we run table(metadata.92_5), we should see that our genomospecies genome names have been repaced with species names.

15. We can now construct the named list that we will supply to trait_list. To construct this list, we'll first initiate an empty list, and assign it to the variable name clades.92_5:

clades.92_5 <- list()

16. Now run the following, which will loop through each genomospecies in our named vector (metadata.92_5), identify all genomes which belong to that genomospecies, and store it in our list, clades.92_5:

for (i in 1:length(unique(metadata.92_5))){
  myclust <- as.character(unique(metadata.92_5)[i])
  tiplabs <- names(metadata.92_5)[which(metadata.92_5==myclust)]
  clades.92_5[[myclust]] <- tiplabs
}

If we type names(clades.92_5), we should see that our list has named elements, with one per genomospecies.

17. Let's create a color palette for our tree by running the following function, which uses the viridis palette to color known/proposed species, and colors the root of the tree and unknown species black:

pal.tree_92_5 <- c("black", viridis(option = "viridis", n = 12), rep("black", 6))

18. We can finally color our phylogeny using the phylo.discrete_trait_OTU function in bactaxRby running the following:

tree_92_5 <- phylo.discrete_trait_OTU(phylo = tree, trait_list = clades.92_5,
                                        color_palette = pal.tree_92_5,
                                        phylo_layout = "circular", tip_label_size = 0.5)

This command:

• Uses phylo.discrete_trait_OTU to color the phylogeny using a named list, clades.92_5
• Colors the respective clusters using the provided color_palette
• Displays the phylogeny in a circular layout with a tip label size of 0.5

To view the phylogeny, run:

tree_92_5

To see more phylogeny annotation options, see ?phylo.discrete_trait_OTU

19. Let's add a heatmap to our tree to display predicted phenotypic information (i.e., anthrax toxin, cereulide, and Cry/Cyt toxin production) for our isolates. If we type ?phylo.discrete_trait_heatmap, we see that we need to supply a data frame to trait_data_frame, where each row corresponds to an isolate and each column to a trait.

To construct a trait data frame, with row names which match our Study ID/tree tip labels, run the following command:

traitmap <- data.frame(x$`Anthrax toxin-encoding cya, lef, and pagAc`,
                       x$`Cereulide synthetase-encoding cesABCDc`,
                       x$`Known Cry- and/or Cyt-encoding genesd`,
                       row.names = x$`Study IDa,b`)

20. Let's change our column names to the trait which they represent:

colnames(traitmap) <- c("Anthrax", "Cereulide", "Thuringiensis")

21. Run each of the following commands to replace cells which correspond to the absence of genes/a trait with a numerical character (i.e., "0", "1", or "2" for Anthrax, Cereulide, and Thuringiensis traits, respectively):

traitmap$Anthrax <- ifelse(test = grepl(pattern = "Absent", x = traitmap$Anthrax), yes = "0", no = "Anthrax toxin genes present")
traitmap$Cereulide <- ifelse(test = grepl(pattern = "Absent", x = traitmap$Cereulide), yes = "1", no = "Cereulide synthetase genes present")
traitmap$Thuringiensis <- ifelse(test = grepl(pattern = "No Cry", x = traitmap$Thuringiensis), yes = "2", no = "Known Cry- and/or Cyt-encoding genes present")

22. Now we can add our heatmap using the following command:

tree_92_5.2 <- phylo.discrete_trait_heatmap(plot = tree_92_5, phylo = tree,
                                   trait_data_frame = traitmap,
                                   font_size = 0,
                                   heatmap_width = 0.1,
                                   heatmap_offset = 0.3,
                                   color_palette = c("#DE49681A", "#8C29811A", "#22A8841A" , "#DE4968FF", "#8C2981FF", "#22A884FF"))

This command:

• Adds a heatmap to our plot, tree_92_5, using our phylogeny (tree) and our trait data frame (traitmap)
• Removes trait names from the heatmap (font_size = 0)
• Displays the heatmap with corresponding width and offset parameters
• Annotates the heatmap using the specified color palette

If we run the following, we can view our phylogeny with the heatmap added:

tree_92_5.2 

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References

Carroll, Laura M., Martin Wiedmann, Jasna Kovac. 2020. "Proposal of a Taxonomic Nomenclature for the Bacillus cereus Group Which Reconciles Genomic Definitions of Bacterial Species with Clinical and Industrial Phenotypes." mBio 11(1): e00034-20; DOI: 10.1128/mBio.00034-20.

Carroll, Laura M., Martin Wiedmann, Jasna Kovac. 2019. "Proposal of a taxonomic nomenclature for the Bacillus cereus group which reconciles genomic definitions of bacterial species with clinical and industrial phenotypes." bioRxiv 779199; doi: https://doi.org/10.1101/779199.

Jain, Chirag, et al. High-throughput ANI Analysis of 90K Prokaryotic Genomes Reveals Clear Species Boundaries. bioRxiv 225342; doi: https://doi.org/10.1101/225342.

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Disclaimer: bactaxR is pretty neat! However, no tool is perfect. As always, interpret your results with caution. We are not responsible for taxonomic misclassifications, misclassifications of an isolate's pathogenic potential or industrial utility, and/or misinterpretations (biological, statistical, or otherwise) of bactaxR results.