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SVision-1.3.3
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تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
SV/CSV callers
| ویژگی | مقدار |
|---|---|
| سیستم عامل | POSIX :: Linux |
| نام فایل | SVision-1.3.3 |
| نام | SVision |
| نسخه کتابخانه | 1.3.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jiadong Lin, Songbo Wang |
| ایمیل نویسنده | jiadong66@stu.xjtu.edu.cn, songbowang125@163.com |
| آدرس صفحه اصلی | https://github.com/xjtu-omics/SVision |
| آدرس اینترنتی | https://pypi.org/project/SVision/ |
| مجوز | GPLv3 |
<img src="https://github.com/xjtu-omics/SVision/tree/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/tree/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 ([download link](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.
## SVision output
### VCF
The SV ```ID``` column is given in the format of ```a_b```, where ```b``` indicates site ```a``` contains other type of SVs.
Filters used in the output.
```Covered```: The entire SV is spanned by long-reads, producing the most confident calls.
```Uncovered```: SV is partially spanned by long-reads, i.e. reads spanning one of the breakpoints.
```Clustered```: SV is partially spanned by long-reads, but can be spanned through reads clusters.
We add extra attributes in the ```INFO``` column of VCF format for SVision detected structural variants.
```BRPKS```: The CNN recognized breakpoint junctions through tMOR.
```GraphID```: The graph index used to indicate the graph structure, which requires ```--report_graph``` and is obtained by calculating isomorphic graphs.
The ID for simple SVs is -1.
```VAF```: The estimated variant allele fraction, which is calculated by DV/DR. Note that SVision does not calculate the genotypes in the current version.
### CSV graph
#### Graph format
The graph output requires ```--report_graph``` activated.
The below example is an CSV in rGFA format, which is detected by SVision at chr11:99,819,283-99,820,576 in HG00733.
The graph output is saved in separated files for each CSV events.
```
S S1 SN:Z:chr11 SO:i:99819338 SR:i:0 LN:i:2990
S I0 SN:Z:m54329U_190827_173812/140708091/ccs SO:i:15813 SR:i:0 LN:i:1113
S I1 SN:Z:m54329U_190827_173812/140708091/ccs SO:i:16927 SR:i:0 LN:i:466
S I2 SN:Z:m54329U_190827_173812/140708091/ccs SO:i:17400 SR:i:0 LN:i:377 DP:S:S1:99820198
S I3 SN:Z:m54329U_190827_173812/140708091/ccs SO:i:17778 SR:i:0 LN:i:838
S I4 SN:Z:m54329U_190827_173812/140708091/ccs SO:i:18617 SR:i:0 LN:i:61 DP:S:S0:99819276
L S0 + I0 + 0M SR:i:0
L I0 + I1 + 0M SR:i:0
L I1 + I2 - 0M SR:i:0
L I2 - I3 + 0M SR:i:0
L I3 + I4 + 0M SR:i:0
L I4 + S1 + 0M SR:i:0
```
Besides the information included in standard [rGFA](https://github.com/lh3/gfatools/blob/master/doc/rGFA.md) format,
we add another ```DP:S``` column to indicate sequence with detected origins via local realignment,
such as node ```I2``` is duplicated from node ```S1```.
#### Graph alignment (Experimental)
**Note**: This function is not included in the current program, it is a post-processing step that tries to validate the detected CSVs.
To validate the detected CSV, we align raw HiFi reads to the mini graph (CSV graph) reported by SVision with GraphAligner.
**Step1: Extract HiFi raw reads**
```
samtools view -b HG00733.ngmlr.sorted.bam chr11:99810000-99830000 > tmp.bam
samtools fasta tmp.bam > tmp.fasta
```
**Step2: Align with GraphAligner**
Please check [GraphAligner](https://github.com/maickrau/GraphAligner) for the detailed usage.
```
GraphAligner -g chr11-99819283-99820576.gfa -f tmp.fasta -a aln.gaf -x vg
```
Example of CSV path supporting reads
```
m54329U_190827_173812/140708091/ccs 21668 0 21668 + >S0>I0>I1<I2>I3>I4>S1
m54329U_190617_231905/88145984/ccs 13612 0 13612 + >S0>I0>I1<I2>I3>I4>S1
m54329U_190617_231905/88145984/ccs 13612 0 13612 + >S0>I0>I1<I2>I3>I4>S1
```
## Contact
If you have any questions, please feel free to contact: jiadong66@stu.xjtu.edu.cn, songbowang125@163.com
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl SVision-1.3.3:
pip install SVision-1.3.3.whl
نصب پکیج tar.gz SVision-1.3.3:
pip install SVision-1.3.3.tar.gz