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Subcommand: random placements

Lucas Czech edited this page Jan 4, 2022 · 7 revisions

Create a set of random phylogenetic placements on a given reference tree.

Usage: gappa simulate random-placements [options]

Options

Input
--reference-tree Required. TEXT
File containing a reference tree in newick format.
--pquery-count Required. UINT=0
Number of pqueries to create.
--subtree INT=-1
If given, only generate random placements in one of the subtrees of the root node. For example, if the root is a trifurcation, values 0-2 are allowed.
Output
--out-dir TEXT=.
Directory to write output files to.
--file-prefix TEXT
File prefix for output files. Most gappa commands use the command name as the base name for file output. This option amends the base name, to distinguish runs with different data.
--file-suffix TEXT
File suffix for output files. Most gappa commands use the command name as the base name for file output. This option amends the base name, to distinguish runs with different data.
--compress FLAG
If set, compress the output files using gzip. Output file extensions are automatically extended by .gz.
Global Options
--allow-file-overwriting FLAG
Allow to overwrite existing output files instead of aborting the command.
--verbose FLAG
Produce more verbose output.
--threads UINT
Number of threads to use for calculations.
--log-file TEXT
Write all output to a log file, in addition to standard output to the terminal.

Description

The command creates a set of random phylogenetic placmeents on a given reference tree. It generates as many pqueries as given by --pquery-count, named pquery_n, with each having a random number of placements on nearby branches of a randomly chosen edge.

The output file is named random-placements.jplace, potentially using the --file-prefix and --file-suffix if provided.

Citation

When using this method, please do not forget to cite

Lucas Czech, Pierre Barbera, Alexandros Stamatakis. Genesis and Gappa: Processing, Analyzing and Visualizing Phylogenetic (Placement) Data. Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa070

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