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SV signature analysis tool with custom SV type

Project description

https://viola-sv.readthedocs.io/en/latest/_static/Viola-logo/JPG/wide.jpg

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Overview

Viola is a flexible and powerful python package designed specifically for analysis of genomic structural variant (SV) signatures. We provide following tools for SV signature analysis:

  • Custom SV classification tool

  • Feature matrix generator

  • SV signature extractor (NMF) with stability evaluation system.

In addition to these, Viola offers a number of other useful utilities, including:

  • VCF filter that accepts genomic coordinates and INFO/FORMAT columns.

  • VCF merging tool with user-defined thresholds.

  • Breakends-to-breakpoint converter with SVTYPE inference.

  • Microhomology inference.

  • VCF-to-BEDPE conversion.

  • Command line tools for light users.

Currently, Viola supports four popular SV callers as input VCF files:

  • Manta

  • Delly

  • Lumpy

  • Gridss

In the future, we plan to support more SV callers!

Installation

The package can be installed with pip:

$ pip install viola-sv

To import Viola in your script, simply run below:

import viola

Command line tools:

$ viola <command> [something]

Prerequisites

Python version 3.6 or newer.

How to Learn Viola

As a first step we recommend the Quick Start page where you can see the basic behaviour of Viola. A good second step is to read the Signature Analysis Tutorial page.

For command line interfaces, see CLI tutorial.

If you want to get a deeper understanding of how Viola objects are structured, how filtering works and how merging works, please refer to the User Guide.

Manuals for individual classes/methods/functions are available in the API Reference.

For further discussion, please join our google group! We’re waiting for your comments, ideas, and requests. The questions about how to use Viola-SV are also welcome!

Documentation

Publications

Sugita, I., Matsuyama, S., Dobashi, H., Komura, D. & Ishikawa, S. Viola: a structural variant signature extractor with user-defined classifications. Bioinformatics (2021) doi:10.1093/bioinformatics/btab662.

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