“Nothing in biology makes sense except in the light of evolution.”
Phylogenetic trees are a tool for organizing biological diversity. Just as maps provide a spatial framework to the geographer, phylogenies provide an evolutionary context to the biologist: they capture the relationship among “things” (species, individuals, genes), represented as tips in the tree, based on common ancestry.
In evolutionary and epidemiological studies of Mycobacterium tuberculosis, it is now common to encounter large phylogenetic trees. Being able to read and critically examine them is extremely useful. Phylogenies can be used to understand the origin of a disease or the onset of an epidemic, to distinguish ongoing transmission from imported cases, to investigate how specific traits like antibiotic resistance evolve et cetera et cetera. Many research ideas originate from looking at and discussing patterns present in phylogenies.
This tutorial provides an introduction to phylogenetic trees in the context of whole genome sequencing of Mycobacterium tuberculosis strains. Phylogenetics is a vast topic, and we can only scratch its surface here. For those motivated to delve deeper into the topic, the Resources section contains links and reading suggestions.
Basic concepts: How to read a phylogeny
Below are two phylogenies of the Mycobacterium tuberculosis complex (MTBC) to illustrate some basic vocabulary and concepts.
Figure 1: Rooted [A] and unrooted [B] phylogeny of the MTBC. Some basic tree vocabulary is shown in red.
The trees in figure 1 look rather different at a first glance, but they are identical except for one key aspect: tree A is rooted while tree B is not.
What is the difference between the two? First, let’s state what is the same in the two trees: the tree topology, that is, the relative branching order. The same groupings are present in the two trees: they both contain the same information about the relatedness of strains. An example: TB isolated from Peruvian mummies is most similar to M. pinnipedii known from marine mammals; they share a most recent common ancestor. This can be seen in the rooted as well as in the unrooted tree.
The key difference between the rooted and the unrooted tree is that only the rooted tree shows the direction and sequence of branching events. The unrooted tree does not tell us, for example, whether M. bovis diverged early or late in the history of the MTBC. It is thus compatible with the old hypothesis that human TB evolved from animal TB. The rooted tree shows that this hypothesis is most likely wrong: animal-associated strains are not ancestral to human-associated strains.
The best way to root a tree is by including an outgroup: a species or lineage which we know a priori to lay outside the phylogeny we’re interested in. M. canettii usually serves this purpose for studying the MTBC, but you can also root, for example, a phylogeny of lineage 2 by including a lineage 4 strain.
Branch lengths
Besides relatedness and direction, a third important piece of information contained in a phylogeny is the branch length. When a phylogeny has been estimated from DNA or protein sequences, branch lengths usually reflect the evolutionary distance between nodes in the tree. This information can be used to translate distance in terms of expected nucleotide changes into years, and thus to connect evolutionary change to historical events.
Figure 2: Phylogeny of the central Asian clade, including the Oslo outbreak. Modified Eldholm et al. 2016.
As branch lengths reflect evolutionary distances, they can also be used to identify transmission clusters and outbreaks. Figure 2 shows a (rooted) tree of the Central Asian Clade (CAC), which is part of lineage 2 (Eldholm et al. 2016). The orange color highlights the Afghan strain family within the CAC. At the bottom of the tree, note the clade with short branch lengths. This is how one would expect an outbreak to look in a phylogenetic tree: a set of strains clustering together and separated by extremely short branches, reflecting their almost identical genomes.
Comment: Phylogenetics with Mycobacterium tuberculosis
Phylogenetics with MTB has some particularities rarely encountered with other organisms.
There seems to be no horizontal gene transfer (HGT) in the MTBC. HGT complicates phylogenetic inference in many bacteria because a piece of DNA introduced by HGT has a different history and thus phylogeny than genes not affected by HGT.
There is little genetic diversity in the MTBC: any two strains differ by only around 2,500 SNPs over the whole genome. To achieve a good resolution of recent evolution (e.g. during an outbreak), whole genome sequences are thus extremely useful.
A large proportion of DNA polymorphisms in the MTBC are singletons, that is, variants present only in a single strain. This adds to the problem of low diversity, since singletons are not informative about tree topology.
In this workshop, and indeed in many studies of the MTBC, SNPs are called not against the reference strain H37Rv, but against a reconstructed ancestral genome. This means that the number of SNPs identified does not reflect the evolutionary distance from some random strain like H37Rv, but from the most recent common ancestor of the MTBC. Take a look at Figure 3 in Goig et al. 2018 to see how this affects the number of SNPs identified in a genome. Below we will see that this has implications for the interpretation of a tree.
Infering phylogeny from SNP alignments
Aligned DNA or protein sequences are the starting material for phylogenetic inference with molecular data.
In this tutorial we will explore the phylogenetic relationship between 19 MTBC strains and 1 strain of M. canettii, where the latter is included as outgroup, allowing us to root the phylogeny. Our starting point will be an alignment of all SNPs identified from whole-genome sequencing data of these 20 strains. This SNP alignment was generated by
mapping short sequenced reads from each sample to the infered sequence of the most recent common ancestor of the MTBC as the reference
calling variants against that reference from the mapped reads of each sample
incorporating the SNP variants of each sample into the reference to obtain a consensus genome of that sample
combining the consensus genomes into a single multi-sequence fasta dataset
discarding bases found to be identical in all samples from all sequences
If you want to gain a complete understanding of the whole process of sequencing-based analysis of MTBC strains, we recommend you, if you have not done so yet, to work through these two tutorials in the order above before continuing with this one here.
Figure 3 shows a snapshot of an illustrative SNP alignment.
Each row in the alignment represents a different strain, each column a position in the reference genome.
Figure 3: Illustrative example of a SNP alignment. Note the absence of conserved positions.
Because this alignment is based on SNPs, it contains only variable positions. This is important to keep in mind! Genetic distances between strains will be hugely overestimated if we leave out all the positions which show no variation!
Near the end of this tutorial, we will show and discuss ways to correct for this.
Comment: The alternative approach: genome assembly
A frequently used alternative approach to obtain a phylogeny from short read data is to a) assemble the genomes (see the numerous Galaxy tutorials on this topic), b) annotate genes, c) extract genes present in all strains (the “core” genes), d) align the core genes. This approach underlies core genome multilocus sequence typing (cgMLST), which is often used to genotype bacterial pathogens (e.g. Zhou et al. 2021).
With this bit of introduction you should, hopefully, be prepared to follow the flow and purpose of the analysis presented here.
You can continue working in the same history you created for the other tutorial, or copy the SNP alignment (the output of Finds SNP sitestool) over to a new one, and can safely skip the following first hands-on part.
If you are not interested in that other tutorial, here are the instructions for setting up a history for the analysis here, and for obtaining a copy of the SNP alignment from Zenodo.
Hands-on: Create a new history and prepare the input data
Create a new history for this analysis
Click the new-history icon at the top of the history panel:
Give the history a suitable name
Click on galaxy-pencil (Edit) next to the history name (which by default is “Unnamed history”)
Type the new name: MTB tree thinking tutorial
Click on Save
If you do not have the galaxy-pencil (Edit) next to the history name:
Click on Unnamed history (or the current name of the history) (Click to rename history) at the top of your history panel
Type the new name: MTB tree thinking tutorial
Press Enter
Upload a version of the SNP alignment via this URL
Click galaxy-uploadUpload Data at the top of the tool panel
Select galaxy-wf-editPaste/Fetch Data
Paste the link(s) into the text field
Change Type (set all): from “Auto-detect” to fasta
Press Start
Close the window
Replace Text in entire line ( Galaxy version 1.1.2) to clean up the aligned sequence names
param-file“File to process”: the uploaded SNP alignment
In param-repeat“1. Replacement”:
“Find pattern”: ^(>.+)\.fastq.*
“Replace with”: \1
From just a brief inspection of the downloaded SNP alignment, you should see that the sequence names in that file carry a .fastq.vcf or .fastq.gz.vcf suffix on the actual sample names.
The above replacement operation, drops these unnecessary endings, which we would otherwise carry over to all outputs generated in the analysis.
When the Replace Text tool run is finished, rename the output dataset to, for example, SNP alignment
Click on the galaxy-pencilpencil icon for the dataset to edit its attributes
In the central panel, change the Name field to SNP alignment
Click the Save button
Take a look at the alignment
Click on the dataset that you just renamed to expand it in the history, then click on galaxy-barchartVisualize and try both, the “Editor” and the “Multiple Sequence Alignment”.
Can you see one of the MTBC particularities mentioned above, the predominance of singletons?
How many sites are there in the alignment?
Also take a look at the M. canettii sequence: being the outgroup, it has a large number of SNPs.
Estimate a phylogeny
There are numerous methods to infer phylogenetic trees, but the most frequently used with large-scale molecular data are based on maximum likelihood and Bayesian inference. The details of how these methods construct trees from an alignment are beyond the scope of this introductory course. To be able to read trees, it is not necessary to know the statistical and computational details of how the trees are estimated. The books listed in the Resources section provide in-depth introductions into the different principles of phylogenetic inference, in particular Baum & Smith 2013 and Yang 2014.
In this tutorial, we will use the maximum likelihood method RAxML to estimate a phylogenetic tree for the 20 strains.
An aspect we ignore in this tutorial is the uncertainty involved in phylogenetic inference. While RAxML will deliver a single tree, not all aspects of this tree are equally well supported by the data. This uncertainty can be quantified through bootstrapping, a procedure where a large number of trees are estimated from random samples of the original data. If a certain split in the original tree is present in all the bootstrapped trees, then we can be confident about this split. Published phylogenies should always include a measure of uncertainty, while for this tutorial you will have to believe me that we are looking at a solid phylogeny…
Hands-on: Estimate a phylogeny for 20 MTBC strains
Phyogenetic reconstruction with RAxML ( Galaxy version 8.2.12+galaxy0) with the following parameters:
param-file“Source file with aligned sequences”: output (Input dataset)
“Model type”: Nucleotide
“RAxML options to use”: Required options only
The RAxML output we are interested in is the “Best-scoring ML tree”.
Select it in you Galaxy history and take a look at it with the different galaxy-barchart visualization options offered by Galaxy.
Figure 4: (Unrooted) RAxML tree in Galaxy's Phylogenetic Tree Visualization
Visualize and manipulate the tree
Phylogenetic trees are great tools because they are at the same time quantitative (we can do calculations on branch lengths, estimate uncertainty of a tree topology etc.) and visually appealing, allowing to actually “see” biologically interesting patterns. Often this requires some tweaking of the tree, for example by coloring parts of the tree according to some background information we have about the samples.
We will use R to plot and manipulate the phylogeny obtained from RAxML. We will provide you with the exact code to produce the figures we need, and, conveniently, you can run this code from an RStudio session that you can start as an interactive tool inside Galaxy.
If you have worked with R or RStudio previously, feel free to use any kind of coding environment for exploring and manipulating the RAxML tree we have produced - just download it from your history and import it into any R session you have access to.
Hands-on: Starting an RStudio session and setting things up
Note the dataset number (the number in front of and separated with a : from the dataset name) of the “Best-scoring ML tree” output of RAxML
Run RStudio
Hands-on: Launch RStudio
Depending on which server you are using, you may be able to run RStudio directly in Galaxy. If that is not available, RStudio Cloud can be an alternative.
Click on the Terminal tab (at the top of the Rstudio window)
Execute the command: conda install r-ape
This will install the ape package into the environment of the running session.
When the previous step has finished, switch back to the Console tab and run the command
library("ape")
You might get a warning about R versions, which you can ignore.
Import the RAxML *“Best-scoring ML tree into the Rstudio session
treefile<-gx_get(###)
where you have to replace ### with the dataset number of the RAxML output in your history.
Plot the RAxML output
We have now set up our R session and are ready to start exploring the tree interactively.
As a first step we would like to parse the tree information from the imported dataset and plot the tree.
Comment
If you are experienced with R, you are encouraged also to start playing with all the code from here on, to modify it, and to explore the numerous phylogenetics packages and functions available in R.
Hands-on: Parse the tree and plot it
# Parse the treetree<-read.tree(treefile)# Plot itplot(tree)
Question
Take a look at the tree generated by RAxML. Is it rooted or unrooted? What is the strain far apart from all other strains?
The tree is unrooted, and the outlier strain is M. canettii, our outgroup. The much longer branch leading to this strain shows that many SNPs separate M. canettii from the common ancestor of the MTBC.
Root the tree
To make the phylogeny more interpretable, we will now root the tree using the M. canettii strain as the outgroup, then exclude that strain, such that patterns within the MTBC become clearer.
Hands-on: Root the tree and drop its outgroup
# Root the treetree_rooted<-root(tree,"ERR313115")# Remove the outgroup to make distances within MTB clearertree_rooted<-drop.tip(tree_rooted,"ERR313115")tree_rooted$root.edge<-0.005plot(tree_rooted,root.edge=T,cex=0.6)
This already looks better, the tree topology stands out more clearly now, and we can identify groups of closely related strains.
Create tree with lineage as tip label instead of strain name
A first piece of information we now want to add to the phylogeny is to which lineage the strains belong. This will allow us to assess whether our tree is consistent with the known phylogeny of the MTBC, shown in Figure 1A, and to visualize which lineages are present in our sample. The information to which lineage a strain belongs can be found in the output of TB-profiler, as explained in the tutorial on TB variant analysis.
Hands-on: Generate a tree with lineage labels
# Assign lineages to samples, as identified by TB-profilermtbc_lineages<-c("ERR181435"="L7","ERR313115"="canettii","ERR551620"="L5","ERR1203059"="L5","ERR2659153"="orygis","ERR2704678"="L3","ERR2704679"="L1","ERR2704687"="L6","ERR5987300"="L2","ERR5987352"="L4","ERR6362078"="L2","ERR6362138"="L2","ERR6362139"="L4","ERR6362156"="L2","ERR6362253"="L2","ERR6362333"="L2","ERR6362484"="L4","ERR6362653"="L2","SRR998584"="L5","SRR13046689"="bovis")# Replace the tree labelstree_lineages<-tree_rootedtree_lineages$tip.label<-as.character(mtbc_lineages[tree_rooted$tip.label])# Define some colors for the lineagescolor_code_lineages=c(L1="#ff00ff",L2="#0000ff",L3="#a000cc",L4="#ff0000",L5="#663200",L6="#00cc33",L7="#ede72e",bovis="black",orygis="black")pal_lineages<-as.character(color_code_lineages[tree_lineages$tip.label])# Plot the old and new tree version next to each otherpar(mfrow=c(1,2))plot(tree_rooted,cex=0.7,root.edge=TRUE)plot(tree_lineages,cex=0.8,tip.color=pal_lineages,root.edge=TRUE)
Question
Looking at the different lineages present in the tree, does our phylogeny make sense? Or asked differently: does our phylogeny show the same branching patterns between lineages as the established phylogeny in Fig. 1A?
There is indeed a problem with our phylogeny: one L2 and one L5 strain do not cluster with the other strains of these lineages. Instead, they appear near the root of the tree, with very short (ERR5987300) to non-existent (ERR1203059) branches. Other parts of the tree are consistent with Fig. 1A, suggesting that we can focus our first round of troubleshooting on these two strains.
First round of exercises
Question: Exercise 1
An important part of bioinformatics consists in trying to find out whether a surprising observation has biological significance — or reflects a mistake somewhere in the numerous steps leading to the result. Have we just discovered two new lineages of MTB, or did we commit a stupid mistake? To find out, take a look a the TB-profiler and the VCF files for the two strange strains, which should be present in your Galaxy history if you’ve followed the previous tutorial. Compare them with “normal” strains. Do you notice something?
The VCF files hold an important hint to explain our puzzling observation: ERR1203059.vcf contains not a single SNP, ERR5987300.vcf only 81 SNPs. By contrast, the other strains have between 750 and 1250 SNPs. What happened here? To find out, we would have to take a closer look at the steps leading from BAM to VCF files. One possibility is that the sequencing depth for these samples was so low that most SNPs were filtered out because they did not pass the quality filtering.
Question: Exercise 2
In the tutorial on transmission clusters two clusters of samples got identified and are reproduced below. How do these clusters show up in the phylogenetic tree? What additional information does the tree contain?
Sample
Cluster_id
DR profile
Clustering
ERR5987352
10
Pre-MDR
Clustered
ERR6362484
10
Pre-MDR
Clustered
ERR6362138
12
MDR
Clustered
ERR6362156
12
Pre-XDR
Clustered
ERR6362253
12
MDR
Clustered
Clusters 10 and 12 appear as clades of closely related strains in the phylogeny: cluster 12 being part of lineage 2, cluster 10 of lineage 4. The phylogeny additionally reveals that cluster 12 is part of a larger clade of rather closely related lineage 2 strains. While clustering with a fixed SNP threshold produces a binary outcome (clustered/unclustered), the phylogeny reveals the gradual nature of relatedness. With a more permissive SNP threshold for clustering, or a different pipeline to call SNPs, we might well identify a larger cluster of L2 strains.
Map a trait onto the tree
Phylogenies are particularly useful when combined with additional information. For our 19 MTB strains, for example, we might know such things as the country of origin, the sampling date, or various phenotypes determined in the lab, for example the virulence of the strains in an animal model. By mapping this additional information onto the phylogeny, we can gain insights into how, where and when these traits evolved.
For our 20 samples, a trait you previously identified (if you have been doing the tutorial on transmission clusters, is the DR profile. Let us map this trait onto the tree and see if we can learn something from the observed patterns.
Hands-on: Generate a tree with DR profiles as labels
# Same as before, but with DR profiles instead of lineagesmtbc_dr<-c("ERR181435"="Sensitive","ERR313115"="Sensitive","ERR551620"="MDR","ERR1203059"="Sensitive","ERR2659153"="Sensitive","ERR2704678"="Sensitive","ERR2704679"="Sensitive","ERR2704687"="Sensitive","ERR5987300"="PreXDR","ERR5987352"="PreMDR","ERR6362078"="MDR","ERR6362138"="MDR","ERR6362139"="PreMDR","ERR6362156"="PreXDR","ERR6362253"="MDR","ERR6362333"="PreXDR","ERR6362484"="PreMDR","ERR6362653"="MDR","SRR998584"="Sensitive","SRR13046689"="Other")tree_dr<-tree_rootedtree_dr$tip.label<-as.character(mtbc_dr[tree_rooted$tip.label])color_code_dr=c(Sensitive="#ff00ff",PreXDR="#0000ff",PreMDR="#a000cc",MDR="#ff0000",Other="#663200")pal_dr<-as.character(color_code_dr[tree_dr$tip.label])par(mfrow=c(1,2))plot(tree_rooted,cex=0.7,root.edge=TRUE)plot(tree_dr,cex=0.8,tip.color=pal_dr,root.edge=TRUE)
Exercises continued
Question: Exercise 3
In the previous tutorial on clustering, you have come across the hypothesis that unclustered cases of DR represent de novo evolution of DR, while clustered cases of DR represent instances of DR transmission. Looking at lineage 2 in the phylogeny above, does this hypothesis hold? How many times would MDR have evolved independently in lineage 2? Is there an alternative explanation for the prevalence of MDR in lineage 2?
MDR would have evolved three times according to the clustering perspective mentioned above: once in cluster 12, once in ERR6362078, and once in ERR6362653. The phylogeny suggest a simpler alternative: MDR could have been already present in the common ancestor of the six L2 strains in our sample; it could have evolved only once, along the long branch leading from the split from lineage 3 to the most recent common ancestor of the six samples. This picture, however, might change with a more extensive sampling of lineage 2. Six samples are hardly sufficient to make claims about the prevalence and evolution of MDR in lineage 2. As for the interpretation of clustering, sampling design is crucial for the interpretation of phylogenies and should always be kept in mind in order to avoid overinterpretation.
Date the phylogeny (advanced)
As a last exercise, we are going to put a timescale on our phylogeny, assuming that mutations accumulate in a regular, clock-wise manner. In the tree, phylogenetic distance = time*rate. As we know the phylogenetic distance, we can get an estimate of time by assuming a mutation rate. This sounds simple in theory, but will require some big assumptions:
As noted above, the starting alignment only contains variable positions, phylogenetic distances in the trees are thus overestimated. To correct for this, we assume that all other sites in the genome are invariant, and rescale branch lengths according to rescaled branch lengths = (branch lengths * alignment length) / genome size, as in Menardo et al. 2019.
We assume that all strains were sampled at time point 0, in the present.
We assume a mutation rate of 2.01e-10 mutations per site per generation (Ford et al. 2013)
To translate generations into years, we assume 200 generations per year.
Hands-on: Rescale branches
# Rescale branch lengths (here called edge lenghts)genome_size=4411532alignment_length=18077invariant_sites=genome_size-alignment_lengthtree_rescaled<-tree_rootedtree_rescaled$edge.length<-((tree_rescaled$edge.length*alignment_length)/genome_size)tree_rescaled$root.edge<-((tree_rescaled$root.edge*alignment_length)/genome_size)par(mfrow=c(1,2))plot(tree_rooted,cex=0.7,root.edge=T,main="original")axisPhylo()plot(tree_rescaled,cex=0.7,root.edge=T,main="rescaled")axisPhylo()dev.off()
Look at the scale bars of the two trees. Accounting for invariable sites has a huge effect on phylogenetic distances!
Hands-on: Remove outliers, perform dates estimation and plot with time-scale
# Let's also remove the two outlier strains, they would cause troubles and are anyway uselesstree_rescaled<-drop.tip(tree_rescaled,c("ERR1203059","ERR5987300"))# Estimate dates: translate phylogenetic distance into years by assuming a mutation rate and the number of generations per yearmutation_rate=2.01e-10generations_per_year=200## Ape has a function, estimate.dates(), to date a tree by assuming a specific mutation ratenode.date<-estimate.dates(tree_rescaled,node.dates=rep(0,length(tree_rescaled$tip.label)),# set sampling dates to 0mu=(mutation_rate*generations_per_year)# mutation rate per year)tree_rescaled$edge.length<-node.date[tree_rescaled$edge[,2]]-node.date[tree_rescaled$edge[,1]]tree_rescaled$tip.label<-as.character(mtbc_lineages[tree_rescaled$tip.label])plot(tree_rescaled,cex=0.6,main="Dated phylogeny (years)")axisPhylo()
Final exercise
Question: Exercise 4 (advanced)
What could be the problem with the assumption that all sites in the reference genome which do not appear in our SNP alignment are invariable?
Imagine that a recent breakthrough study has found that the mutation rate in MTB is 10 times higher than we assumed. How would this change the estimated dates?
About 5 % of the MTB reference genome consists of repetitive or otherwise complicated regions where mapping and SNP calling cannot be done reliably. Most SNP calling pipelines exclude such regions, also the one used in this course. Rather than to assume that these regions are invariant, we should ignore them in our calculations. By not doing this, genomes seem more similar than they are; we underestimate phylogenetic distances.
A rate 10 times higher implies that there will be 10 times more mutations observed in the same time span, or, the other way around, that it will take a time span 10 times shorter to observe the same number of mutations. The timescale of the phylogeny would thus shift one order of magnitude, to hundreds rather than thousands of years. As this example shows, there are considerable uncertainties associated with molecular dating. This is also true for more sophisticated methods (see Menardo et al. 2019 for a recent discussion of molecular dating with MTB).
Conclusion
Any phylogenetic analysis requires careful exploration of results and an understanding of its limitations to avoid overinterpretaion. The combination of Galaxy and R accessed through an interactive tool is very powerful because the Galaxy platform provides the compute resources and tools necessary to calculate the phylogenetic tree, and offers straightforward access to RStudio. The flexibility of R and its packages then lets you take a deep dive into the results.
Resources
To develop a deeper understanding of phylogenetic trees, there is no better way than estimating phylogenies yourself — and work through a book on the topic in your own mind’s pace.
Books
Phylogenetics in the genomics era, 2020. An open access book covering a variety of contemporary topics.
Tree Thinking, 2013, by David A. Baum & Stacey D. Smith
Ford, C. B., R. R. Shah, M. K. Maeda, S. Gagneux, M. B. Murray et al., 2013 Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis. Nature Genetics 45: 784–790. 10.1038/ng.2656
Eldholm, V., J. H.-O. Pettersson, O. B. Brynildsrud, A. Kitchen, E. M. Rasmussen et al., 2016 Armed conflict and population displacement as drivers of the evolution and dispersal of \lessi\greaterMycobacterium tuberculosis\less/i\greater. Proceedings of the National Academy of Sciences 113: 13881–13886. 10.1073/pnas.1611283113
Goig, G. A., S. Blanco, A. L. Garcia-Basteiro, and I. Comas, 2018 Contaminant DNA in bacterial sequencing experiments is a major source of false genetic variability. 10.1101/403824
Menardo, F., S. Duchêne, D. Brites, and S. Gagneux, 2019 The molecular clock of Mycobacterium tuberculosis (R. Biek, Ed.). PLOS Pathogens 15: e1008067. 10.1371/journal.ppat.1008067
Zhou, Z., J. Charlesworth, and M. Achtman, 2021 HierCC: a multi-level clustering scheme for population assignments based on core genome MLST (J. Kelso, Ed.). Bioinformatics 37: 3645–3646. 10.1093/bioinformatics/btab234
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Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012
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title = "Tree thinking for tuberculosis evolution and epidemiology (Galaxy Training Materials)",
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doi = {10.1371/journal.pcbi.1010752},
url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
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month = {jan},
publisher = {Public Library of Science ({PLoS})},
volume = {19},
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pages = {e1010752},
author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
editor = {Francis Ouellette},
title = {Galaxy Training: A powerful framework for teaching!},
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Congratulations on successfully completing this tutorial!
You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.
Christoph Stritt
Daniela Brites
Galo A. Goig
Wolfgang MaierQuestions:
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