HogProf

• HogProf is an extensible and tunable approach to phylogenetic profiling using orthology data. It is powered by minhash based datastructures and computationally efficient.
• Still under major development and may change

Features

• Using orthoxoml files and a taxonomy calculated enhanced phylogenies of each family
• These are transformed into minhash signatures and a locally sensitive hashing forest object for search and comparison of profiles
• Taxonomic levels and evolutionary event types ( presence, loss, duplication ) can have custom weight in profile construction
• Optimization of weights using machine learning

If you run into any problems feel free to contact me at dmoi@unil.ch

Quickstart

to install from github

$ git clone https://github.com/DessimozLab/HogProf.git
$ pip install -r pipreqs.txt .

or to install from pypi

$ pip install hogprof

lets get a current version of the OMA hdf5 file and GAF. This will alow us to use the HOGs and study the functional enrichment of our search results.

$ cd ../..
$ mkdir YourOmaDirectory
$ cd YourOmaDirectory
$ wget https://omabrowser.org/All/OmaServer.h5
$ wget https://omabrowser.org/All/oma-go.txt.gz

We also need to make a location to store our pyprofiler databases

$ cd ..
$ mkdir YourHogProfDirectory

Ok. We're ready! Now let's compile a database containing all HOGs and our desired taxonomic levels using default settings. Launch the lshbuilder. dbtypes available on the command line are : all , plants , archaea, bacteria , eukarya , protists , fungi , metazoa and vertebrates. These will use the NCBI taxonomy as a tree to annotate events in different gene family's histories.

$python lshbuilder.py --outpath YourHogProfDirectory --dbtype all --OMA YourOmaDirectory/OmaServer.h5 --nthreads numberOfCPUcores         

This should build a taxonomic tree for the genomes contained in the release and then calculate enhanced phylogenies for all HOGs in OMA.

Once the database is completed it can be interogated using a profiler object. Construction and usage of this object should be done using a python script or notebook. This shown in the example notebook searchenrich.ipynb found in the examples. Please feel free to modify it to suit the needs of your own research.