PIVO: Phylogeny by IteratiVe Optimization
A Practical Algorithm for Ancestral Rearrangement Reconstruction

This page contains supplementary material to our papers:

• Jakub Kováč, Broňa Brejová, Tomáš Vinař. A Practical Algorithm for Ancestral Rearrangement Reconstruction. WABI 2011.
• Albert Herencsár, Broňa Brejová. An Improved Algorithm for Ancestral Gene Order Reconstruction. ITAT 2014.

The Hemiascomycetes mtDNA Dataset

• When using this dataset, please cite Valach et al.: Evolution of linear chromosomes and multipartite genomes in yeast mitochondria. NAR, 2011.
• Phylogenetic tree
• Gene orders ($ denotes end of a linear chromosome, @ a circular chromosome, multiple forms shown for species with recent duplications)
• Gene names
• Species names
• Predicted history (each species name is followed by the number of DCJ operations on the branch to parent)

The Campanulaceae cpDNA Dataset

• Cosner et al.: An empirical comparison of phylogenetic methods on chloroplast gene order data in Campanulaceae.
• Phylogenetic tree
• Gene orders
• Predicted history

PIVO software

Original software, written in Python.

• Pivo
• run python genr.py T G n to generate n histories starting from a random one; T is a file with the input tree, G is a file with the input genomes; histories are generated into directory m
• python genm.py T G n p works similarly, but every p-th history it starts with a history similar to the currently best history in m/.

PIVO2 software

Improved version with randomization, written in Java.

• Pivo2
• For usage, see comments in file Pivo2.java