Data

Tools

Indexes

Applications

Experiments

Open Source Science

svision

Detecting genome structural variants with deep learning in single molecule sequencing

https://github.com/xjtu-omics/svision

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: nature.com, science.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (17.5%) to scientific vocabulary

Keywords

complex-structural-variation deep-neural-networks single-molecule-sequencing structral-variation
Last synced: 10 months ago · JSON representation

Repository

Detecting genome structural variants with deep learning in single molecule sequencing

Basic Info
  • Host: GitHub
  • Owner: xjtu-omics
  • License: gpl-3.0
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 37.9 MB
Statistics
  • Stars: 113
  • Watchers: 4
  • Forks: 10
  • Open Issues: 18
  • Releases: 8
Topics
complex-structural-variation deep-neural-networks single-molecule-sequencing structral-variation
Created over 5 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Support

README.md

SVision

SVision is a deep learning-based structural variants caller that takes aligned reads or contigs as input. Especially, SVision implements a targeted multi-objects recognition framework, detecting and characterizing both simple and complex structural variants from three-channel similarity images.

License and citation

SVision is free for non-commercial use by academic, government, and non-profit/not-for-profit institutions. A commercial version of the software is available and licensed through Xi’an Jiaotong University. For more information, please contact with Jiadong Lin (jiadong66@stu.xjtu.edu.cn) or Kai Ye (kaiye@xjtu.edu.cn).

Please cite our paper "Lin, J., Wang, S., Audano, P.A. et al. SVision: a deep learning approach to resolve complex structural variants. Nat Methods (2022)." PDF

Installation

Operation systems

  • MacOS, Big Sur (V11.6)
  • Ubuntu (V20.04, including Windows Subsystem for Linux)
  • CentOS Linux (V7.6.1810)

From source

```

Get latest source code

git clone https://github.com/xjtu-omics/SVision.git cd SVision

Create conda environment and install SVision

conda env create -f environment.yml python setup.py install ```

Docker

docker pull jiadongxjtu/svision:latest

Usage

Please check the wiki page for more usage and output file format.

We provided support scripts used in this study to filter SVision calls at SVisionUtil, please follow instructions to filter your own calls.

General usage

Required Input/Ouput parameters

-o OUT_PATH Absolute path to output -b BAM_PATH Absolute path to bam file -m MODEL_PATH Absolute path to CNN predict model -g GENOME Absolute path to your reference genome (.fai required in the directory) -n SAMPLE Name of the BAM sample name

-g path to the reference genome, the index file should under the same directory. Please include all chromosomes you want to detect in the reference file. SVision only call SVs from chromosomes specified in the reference.

-m path to the pre-trained deep learning model. NOTE: Please use -m svision-cnn-model.ckpt while running your data.

NOTE: If your input contains the alignment of assemblies, please activate the contig mode with --contig.

Run demo data

The demo data is ./supports/HG00733.svision.demo.bam, which is extracted from whole genome sequencing HiFi data of HG00733 used in this study. We provided HG00733 whole genome calls used in this study, which is available at SVisionUtil. The HiFi data of HG00733 is generated by HGSVC in a recent study published at Science.

Prepare required inputs

  1. Download reference genome GRCh38

  2. Download pretrained CNN model. There are three files and please put all files under a directory (e.g., /home/user/svision_model/).

Run SVision

Before running, please create a directory for SVision output (e.g., /home/user/svision_out).

SVision -o ./home/user/svision_out -b ./supports/HG00733.svision.demo.bam -m /home/user/svision_model/svision-cnn-model.ckpt -g /path/to/reference.fa -n HG00733 -s 5 --graph --qname

docker run -v /local/path:/container/path jiadongxjtu/svision:latest SVision -o /container/path/svision_out -b /container/path/HG00733.svision.demo.bam -m /container/path/svision_model/svision-cnn-model.ckpt -g /container/path/reference.fa -n HG00733 -s 5 --graph --qname REMINDER: For Docker run, please put your BAM file, reference file and the pre-trained model under the /local/path.

Owner

  • Name: xjtu-omics
  • Login: xjtu-omics
  • Kind: organization

GitHub Events

Total
  • Issues event: 5
  • Watch event: 15
  • Issue comment event: 11
  • Push event: 4
Last Year
  • Issues event: 5
  • Watch event: 15
  • Issue comment event: 11
  • Push event: 4

Committers

Last synced: over 3 years ago

All Time
  • Total Commits: 115
  • Total Committers: 4
  • Avg Commits per committer: 28.75
  • Development Distribution Score (DDS): 0.183
Top Committers
Name Email Commits
jiadonglin j****4@g****m 94
zhaohh52 4****2@u****m 17
SongboWangGit 9****3@q****m 3
bowen 9****1@q****m 1
Committer Domains (Top 20 + Academic)
qq.com: 2

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 39
  • Total pull requests: 0
  • Average time to close issues: 5 months
  • Average time to close pull requests: N/A
  • Total issue authors: 34
  • Total pull request authors: 0
  • Average comments per issue: 2.59
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 7
  • Pull requests: 0
  • Average time to close issues: about 1 month
  • Average time to close pull requests: N/A
  • Issue authors: 7
  • Pull request authors: 0
  • Average comments per issue: 1.43
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • bo8883 (2)
  • baozg (2)
  • Bigdata20688 (2)
  • tnnandi (2)
  • Pigrenok (2)
  • MinjaYuan (1)
  • leedchou (1)
  • zer0127 (1)
  • hanxiaoxu1110 (1)
  • rxw125 (1)
  • biozzq (1)
  • wudikyy (1)
  • DHmeduni (1)
  • jamesdalg (1)
  • asylvz (1)
Pull Request Authors
Top Labels
Issue Labels
enhancement (4) good first issue (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 6 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 4
  • Total maintainers: 1
pypi.org: svision

SV/CSV callers

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 6 Last month
Rankings
Stargazers count: 7.6%
Dependent packages count: 10.0%
Forks count: 10.9%
Average: 19.7%
Dependent repos count: 21.7%
Downloads: 48.5%
Maintainers (1)
jiadong66
Last synced: 11 months ago

Dependencies

setup.py pypi
  • beautifulsoup4 *
  • intervaltree *
  • pysam *
  • pyvcf *
  • scipy ==1.5.4
  • tensorflow ==1.14.0
environment.yml conda
  • pip
  • python 3.6.*
Dockerfile docker
  • ubuntu 18.04 build