SVision 1.3.1

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/>

## 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

Step1: Create a python environment with conda

` conda create -n svision-env python=3.6 ` Step2: Install required packages of specific versions

` conda install -c anaconda pysam==0.16.0 conda install -c conda-forge opencv==4.5.1 conda install -c conda-forge tensorflow==1.14.0 ` Step3: Install SVision from PyPI

` pip install SVision `

(Optional) 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> `

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

#### Input/output 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.

`-m` path to the pre-trained deep learning model, which is available at https://drive.google.com/drive/folders/1j74IN6kPKEx9hy3aENx3zHYPUnyYWGvj?usp=sharing.

#### General parameters ` -t THREAD_NUM Thread numbers [1] -s MIN_SUPPORT Min support read number for an SV [1] -c CHROM Specific region to detect, format: chr1:xxx-xxx or 1:xxx-xxx --hash_table Activate hash table to align unmapped sequences --cluster_callset Cluster original callset to merge uncovered event --report_mechanism Report mechanisms for DEL event --report_graph Report graph for events --contig Activate contig mode `

`--hash_table` enables the image subtraction process, which is activated by default.

`--report_graph` enables the program to create the CSV graph in GFA format, which is not activated by default.

`--report_mechanism` is used to infer the formation mechansim according to the breakpoint sequence features. This is still underdevelopment, which is not recommended to use for current version.

`--contig` is used for calling from assemblies, which currently uses minimap2 aligned BAM file as input.

#### Other parameters

`--partition_max_distsance` maximum distance allowed of a group of feature sequences.

`--cluster_max_distance` maximum distance for feature sequence clustering. This is implemented via Scipy hierarchical clustering.

`--k_size` size of kmer used in hash-table realignment, only used when `--hash_table` is activated.

`--min_accept` minimum matched segment length, default is 50bp.

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