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OrthoHMM using high sensitivity and specificity Hidden Markov Models for orthology inference.

If you found OrthoHMM useful, please cite OrthoHMM: Improved Inference of Ortholog Groups using Hidden Markov Models. Steenwyk et al. 2024, bioRxiv. doi: 10.1101/2024.12.07.627370.


Performance

As of v0.2.0, OrthoHMM ships a built-in profile HMM + k-mer prefilter search engine that replaces the phmmer subprocess. A historical production CLI measurement used five bacterial proteomes (15,932 proteins) and a 32-CPU budget:

proteomes

wall time

peak process-tree RSS

orthogroups

5

7.0 s

1.36 GiB

12,995

Groups include singletons. The former 20-100 proteome table used a separate experimental driver and is not production-CLI scaling evidence. The historical optimization report retains commands, checksums and rejected experiments. This local measurement does not establish comparative speed or memory advantages. The 27-attempt matched-resource panel is now running on the local Threadripper, with 32 native CPUs and a 128-GiB limit per run. Competing analyses may distort shared-host timing by an unknown, potentially method-dependent amount; matching limits does not establish isolated speedups. Exclusions and missing repeats remain explicit, and historical measurements are not pooled with the new panel. See Performance Assessment for current benchmark scope and limitations. The legacy phmmer path is still available via --search_mode phmmer but is no longer the default.


Quick Start

This documentation describes the development checkout. As checked on 30 September 2026, the latest PyPI release is 0.2.0 and requires MCL. Installing from PyPI does not provide this checkout’s high-sensitivity or phylogeny pipeline. Use an isolated source installation for development features; use the publication reproduction guide for exact benchmark reproduction rather than an unpinned checkout. The pip commands below install the published release.

1. Install external dependencies

The development pipeline uses the built-in search engine and Leiden CPM clustering, so it has no required external bioinformatics binary. mcl is required for published v0.2.0; in the development checkout it is only required if you opt into --clustering mcl.

HMMER is optional and only required if you opt into the legacy --search_mode phmmer pipeline.


2. Install OrthoHMM

# install
pip install orthohmm
# run
orthohmm <path_to_directory_of_FASTA_files>

Below are more detailed instructions, including alternative installation methods.


1) Installation

If you are having trouble installing OrthoHMM, please contact the lead developer, Jacob L. Steenwyk, via |contactSteenwyk|_ or |blueskySteenwyk|_ to get help.

1. Install external dependencies

The development pipeline uses the built-in search engine and Leiden CPM clustering, so it has no required external bioinformatics binary. mcl is required for published v0.2.0; in the development checkout it is only required if you opt into --clustering mcl.

HMMER is optional and only required if you opt into the legacy --search_mode phmmer pipeline.

2a. Install OrthoHMM from pip

To install using pip, we recommend building a virtual environment to avoid software dependency issues. To do so, execute the following commands:

# create virtual environment
python -m venv venv
# activate virtual environment
source venv/bin/activate
# install orthohmm
pip install orthohmm

Note, the virtual environment must be activated to use orthohmm.


Install from source

Similarly, to install from source, we strongly recommend using a virtual environment. To do so, use the following commands:

# download
git clone https://github.com/JLSteenwyk/orthohmm.git
cd orthohmm/
# create virtual environment
python -m venv venv
# activate virtual environment
source venv/bin/activate
# install
make install

To deactivate your virtual environment, use the following command:

# deactivate virtual environment
deactivate

Note, the virtual environment must be activated to use orthohmm.


2b. Install OrthoHMM from source

Similarly, to install from source, we recommend using a virtual environment. To do so, use the following commands:

git clone https://github.com/JLSteenwyk/orthohmm.git
cd orthohmm/
make install

If you run into permission errors when executing make install, create a virtual environemnt for your installation:

git clone https://github.com/JLSteenwyk/orthohmm.git
cd orthohmm/
python -m venv venv
source venv/bin/activate
make install

Note, the virtual environment must be activated to use orthohmm.


2) Usage

To use OrthoHMM in its simpliest form, execute the following command:

orthohmm <path_to_directory_of_FASTA_files>