Metadata-Version: 1.2
Name: SVision
Version: 1.3.0
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/>
        
        
        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 (jiadong324@stu.xjtu.edu.cn) or Kai Ye (kaiye@xjtu.edu.cn).
        
        ## 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: 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
