rf-py

RF Opt Algorithm as stand-alone program in Python. RF-Optimizer is a program for evolutionary analysis of large datasets. Given a large evolutionary tree topology, it uses a large set of smaller tree topologies to analyse the plausibility of the large tree and proposes corrections as a listing of "weak" taxa. A high score for a taxa set stands for low plausibility.

Dependencies

• Python3+
• bitarray module
• numpy module

Script dependencies

• Python2.7
• ETE2

Current Workflow (will be edited, before production-ready)

• Use RAxML fast plausibility to extract all small bipartitions and induced bipartitions

raxmlHPC -f R -m GTRCAT -t largetree -z referencetrees -n T1

• This will automatically save the results in a file called bips.txt and the tree names in a file called names.txt

• In rf-opt.py, edit the settings

rf_optimize(starting_tree, end_tree, "path/to/your/raxml-bips")

# Example usage:
rf_optimize(10000, 10020, "bips.txt")

• Start the algorithm with

python rf-opt.py

• All results will be saved in a file called scoring.txt

Scripts

• read_drops.py takes as input scoring.txt, names.txt and returns you the n best dropsets of this iteration
• reduce_stbase.py takes the trees in stbase and ignores the first n of them (used for testing)
• delete_nodes.py takes a set of trees and deletes a dropset (works currently only with Python2.7 due to ETE dependency)