MetaPepticon

Automated anticancer peptide prediction from genomics and metagenomics datasets

If you use this tool, please cite:

Erözden AA, Tavşanlı N, Demirel G, Sanli NO, Çalışkan M, Arıkan M. 2026. MetaPepticon: automated prediction of anticancer peptides from microbial genomes and metagenomes. PeerJ 14:e20990 https://doi.org/10.7717/peerj.20990

Table of contents

• Overview
• Requirements
• Setup
  • Data
  • Config
• Running
• Outputs
  • Final outputs
  • Intermediate outputs

Overview

MetaPepticon allows discovery of candidate anticancer peptides (ACPs) directly from diverse inputs, including:

• Raw genomic, metagenomic, transcriptomic, and metatranscriptomic reads
• Assembled contigs
• Peptide sequences.

By employing a consensus-based strategy and supporting heterogeneous data types, it facilitates scalable, reproducible, and high-confidence identification of ACP candidates.

Requirements

MetaPepticon requiresconda and snakemake:

1. Install conda: If you do not have conda installed, install conda.

2. Create a Snakemake environment in conda:

conda create -c conda-forge -c bioconda -n snakemake snakemake=8.25.5 python=3.11

3. Clone MetaPepticon repository: If you do not have git installed, install git.

git clone --recursive https://github.com/arikanlab/MetaPepticon.git

Note: Once conda and snakemake are set up, MetaPepticon manages the installation of all other tools and dependencies automatically in their respective environments during the first run.

Setup

Data

Copy your raw data to the relevant subfolders within the data directory:

Important: Please check sample format requirements below:

Data	Subfolder	Library Layout	Expected File Names
Metagenomics	data/MG	PE	samplename_1.fastq.gz, samplename_2.fastq.gz
Metatranscriptomics	data/MT	PE	samplename_1.fastq.gz, samplename_2.fastq.gz
Genomics	data/SG	PE	samplename_1.fastq.gz, samplename_2.fastq.gz
Transcriptomics	data/ST	PE	samplename_1.fastq.gz, samplename_2.fastq.gz
Contigs	data/contigs	-	samplename.fasta
Peptides	data/peptides	-	samplename.fasta

Config

MetaPepticon provides two options for generating the config.yaml

Option 1 (CLI)

bash workflow/scripts/prepare_config.sh

Option 2 (GUI)
Install dependencies:

pip install PyYAML PyQt5

Generate config file:

bash workflow/scripts/gui_prepare_config.py

Both options generate config.yaml file within config folder based on contents of data directory. Review and modify analysis parameters as needed.

Running

Once setup is complete, follow these steps to run MetaPepticon:

1. Activate snakemake environment:

conda activate snakemake

2. Run MetaPepticon:
Execute the following command from your project folder:

snakemake -s workflow/Snakefile --resources slot=1 --cores 16 --use-conda

Note: Adjust --cores to the number of cores available. Do not change--resources as it ensures proper resource allocation for assembly and toxicity prediction steps.

Outputs

MetaPepticon generates a results folder with two subfolders:

Final outputs

results/final: Tab delimited tables (.txt), one per sample, including anticancer peptide and toxicity predictions

Intermediate outputs

intermediate_files: Outputs from each each step of the pipeline.