sublyme 1.2.1

A SUBLYME pipeline for Uncovering Bacteriophage Lysins in Metagenomic Datasets

SUBLYME

Software for Uncovering Bacteriophage LYsins in MEtagenomic datasets

Table of Contents

1. About the Project
2. Getting Started
   • Prerequisites
   • Installation
3. Usage details
4. Output format

About the Project

SUBLYME is a tool to identify bacteriophage lysins. It utilizes the highly informative ProtT5 protein embeddings to make predictions and was trained using proteins in the PHALP database.

Getting started

SUBLYME has been packaged in PyPI for ease of use. The source code can be downloaded from GitHub.

Prerequisites

A GPU is recommended to compute embeddings for large datasets.

The full list of dependencies can be found in requirements.txt.

Dependencies are taken care of by pip.

python/3.11.5
joblib==1.2.0
numpy==1.26.4
pandas==2.2.1
torch==2.3.0
scipy==1.13.1
scikit-learn==1.3.0
transformers==4.43.1
sentencepiece==0.2.0

Installation

First create a virtual environment in python 3.11.5. For example:

conda create -n sublyme_env python=3.11.5
conda activate sublyme_env

From pypi:

pip install sublyme

Usage

sublyme test/input.faa -t 4

From apptainer:

Download Apptainer or singularity. On windows, this will require a virtual machine. WSL works well.

Fetch SUBLYME from Sylabs:

apptainer pull sublyme.sif library://alexandre_boulay/sublyme/sublyme

Usage

apptainer run sublyme.sif test/input.fa path/to/output_folder {protein|genome} nb_threads [--no-dedup]

The apptainer image accepts either protein or genomic sequences. If genomes are used as input, Prodigal will be run to determine coding sequences. Proteins will be deduplicated using MMseqs unless specified otherwise (--no-dedup) and lysins will be predicted within the resulting set of proteins. Arguments must be specified in the order they appear above.

The script outputs 2-4 files:

• genes.fna: genes predicted by Prodigal.
• proteins.faa: proteins predicted by Prodigal.
• proteins.csv: protein embeddings computed using ProtT5.
• sublyme_predictions.csv: predictions obtained from sublyme.

From source:

git clone https://github.com/Rousseau-Team/sublyme.git
cd sublyme
pip install -r requirements.txt

ex. python3 src/sublyme/sublyme.py test/input.faa -t 4 --models_folder src/sublyme/models

Usage details

A fasta file of protein sequences or a csv file of protein embeddings can be used as input.

Specifying the option --only_embeddings will only compute embeddings. This step is much faster with a GPU. The embeddings file can then be reinputted using the same command (without --only_embeddings) and specifying the new file as input file.

Options:

• input_file: Path to input file containing protein sequences (.fa*) or protein embeddings (.csv) that you wish to annotate.
• --threads (-t): Number of threads (default 1).
• --output_folder (-o): Path to the output folder. Default folder is ./outputs/.
• --models_folder (-m): Path to folder containing pretrained models (lysin_miner.pkl, val_endo_clf.pkl). Default is src/sublyme/models.
• --only_embeddings: Whether to only calculate embeddings (no lysin prediction).

Output format

The output consists of a csv file with a column for the final prediction and one column each for probabilities associated to lysins, endolysins and VALs.

Ex.

pred	lysin	endolysin	VAL
lysin|endolysin	0.98	0.95	0.05
Na	0.01	Na	Na

Note that the endolysin/VAL classifier is one multiclass classifier, implying that their probabilities will always add up to one and that the classifier will always assign one of these to be true.

Also, the endolysin/VAL classifier is only applied to proteins first predicted as being lysins (lysin proba >0.5).