IgPhyML lineage tree analysis

IgPhyML is a program designed to build phylogenetic trees and test evolutionary hypotheses regarding B cell affinity maturation.

The biology of B cell somatic hypermutation (SHM) violates important assumptions in most standard phylogenetic substitution models; further, while most phylogenetics programs are designed to analyze single lineages, B cell repertoires typically contain thousands of lineages. IgPhyML addresses both of these issues by implementing substitution models that correct for the context-sensitive nature of SHM, and combines information from multiple lineages to give more precisely estimated repertoire-wide model parameter estimates.

An in-depth description of IgPhyML installation and usage can be found at the IgPhyML website.

Quick start

Once installed, IgPhyML can be run through BuildTrees by specifying the --igphyml option. IgPhyML is easiest to run through the Immcantation Docker image. If this is not possible, these instructions require Change-O 0.4.6 or higher, Alakazam 0.3.0 or higher, and IgPhyML to be installed, with the executable in your PATH variable.

The following commands should work as a first pass on many reasonably sized datasets, but if you really want to understand what’s going on or make sure what you’re doing makes sense, please check out the IgPhyML website.

Build trees and estimate model parameters

Download the IgPhyML repository, move to the examples folder, and run BuildTrees:

# Clone IgPhyML repository to get example files
git clone https://bitbucket.org/kleinstein/igphyml

# Move to examples directory
cd igphyml/examples

# Run BuildTrees
BuildTrees.py -d example.tsv --outname ex --log ex.log --collapse \
    --sample 3000 --igphyml --clean all --nproc 1

This command processes an AIRR-formatted dataset of BCR sequences that have been clonally clustered with germlines reconstructed. It then quickly builds trees using the GY94 model and, using these fixed topologies, estimates HLP19 model parameters. This can be sped up by increasing the --nproc option. Subsampling using the --sample option in isn’t strictly necessary, but IgPhyML will run slowly when applied to large datasets. Here, the --collapse flag is used to collapse identical sequences. This is highly recommended because identical sequences slow down calculations without affecting likelihood values in IgPhyML.

Visualize results

The output file of the above command can be read using the readIgphyml function of Alakazam. After opening an R session in the examples subfolder, enter the following commands. Note that when using the Docker container, you’ll need to run dev.off() after plotting the tree to create a pdf plot in the examples directory:


db = readIgphyml("ex_igphyml-pass.tab")

# Plot largest lineage tree

# Show HLP10 parameters
NSEQ          "4"
NSITE         "107"
TREE_LENGTH   "0.286"
LHOOD         "-290.7928"
KAPPA_MLE     "2.266"
OMEGA_FWR_MLE "0.5284"
OMEGA_CDR_MLE "2.3324"
WRC_2_MLE     "4.8019"
GYW_0_MLE     "3.4464"
WA_1_MLE      "5.972"
TW_0_MLE      "0.8131"
SYC_2_MLE     "-0.99"
GRS_0_MLE     "0.2583"
map to buried treasure

Lineage tree of example clone.

To visualize a larger dataset with bigger trees, and bifurcating tree topologies, again open an R session in the examples directory:


db = readIgphyml("sample1_igphyml-pass.tab",format="phylo")

# Plot largest lineage tree

Phylo-formatted lineage tree of a larger B cell clone.