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sashimi.py

PyPI version PyPI download install with bioconda Documentation Status License: BSD 3-Clause DOI


Tutorials

what is sashimi.py

sashimi.py is a tool for visualizing various next-generation sequencing (NGS) data, including DNA-seq, RNA-seq, single-cell RNA-seq and full-length sequencing datasets.

Features of sashimi.py

  1. Support various file formats as input
  2. Support strand-aware coverage plot
  3. Visualize coverage by heatmap, including HiC diagram
  4. Visualize protein domain based the given gene id
  5. Demultiplex the single-cell RNA/ATAC-seq which used cell barcode into cell population
  6. Support visualizing individual full-length reads in read-by-read style
  7. Support visualize circRNA sequencing data

Input

sashimi.py supports almost NGS data format, including

  • BAM
  • Bed
  • bigBed
  • bigWig
  • Depth file generated by samtools depth
  • naive Hi-C format

Output

The output will be a pdf and other image file formats which satisfy the requirement of the major journals, and each track on output corresponds these datasets from config file.

Usage

The sashimi.py is written in Python, and user could install it in a variety of ways as follows

Note: if segment fault with multiple processing, please try to use docker image, or just run with -p 1.

  1. install from PiPy

    pip install sashimi.py
    
  2. install from bioconda

    conda install -c bioconda sashimi-py
    
    # or
    conda env create -n sashimi -f environment.yaml
    
    # or
    conda env create -n sashimi -c conda-forge -c bioconda  -f requirements.txt
    conda activate sashimi && python setup.py install
    
  3. using docker image

    docker pull ygidtu/sashimi
    docker run --rm ygidtu/sashimi --help
    
    # or 
    
    git clone from https://github.com/ygidtu/sashimi.py sashimi
    cd sashimi
    docker build -t ygidtu/docker .
    docker run --rm ygidtu/sashimi --help
    
  4. install from source code

    git clone https://github.com/ygidtu/sashimi.py sashimi
    cd sashimi
    python setup.py install
    
    sashimipy --help
    # pr
    python main.py --help
    
  5. running from a local webserver

     git clone https://github.com/ygidtu/sashimi.py sashimi
     cd sashimi/web
    
     # build the frontend static files
     npm install -g vue-cli vite && npm install
     vite build
    
     # prepare the backend server
     pip install fastapi pydantic jinja2 uvicorn
    
     python server.py --help
    
  6. for pipenv users

    git clone https://github.com/ygidtu/sashimi.py
    cd sashimi.py
    pipenv install   # create virtualenv and install required packages
    pipenv shell   # switch to virtualenv
    
    sashimipy --help
    # pr
    python main.py --help
    

Example

python main.py \
  -e chr1:1270656-1284730:+ \
  -r example/example.sorted.gtf.gz \
  --interval example/interval_list.tsv \
  --density example/density_list.tsv \
  --show-side \
  --igv example/igv.tsv \
  --heatmap example/heatmap_list.tsv \
  --focus 1272656-1272656:1275656-1277656 \
  --stroke 1275656-1277656:1277856-1278656@blue \
  --sites 1271656,1271656,1272656 \
  --line example/line_list.tsv \
  -o example/example.png \
  --dpi 300 \
  --width 10 \
  --height 1 \
  --barcode example/barcode_list.tsv \
  --domain --remove-duplicate-umi

here is the output file.

Questions

Visit issues or contact Yiming Zhang and Ran Zhou

Citation

If you use Sashimi.py in your publication, please cite Sashimi.py by

Zhang et al. Sashimi.py: a flexible toolkit for combinatorial analysis of genomic data. bioRxiv 2022.11.02.514803.

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