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tree-based orthology inference

Project description

ppp_logotype

About

Orthologs are genes that are related through a speciation event, while paralogs are genes that are related through a gene duplication event. Accurate identification of orthologs is a prerequisite for phylogenomics, since including genes that diverged because of a gene duplication event for species tree inference can cause an erroneous inference of speciation nodes, due to disparencies between individual gene trees and the species tree. Unfortunately, contaminants present in even a single taxon can cause a tree-based orthology inference method to erroneuosly infer paralogy and unnecessarily exclude sequences.

PhyloPyPruner is a Python package for phylogenetic tree-based orthology inference, using the species overlap method. It uses trees and alignments inferred from the output of a graph-based orthology inference approach, such as OrthoMCL, OrthoFinder or HaMStR, in order to obtain sets of sequences that are 1:1 orthologous. In addition to algorithms seen in pre-existing tree-based tools (for example, PhyloTreePruner, UPhO, Agalma or Phylogenomic Dataset Reconstruction), this package provides new methods for reducing potential contamination.

proteomes2orthologs

Figure 1. A rough overview of a tree-based orthology inference approach.

Quick installation

The easiest way to install PhyloPyPruner is by using the package manager for Python, pip:

pip install phylopypruner # install for all users
pip install --user phylopypruner # install for the current user only

Once installed, the program is located within $HOME/.local/bin. Depending on your OS, you might have to add the directory to your $PATH to avoid typing the entire path. Once in your path, you run the program like this:

phylopypruner

Documentation

  1. About PhyloPyPruner
  2. Tutorial
  3. Installation
  4. Input data
  5. Output files
  6. Methods
  7. Options

Cite

Our manuscript is still in preparation, it will be posted here once a preprint of the article is available.

© Kocot Lab 2019

Project details


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