Metadata-Version: 1.2
Name: SVision
Version: 1.3.1
Summary: SV/CSV callers
Home-page: https://github.com/xjtu-omics/SVision
Author: Jiadong Lin, Songbo Wang
Author-email: jiadong66@stu.xjtu.edu.cn, songbowang125@163.com
License: GPLv3
Description: <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 (jiadong324@stu.xjtu.edu.cn) or Kai Ye (kaiye@xjtu.edu.cn).
        
        ## 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: jiadonglin324@163.com, songbowang125@163.com
        
Keywords: SVision,Deep learning,Complex structural variants,Structural variants,Single moleculo sequencing
Platform: UNKNOWN
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6
