gctree 3.3.0

phylogenetic inference of genotype-collapsed trees

gctree

Implements phylogenetic inference for data with repeated sequences, as described in:

DeWitt, Mesin, Victora, Minin and Matsen, Using genotype abundance to improve phylogenetic inference, Molecular Biology and Evolution.

Note: full documentation of the gctree package is available at: https://matsengrp.github.io/gctree

This README provides info on use of SCons pipelines that wrap the base gctree package. Note: SCons pipelines are deprecated. They do not support more recent functionality, and may be removed in a future release.

Installation

Linux/MacOS

Base package install

See the docs install page to install the gctree Python package and its command line interface. Skip this if you are using the SCons pipelines (see below).

SCons pipeline install

SCons pipelines can be used to for end-to-end phylogenetic inference from sequence data. These must be run from the repo directory after cloning.

0. For installing dependencies, conda environment management is recommended. First install conda or miniconda.
1. Create a python 3 conda environment called gctree from the included environment file:

   conda env create -f environment.yml
   

2. Activate the environment:

   conda activate gctree
   

Pipeline quick start

All commands should be issued from within the gctree repo directory.

inference

• input file: FASTA or PHYLIP file containing a sequence for each observed individual/cell, and an additional sequence containing the ancestral genotype of all observed sequences (used for outgroup rooting).
• run inference:

  scons --inference --outdir=<output directory path> --input=<input FASTA or PHYLIP file> --root_id=<id of ancestral sequence in input file>
  

• description of inference output files: After the inference pipeline has completed, the output directory will contain the following output files:
  • <input file>.idmap: text file mapping collapsed sequenced ids to cell ids from the original input file
  • <input file>.counts: text file mapping collapsed sequenced ids to their abundances
  • <input file>.phylip: phylip alignment file of collapsed sequences for computing parsimony trees
  • dnapars/: directory of parsimony tree output from PHYLIP's dnapars
  • gctree.inference.*.svg: rendered tree images for each of the parsimony trees
  • gctree.inference.abundance_rank.pdf: histogram of genotype abundances
  • gctree.inference.likelihood_rank.pdf: rank plot of gctree likelihoods for the parsimony trees
  • gctree.inference.log: log file containing parameter fits, numerical likelihood results, and any other program messages
  • gctree.inference.parsimony_forest.p: a python pickle file containing the parsimony trees as CollapsedTree objects

simulation

scons --simulate  --outdir=<output directory path> --N=<integer population size to simulate>

Pipeline example

run gctree inference pipeline on the included FASTA file

• Example input data set

  See the quickstart docs page for a description of these example data.

• Run inference

  From within the gctree repository directory:

  scons --inference --input=example/150228_Clone_3-8.fasta --outdir=_build --id_abundances --root_id=GL --jobs=2
  

  This command will produce output in subdirectory _build/.

• Explanation of arguments

  --outdir=_build specifies that results are to be saved in directory _build/ (which will be created if it does not exist)

  --id_abundances flag means that integer sequence IDs in the input file are interpreted as abundances. The example input FASTA includes a sequence with id "17".

  --root_id=GL indicates that the root root sequence has id "GL" in the input FASTA. This sequence is the germline sequence of the V gene used in the V(D)J rearrangement that defines this clonal family.

  --jobs=2 indicates that 2 parallel processes should be used

  If running on a remote machine via ssh, it may be necessary to provide the flag --xvfb which will allow X rendering of ETE trees without X forwarding.

Inference pipeline

scons --inference ...

required arguments

--input=[path] path to FASTA or PHYLIP input alignment

--outdir=[path] directory for output (created if does not exist)

--root_id=[string] ID of root sequence in input file used for outgroup rooting, default 'root'. For BCRs, we assume a known root V(D)J rearrangemnt is an additional sequence in our alignment, regardless of whether it was observed or not. This ancestral sequence must appear as an additional sequence. For applications without a definite root state, an observed sequence can be used to root the tree by duplicating it in the alignment and giving it a new id, which can be passed as this argument.

optional arguments

--colorfile=[path] path to a file of plotting colors for cells in the input file. Example, if the input file contains a sequence with ID cell_1, this cell could be colored red in the tree image by including the line cell_1,red in the color file.

--bootstrap=[int] boostrap resampling, and inference on each, default no bootstrap

--id_abundances if this flag is set, parse input IDs that are integers as indicating sequence abundance. Otherwise each line in the input is assumed to indicate an individual (non-deduplicated) sequence. NOTE: the example input FASTA file example/150228_Clone_3-8.fasta requires this option.

Simulation pipeline

scons --simulation ...

required arguments

--N=[int] populaton size to simulate. Note that N=1 is satisfied before the first time step, so this choice will return the root with no mutation.

--outdir=[path] directory for output (created if does not exist)

optional arguments

--root=[string] DNA sequence of root sequence from which to begin simulating, a default is used if omitted

--mutability=[path] path to S5F mutability file, default 'S5F/mutability'

--substitution=[path] path to S5F substitution file, default 'S5F/substitution'

--lambda=[float, float, ...] values for Poisson branching parameter for simulation, default 2.0

--lambda0=[float, float, ...] values for baseline mutation rate, default 0.25

--T=[int] time steps to simulate (alternative to --N)

--nsim=[int] number of simulation of each set of parameter combination, default 10

--n=[int] number of cells to sample from final population, default all

Optional arguments for both inference and simulation pipelines

--jobs=[int] number of parallel processes to use

--srun should cluster jobs be submitted with Slurm's srun?

--frame=[int] codon reading frame, default None

--quick less thorough parsimony tree search (faster, but smaller parsimony forest)

--idlabel label sequence IDs on tree, and write FASTA alignment of distinct sequences. The mapping of the unique names in this FASTA file to the cell names in the original input file can be found in the output file with suffix .idmap

--xvfb needed for X rendering in on remote machines. Try setting this option if you get the error:ETE: cannot connect to X server

--dnaml include results for maximum likelihood tree inference using dnaml from the PHYLIP package