🧬 OrthoGather: a local platform for orthology-based proteome comparison and Gene Ontology enrichment

OrthoGather — compare proteomes with OrthoFinder and discover function with GOATOOLS — all in a local web application.
Download UniProt proteomes, run OrthoFinder, perform Gene Ontology enrichment, and export publication-ready figures and tables.
Requires Python 3.7.

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🧩 Overview

OrthoGather is a local web application that integrates orthology inference with functional interpretation for comparative proteome and proteomics analyses.

It enables users to:

• Download reference proteomes from UniProt and infer orthogroups using OrthoFinder.
• Explore shared and species-specific orthogroups through interactive UpSet plots.
• Perform Gene Ontology (GO) enrichment with GOATOOLS, leveraging orthogroup relationships to propagate functional information across species.
• Generate publication-ready figures and Excel tables for downstream analysis.

All analyses run locally, favouring privacy, reproducibility, and rapid iteration, and are particularly useful when working with poorly annotated or non-model organisms.

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💡 Why it helps

A substantial fraction of proteins across organisms remain under-annotated or inconsistently annotated, which complicates functional interpretation and cross-species comparisons. This is particularly limiting in proteomics experiments involving non-model species or clinical isolates.

OrthoGather addresses this gap by exploiting orthogroup relationships to transfer functional information from well-annotated proteins to those with limited annotation. By combining orthology-based comparison with Gene Ontology enrichment in a single workflow, the platform moves beyond identifying shared proteins toward inferring shared and species-specific biological functions.

Starting from any UniProt-associated proteome set, orthology provides evolutionary context, while Gene Ontology enrichment provides a functional readout — both integrated into a single, local interface.

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📖 Citation

If you use OrthoGather in your research, please cite:

Manuscript in preparation.

This section will be updated with the bioRxiv preprint and the final journal reference once available.

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🔽 Download and Installation

Prerequisites

Before installing OrthoGather, please ensure that you have:

• Git, including Git LFS

  git lfs install

• Conda or Micromamba
• A Unix-based environment (macOS, Linux, or WSL)

Clone the repository

🔽 Download and Installation

To install OrthoGather, first clone the repository and move into the project folder:

git clone https://github.com/CarlosVivasR/OrthoGather.git
cd OrthoGather

The installation process depends on your operating system. Each method automatically configures the required environment and dependencies, but you can consult installation_guide.pdf for a complete explanation of every step and additional troubleshooting details.

🧩 macOS (Intel / Rosetta)

Run the following command to install OrthoGather on macOS systems with Intel chips, or using Rosetta mode on Apple Silicon:

./install_orthogather_mac.sh

This script will:

• Check that you are running in Intel (Rosetta) mode.
• Create a dedicated environment named orthogather37 with Python 3.7.
• Install all required dependencies (Flask, GOATOOLS, OrthoFinder, etc.).
• Verify that OrthoGather is correctly installed and ready to use.

⚠️ Note: Conda must be installed on macOS before running this script (e.g., via Miniforge, Anaconda, or Miniconda). The installation guide (installation_guide.pdf) includes step-by-step instructions on how to install Conda and enable Rosetta mode properly.

Once completed, remember to open your terminal in Rosetta mode and activate the environment each time you want to use the tool:

conda activate orthogather37
python app.py

🧬 Linux / WSL (Windows Subsystem for Linux)

For Linux or WSL users, run the following command:

./install_orthogather_wsl.sh

The script will automatically:

• Detect if you are running inside a WSL or Linux environment.
• Check if Micromamba is installed — if not, it will display the command to install it manually and prompt you to restart the terminal.
• Create the environment orthogather37 with Python 3.7.
• Install all required dependencies and verify the OrthoFinder installation.
• After installation, activate the environment and start the tool:

micromamba activate orthogather37
python app.py

For a comprehensive explanation of the setup process, including dependency management, configuration tips, and troubleshooting on both macOS and WSL/Linux, please refer to the detailed installation_guide.pdf included in this repository.

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🧬 Input flows

You can start an analysis in three ways:

New Analysis

Select organisms from a UniProt catalog, download proteomes, and run OrthoFinder locally with live logs.
Creates a clean, self-contained workspace for your study.

Preselected Dataset

A ready-to-use example that lets you explore the full workflow immediately (ideal for demos or teaching).

External Data Upload

Upload a .zip with previously generated OrthoFinder results from another system to reuse completed analyses without recomputation.

Regardless of the entry point, OrthoGather focuses downstream steps on the standard Orthogroups output, keeping only what is needed for analysis and export.

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🔬 Analysis routes

Once orthogroups are available (generated or uploaded), you can take either route — or both — in any order.

1️⃣ Comparative Orthogroup Analysis

This module helps you examine the presence and distribution of orthogroups across a user-defined subset of species and, optionally, narrow the scope to proteins of interest via UniProt IDs.

Features:

• Subset by species — pick two or more species to create a focused comparison set (useful for clades, model–non-model contrasts, or custom panels).
• Two UpSet plots (via UpSetPlot):
  • Species combinations — number of orthogroups unique/shared across species combinations (presence/absence patterns).
  • Protein contribution — how many proteins each combination contributes, clarifying the magnitude behind intersections.
• Optional protein-level filter — restrict orthogroups to those containing specific UniProt IDs (e.g., differentially expressed proteins, pathway members, or candidate families).

Exports: publication-ready PNG figures and Excel/CSV tables summarizing orthogroup membership and intersections.

2️⃣ Gene Ontology Enrichment Analysis

This module turns orthogroup-level findings into functional hypotheses.

Workflow:

• GOA download (per species) and an annotation coverage panel (4-in-1) to gauge how well proteins are annotated before enrichment.
• Define sets:
  • Foreground — paste UniProt IDs for the set to be tested.
  • Background — paste UniProt IDs or use “all species with GOA” from your selection.
  • Include complete orthogroups (optional) — expand IDs to all members of their orthogroups to capture functionally related proteins.
• Run enrichment with GOATOOLS, then review significant terms and download detailed results.

Outputs: the enrichment figure and structured tables for downstream exploration.

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🚀 Looking ahead

OrthoGather is designed to grow. Near-term additions include:

• GO DAG visualisation
• Richer summary plots
• Faster foreground/background iteration
• Lightweight batch workflows

All while keeping the same local, reproducible, and privacy-preserving design.

In short: formulate testable functional hypotheses from orthogroup presence/absence, exploit well-annotated orthologs to illuminate under-annotated proteins, and obtain immediate, visual answers to “who shares what?” — with publication-ready outputs and no cloud dependency.

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📚 References & attributions

• OrthoFinder — phylogenetic orthology inference platform. See papers linked in their README. OrthoFinder GitHub
• GOATOOLS — Python library for Gene Ontology analyses. GOATOOLS GitHub
• UpSetPlot — visualization of set intersections. UpSetPlot Docs
• UniProt — comprehensive resource for protein sequence and annotation. UniProt