Standalone operation

While IgPhyML is easiest to use when run indirectly through the Change-O program BuildTrees, this is not always possible or desirable. This section details how IgPhyML may be run directly as a standalone program. As before, this section requires IgPhyML to be installed, with the executable in your PATH variable. This is already done in the Docker image.

Model specification and confidence interval estimation are the same as when running IgPhyML from BuildTrees. The means of specifying input, however, are different.

To view all options for IgPhyML directly, run the command:

igphyml --help

Analyzing a single lineage

In its most basic and flexible operation, IgPhyML operates on single lineage. Its only required input is an in-frame multiple sequence alignment, without any stop codons, in FASTA or PHYLIP format. The model must be specified. The most basic is GY94, which is fast but doesn’t account for SHM context sensitivity. The HLP19 model corrects for SHM context sensitivity but is slower requires the root sequence be specified:

cd examples

#Estimate parameters and topology using GY94 model
igphyml -i example.fasta -m GY --run_id gy

#Estimate parameters and topology using HLP19 model
igphyml -i example.fasta -m HLP --root V4-59 --run_id hlp

It is also possible to use a fixed tree topology:

igphyml -i example.fasta -m HLP --root V4-59 -u example.fasta_igphyml_tree_gy.txt

Or estimate separate \(\omega\) values for CDR and FWR partitions:

igphyml -i example.fasta -m HLP --root V4-59 --partfile part.example.txt

Generally, all the following features of repertoire-wide phylogenetic analysis can also be performed on single lineages by specifying input files in the above manner.

Analyzing repertoires

These 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 rest of the manual.

Convert Change-O files into IgPhyML inputs

Move to the examples subfolder and run, in order:

BuildTrees.py -d example.tab --outname ex --log ex.log --collapse

This will create the directory ex and the file ex_lineages.tsv. Each ex/<clone ID>.fasta contains the IMGT mutliple sequence alignemt for a particular clone, and each ex/<clone ID>.part.txt file contains information about V and J germline assignments, as well as IMGT unique numbering for each site. The file ex.log will contain information about whether or not each sequence was included in the analysis. The file ex_lineages.tsv is the direct input to IgPhyML. Each line represents a clone and shows the multiple sequence alignment, starting tree topology (N if ignored), germline sequence ID in alignment file, and partition file (N if ignored). These repertoire files start with the number of lineages in the repertoire, and lineages are arranged from most to least number of sequences. Here, it is important to not use --igphyml or --clean all to prevent IgPhyML from being run by BuildTrees, and to prevent these intermediate files from being deleted.

Build lineage trees using the GY94 model

This option is fast and makes good starting topologies, but doesn’t correct for SHM mutation biases. Use the --outrep option to make a modified

igphyml --repfile ex_lineages.tsv -m GY --outrep ex_lineages_gy.tsv --run_id gy

Build lineage trees using the HLP19 model with GY94 starting trees

This option is slower but corrects for mutation biases of SHM:

#HLP topology, branch lengths, and parameters (slow)
igphyml --repfile ex_lineages_gy.tsv -m HLP --threads 1

#GY94 topology, HLP19 branch lengths and parameters (faster)
igphyml --repfile ex_lineages_gy.tsv -m HLP --optimize lr --threads 1

Both of these can be parallelized by modifying the --threads <thread count> option. Trees files are listed as ex/<clone id>.fasta_igphyml_tree.txt, and can be viewed with most tree viewers (I recommend FigTree). Parameter estimates are in ex_lineages.tsv_igphyml_stats.txt.

Controlling output format

Alternatively, run using --oformat tab to create input readable by the readIgphyml function of Alakazam.:

#Output can be read using readIgphyml function
igphyml --repfile ex_lineages_gy.tsv -m HLP --optimize lr --threads 1 --oformat tab

Open an R session, and run the following commands. Note the results are the same as in the quickstart example

library(alakazam)
library(igraph)

db = readIgphyml("ex_lineages_gy.tsv_igphyml_stats.tab")

#plot largest lineage tree
plot(db$trees[[1]],layout=layout_as_tree)

#show HLP10 parameters
print(t(db$param[1,]))
CLONE         "REPERTOIRE"
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.