SVision 1.3.0

SV/CSV callers

<img src=”https://github.com/xjtu-omics/SVision/blob/master/supports/svision-logo.png” alt=”svision_logo” width=”30%” height=”30%” align=center/>

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.

<img src=”https://github.com/xjtu-omics/SVision/blob/master/supports/workflow.png” alt=”SVision workflow” width=”60%” height=”60%” align=center/>

Please check the [wiki](https://github.com/xjtu-omics/SVision/wiki) page for more details.

## License

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 ([e-mail]) or Kai Ye ([e-mail]).

## Install and run

### Install from PyPI

Step1: Create a python environment with conda

` conda create -n svision-env python=3.6 ` Step2: Install deep-learning related packages

` conda install -c conda-forge opencv==4.5.1 conda install -c conda-forge tensorflow==1.14.0 `

### Install from source Step1: Create a python environment with conda

` conda create -n svision-env python=3.6 ` step2: Install basic packages ` conda install -c anaconda scipy, pysam, numpy, beautifulsoup4 `

Please install numpy=1.16.4 to avoid feature warnings raised by tensorflow

Step3: Install deep-learning related packages

` conda install -c conda-forge opencv==4.5.1 conda install -c conda-forge tensorflow==1.14.0 ` Step4: Install from source code

` git clone https://github.com/xjtu-omics/SVision.git cd SVision python setup.py install `

### Usage

` SVision [parameters] -o <output path> -b <input bam path> -g <reference> -m <model path> `

## Change Logs

V1.2.1

Fixing insertion length for detailed breakpoints.

V1.2

1. Adding function for calling from minimap2 aligned BAM, where CIGAR operator is different from NGMRL.

2. Adding Graph representation for detected complex structural variants.

3. Adding a prameter for detecting from contig aligned BAMs.

V1.1.6

1. Making changes to the formation mechanism inference module.

2. Adding GT, DV and DF to the standard VCF output.

V1.1.5

Fixed bug: The function process_cigars() in collect_signatures.py affect the breakpoints’ precision of short (about 50bp) DEL and INS.

V1.1.4

Adding a SV formation mechanism inference module.

V1.1.3

Adding internal breakpoints refine module.

V1.1.2

1. Fixing bug while processing alternative contigs, such as chrUn_JTFH01001938v1_decoy

2. Adding breakpoint left shift operation

3. Fixing bug while distinguish major and minor segments at src/analyze_reads.py line 20

## Contact If you have any questions, please feel free to contact: [e-mail], [e-mail]