https://github.com/monarch-initiative/svanna

Efficient and accurate pathogenicity prediction for coding and regulatory structural variants in long-read genome sequencing
https://github.com/monarch-initiative/svanna

bioinformatics genomics long-read-sequencing prioritization rare-disease

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Efficient and accurate pathogenicity prediction for coding and regulatory structural variants in long-read genome sequencing

• Host: GitHub
• URL: https://github.com/monarch-initiative/svanna
• Owner: monarch-initiative
• Created: 2020-07-12T16:54:49.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2024-06-28T13:20:26.000Z (12 months ago)
• Last Synced: 2024-06-28T13:36:18.986Z (12 months ago)
• Topics: bioinformatics, genomics, long-read-sequencing, prioritization, rare-disease
• Language: Java
• Homepage:
• Size: 3.72 MB
• Stars: 31
• Watchers: 9
• Forks: 4
• Open Issues: 16
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.rst

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README

        
# SvAnna - Structural Variant Annotation and Analysis

[![GitHub release](https://img.shields.io/github/release/TheJacksonLaboratory/SvAnna.svg)](https://github.com/TheJacksonLaboratory/SvAnna/releases)

[![Java CI with Maven](https://github.com/TheJacksonLaboratory/SvAnna/workflows/Java%20CI%20with%20Maven/badge.svg)](https://github.com/TheJacksonLaboratory/SvAnna/actions/workflows/maven.yml)

[![Documentation Status](https://readthedocs.org/projects/svanna/badge/?version=master)](https://svanna.readthedocs.io/en/master/?badge=master)

Efficient and accurate pathogenicity prediction for coding and regulatory structural variants in long-read genome sequencing.

Most users should download the latest SvAnna distribution ZIP file from

the [Releases page](https://github.com/TheJacksonLaboratory/SvAnna/releases).

## Example use

SvAnna is a standalone command-line Java application and can be run as follows:

```shell

java -jar svanna-cli.jar -d path/to/svanna/data \

  -t HP:0008330 \

  --vcf example.vcf.gz \

  --output-format html,csv,vcf

```

The analysis will filter out common SVs and perform phenotype-driven prioritization of the remaining SVs. 

The SVs are assigned with *"Pathogenicity of Structural variation"* (PSV) score and written into 

one of several output formats, such as CSV table, a VCF file, or a detailed HTML report.

### HTML report

The HTML report includes a header with the analysis summary and the SVs ordered by the PSV score 

with the best scores on top.

### Analysis summary

The summary presents the clinical features encoded into terms of Human Phenotype Ontology (HPO) as well as 

the other analysis parameters.

![Analysis summary](img/analysis-summary.png)

### Variant counts

The report further breaks down SVs into several categories:

![Variant counts](img/variant-counts.png)

### Structural variants

Last, each SV is presented in the context of the overlapping genes and transcripts:

![Variant transcript summary](img/variant-tx-summary.png)

We also show the variant in context of the neighboring repetitive regions and genes/transcripts:

![Variant context](img/variant-tx-context.png)

## Read more

Please consult the Read the docs site for a detailed documentation:

- [stable version](https://svanna.readthedocs.io/en/master) describing the latest release at the *Releases page*, or

- [latest version](https://svanna.readthedocs.io/en/latest) summarizing the latest development on `development` branch.

Check out SvAnna manuscript in [Genome Medicine](https://doi.org/10.1186/s13073-022-01046-6).