IgPhyML is easiest to use when run indirectly through the Change-O program
by specifying the
Most of 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
If these aren’t possible, see IgPhyML standalone operation
To view all options for BuildTrees , run the command:
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 rest of the website.
Build trees and estimate model parameters¶
Move to the
examples subfolder and run:
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
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
--nproc option. Subsampling using the
--sample option in isn’t
strictly necessary, but IgPhyML will run slowly when applied to large datasets.
--collapse flag is used to collapse identical sequences. This is
highly recommended because identical sequences slow down calculations without
affecting likelihood values in IgPhyML.
The output file of the above command can be read using the
After opening an
R session, enter the following commands. Note that
when using the Docker container, you’ll need to run
plotting the tree to create a pdf plot in the
library(alakazam) library(igraph) db = readIgphyml("ex_igphyml-pass.tab") #plot largest lineage tree plot(db$trees[],layout=layout_as_tree) #show HLP10 parameters print(t(db$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"