vicon 1.0.5

VICON - Viral Conserved Sequence Extraction Toolkit

VICON - Viral Sequence Analysis Toolkit

VICON is a Python package for processing and analyzing viral sequence data, with specialized tools for viral genome coverage analysis and sequence alignment.

Features

• Viral sequence alignment and coverage analysis
• K-mer analysis and sliding window coverage calculations
• Visualization tools for coverage plots
• Wrapper scripts for vsearch and ViralMSA

Installation

Option 1: Conda (Recommended)

The easiest way to install VICON with all dependencies:

# Create and activate a new environment
conda create -n vicon python=3.11
conda activate vicon

# Install VICON and all dependencies
conda install -c conda-forge -c bioconda -c eka97 vicon

# Set required permissions
chmod +x "$CONDA_PREFIX/bin/vicon-run"
chmod +x "$CONDA_PREFIX/bin/viralmsa"
chmod +x "$CONDA_PREFIX/bin/minimap2"

Option 2: PyPI (pip)

Install from PyPI:

pip install vicon

Note: When installing via pip, you must manually install these external dependencies:

• minimap2 (≥2.30)
• vsearch
• ViralMSA

Installing External Dependencies

Ubuntu / Debian:

sudo apt-get update
sudo apt-get install -y minimap2 vsearch

macOS (Homebrew):

brew install minimap2 vsearch

ViralMSA:

mkdir -p ~/bin && cd ~/bin
wget "https://raw.githubusercontent.com/niemasd/ViralMSA/master/ViralMSA.py"
chmod +x ViralMSA.py
ln -sf "$PWD/ViralMSA.py" ~/.local/bin/viralmsa

Usage

Run the VICON pipeline with:

vicon-run --config path/to/your/config.yaml

Input FASTA Preprocessing

Note:
VICON automatically preprocesses your input FASTA files (both sample and reference) before analysis:

• Converts all sequences to uppercase
• Cleans and standardizes FASTA headers
• Replaces any non-ATCG characters in sequences with 'N'

You do not need to manually edit or check your FASTA files for these issues.

Example Configuration

Create a configuration file (config.yaml):

project_path: "project_path"
virus_name: "orov"
input_sample: "data/orov/samples/samples.fasta"
input_reference: "data/orov/reference/reference.fasta"
email: "[e-mail]"
kmer_size: 150
threshold: 147 # shows a tolerance of 150-147 = 3 degenerations
l_gene_start: 8000
l_gene_end: 16000
coverage_ratio: 0.5
min_year: 2020
threshold_ratio: 0.01
drop_old_samples: false
drop_mischar_samples: true

FASTA Header Year Extraction

The pipeline automatically extracts years from FASTA headers using a two-step approach:

1. Priority extraction: Years following separators (|, _, /, -)
2. Fallback extraction: Any standalone 4-digit number between 1850-2030

Header Example	Year Extracted?	Extracted Year	Reason
`>sample	2021`	✅ Yes	2021
>sample_2020	✅ Yes	2020	After underscore separator
>sample/2019/data	✅ Yes	2019	After slash separator
>sample-2022-final	✅ Yes	2022	After dash separator
>data 2021 sequence	✅ Yes	2021	Standalone 4-digit number
>sample.2020.version	✅ Yes	2020	Standalone 4-digit number
>test2021extra	✅ Yes	2021	Standalone 4-digit number
`>sample	202`	❌ No	-
>sample_1800_old	❌ No	-	Outside valid range (1850-2030)
>sample20213long	❌ No	-	5 consecutive digits

Best Practice: Use |YYYY, _YYYY, /YYYY, or -YYYY patterns for reliable year extraction.

License

This project is licensed under the terms of the MIT license.