ATaRVa - a tandem repeat genotyper

ATaRVa (pronounced uh-thur-va, IPA: /əθərvə/, Sanskrit: अथर्व) is a technology-agnostic tandem repeat genotyper, specially designed for long read data. The name expands to Analysis of Tandem Repeat Variation, and is derived from the the Sanskrit word Atharva meaning knowledge.

Motivation

Long-read sequencing propelled comprehensive analysis of tandem repeats (TRs) in genomes. Current long-read TR genotypers are either platform specific or computationally inefficient. ATaRva outperforms existing tools while running an order of magnitude faster. ATaRVa also supports multi-threading, haplotyping, motif decomposition and methylation profiling, making it an invaluable tool for population scale TR analyses.

Installation

PyPI installation

ATaRVa can be directly installed using pip with the package name ATaRVa.

$ pip install ATaRVa

Alternatively, it can be installed from the source code:
It is recommended to install this inside a Python virtual environment.

# Create a python env
$ python -m venv atarva_env

# Activate the env
$ source atarva_env/bin/activate
$ pip install build

# Download the git repo
$ git clone https://github.com/SowpatiLab/ATaRVa.git

# Install
$ cd ATaRVa
$ python -m build
$ pip install .

# Deactivate the env
$ deactivate

Both of the methods add a console command atarva, which can be executed from any directory

Docker installation

ATaRVa can also be installed using the provided Docker image with the following steps:

$ cd ATaRVa
$ docker build --network host -t atarva

Documentation

• Usage
• genotype usage
• merge usage
• FAQs
• Changelog

Usage

The help message and available subcommands can be accessed using

$ atarva -h
#  or
$ atarva --help

which gives the following output

ATaRVa - Analysis of Tandem Repeat Variants
Sowpati Lab

Usage:
    atarva [OPTIONS] <COMMAND>

Commands:
  genotype  Tandem Repeat Genotyper
  merge     Merge ATaRVa VCF files

Options:
  -h, --help     Print help
  -v, --version  Print version

Analysis script

All scripts used for analysis are provided in ATaRVa_Manuscript

Citation

If you find ATaRVa useful for your research, please cite it as follows:

ATaRVa: Analysis of Tandem Repeat Variation from Long Read Sequencing data
Abishek Kumar Sivakumar, Sriram Sudarsanam, Anukrati Sharma, Akshay Kumar Avvaru, Divya Tej Sowpati
BioRxiv, doi: https://doi.org/10.1101/2025.05.13.653434

Contact

For queries or suggestions, please contact:

Divya Tej Sowpati - tej at csirccmb dot org

Abishek Kumar S - abishekks at csirccmb dot org

Akshay Kumar Avvaru - avvaruakshay at gmail dot com