NAME¶
roguenarok-single - implements the RogueNaRok algorithm for rogue taxon
identification (single core version)
SYNOPSIS¶
roguenarok-single -i <bootTrees> -n <runId> [-x
<excludeFile>] [-c <threshold>] [-b] [-s <dropsetSize>] [-w
<workingDir>] [-h]
OPTIONS¶
OBLIGATORY¶
- -i <bootTrees>
- A collection of bootstrap trees.
- -n <runId>
- An identifier for this run.
OPTIONAL¶
- -t <bestKnownTree>
- If a single best-known tree (such as an ML or MP tree) is provided,
RogueNaRok optimizes the bootstrap support in this best-known tree (still
drawn from the bootstrap trees). The threshold parameter is ignored.
- -x <excludeFile>
- taxa in this file (one taxon per line) will not be considered for
pruning.
- -c <threshold>
- A threshold or mode for the consensus tree that is optimized. Specify a
value between 50 (majority rule consensus) and 100 (strict consensus) or
MR (for the extended majority rule consensus). Note that rogue taxa
identified with respect to different thresholds can vary substantially.
DEFAULT: MR consensus
- -b
- Instead of trying to maximize the support in the consensus tree, the
RogueNaRok will try to maximize the number of bipartition in the final
tree by pruning taxa. DEFAULT: off
- -L <factor>
- a weight factor to penalize for dropset size. Factor=1 is Pattengale's
criterion. The higher the value, the more conservative the algorithm is in
pruning taxa. DEFAULT: 0.0 (=RBIC)
- -s <dropsetSize>
- maximum size of dropset per iteration. If dropsetSize == n, then
RogueNaRok will test in each iteration which tuple of n taxa increases
optimality criterion the most and prunes taxa accordingly. This improves
the result, but runtimes will increase at least linearly. DEFAULT: 1
- -w <workDir>
- A working directory where output files are created.
- -h
- This help file.
EXAMPLES¶
- roguenarok-single -i <bootstrapTreeFile> -n run1
AUTHOR¶
RogueNaRok was released by Andre J. Aberer in 2011-10-25.
This manpage was written by Andreas Tille for the Debian
distribution and can be used for any other usage of the program.
