empathi 1.0.0

An embedding-based phage protein annotation tool by hierarchical assignment

EmPATHi
Embedding-based Phage Protein Annotation Tool by Hierarchical assignment

Table of Contents

1. About the Project
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Contact

About the Project

Little description.

Preprint can be found at: [link]

Getting Started

EmPATHi has been packaged in PyPI and as an Apptainer container for ease of use.
The source code can also be downloaded from HuggingFace.

Prerequisites

The full list of dependencies and versions we tested to be compatible can be found in requirements.txt. Dependencies are taken care of by pip and Apptainer. See instructions below.

python/3.11.5
joblib==1.2.0
numpy==1.26.4
pandas==2.2.1
matplotlib==3.9.0
torch==2.3.0
scipy==1.13.1
scikit-learn==1.5.0
transformers==4.43.1
sentencepiece==0.2.0
seaborn==0.13.2

The models used by EmPATHi must be obtained seperately. See instructions below.
The models folder for EmPATHi must be obtained from HuggingFace.
ProtT5 must also be downloaded from HuggingFace.

Installation

First, create a virtual environement in python 3.11.5. This can be done using tools such as conda and virtualenv.

Download models for EmPATHi and ProtT5:

git lfs install
git clone https://huggingface.co/AlexandreBoulay/EmPATHi
git clone https://huggingface.co/Rostlab/prot_t5_xl_half_uniref50-enc Rostlab/prot_t5_xl_half_uniref50-enc
export PATH="/path/to/EmPATHi/models:$PATH"
export PATH="/path/to/Rostlab/prot_t5_xl_half_uniref50-enc:$PATH"

1. PIP

pip install empathi

2. Apptainer

3. From source code

Clone the repo if it isn't already done:

git lfs install
git clone https://huggingface.co/AlexandreBoulay/EmPATHi

Install dependencies:

cd EmPATHi
pip install -r requirements.txt

Usage

For pip:

python
from empathi import empathi
empathi(input_file, name, output_folder="path/to/output")

For Apptainer:

From command line:

python src/empathi/empathi.py -h

Options:

• input_file: Path to input file containing protein sequencs (.fa*) or protein embeddings (.pkl/.csv).
• name: Name of file you want to save to (wOut extension). Should be different between runs to avoid overwriting files.
• --models_folder: Path to folder containing EmPATHi models. Can be left unspecified if it was added to PATH earlier.
• --only_embeddings: Whether to only calculate embeddings (no functional prediction).
• --output_folder: Path to the output folder. Default is ./empathi_out/.
• --mode: Which types of proteins you want to predict. Accepted arguments are "all", "pvp", "rbp", "lysin", "regulator"...

When launching from python omit the '--' in front of args.

Contact