SV signature analysis tool with custom SV type
Project description
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.
Recommended Environment
- OS
Linux
Mac
- Script Manager
Jupyter Notebook/Jupyter Lab
Google Colaboratory
Docker support is coming soon!
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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