trackplot 0.0.1b0

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trackplot

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Tutorials

what is trackplot

trackplot 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 trackplot

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

trackplot supports almost NGS data format, including

• BAM
• Bed
• Depth file generated by samtools depth
• bigBed [pyBigWig required]
• bigWig [pyBigWig required]
• naive Hi-C format [hicmatrix required]

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 trackplot 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 bioconda

   conda install -c bioconda -c conda-forge trackplot
   
   # or install trackplot into an isolated environments
   conda create -n sashimi -c bioconda -c conda-forge trackplot
   
   # or install latest trackplot  
   git clone https://github.com/ygidtu/trackplot.git sashimi
   cd sashimi
   conda create -n sashimi -f environment.yaml
   

2. install from PyPi

   pip install trackplot
   # __Note:__ We noticed some pypi mirrors are not syncing some packages we depend on, 
   # therefore please try another pypi mirror once you encounter 
   # `No local packages or working download links found for xxx`
   
   # optional, enable bigWig, bigBed and hicMatrix support
   pip install pybigwig hicmatrix
   

3. using docker image

   docker pull ygidtu/sashimi
   docker run --rm ygidtu/sashimi --help
   
   # or build docker image from source
   git clone https://github.com/ygidtu/trackplot 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/trackplot sashimi
   cd sashimi
   pip install -r requirements.txt
   python setup.py install
   
   # optional, enable bigWig, bigBed and hicMatrix support
   pip install pybigwig hicmatrix
   
   sashimipy --help
   # or
   python main.py --help
   

5. for pipenv or poetry users

   git clone https://github.com/ygidtu/trackplot
   cd trackplot
   
   # pipenv
   # create virtualenv and install required packages
   pipenv install
   # optional, with `--pypi-mirror https://pypi.tuna.tsinghua.edu.cn/simple` to specify your faverate PyPi mirror
   # optional, with `--skip-lock` once encounter locking issues
   
   # switch to virtualenv
   pipenv shell && python main.py --help
   
   # or just run with pipenv
   pipenv run python main.py --help
   
   
   # poetry
   # once facing installation issues, please try to change PyPi mirror in tool.poetry.source section of pyproject.toml 
   # create virtualenv and install required packages
   poetry install
   
   # switch to virtualenv
   poetry shell  && python main.py --help
   
   # or just run with poetry
   poetry run python main.py --help
   

6. running from a local webserver

   Install trackplot before set up the web server

   git clone https://github.com/ygidtu/trackplot 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
   

Example

The example folder is downloaded from here. And a more detailed tutorial could be found at here.

python main.py \
  -e chr1:1270656-1284730:+ \
  -r example/example.sorted.gtf.gz \
  --interval example/interval_list.tsv \
  --density example/density_list.tsv \
  --show-site \
  --show-junction-num \
  --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.png \
  --dpi 300 \
  --width 10 \
  --height 1 \
  --barcode example/barcode_list.tsv \
  --domain --remove-duplicate-umi \
  --normalize-format cpm \
  -p 4

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.