Pycallingcards

Pycallingcards is a package for calling cards data analysis developed and maintained by Mitra Lab at Washington University in St. Louis.

Calling cards is a sequencing technology to assay TF binding which could be done in vitro and in vivo at both bulk and single-cell level. To know more about calling cards technology, please check Moudgil et al and Wang et al.

Pycallingcards is composed of five different part: datasets, reading (rd), preprocessing (pp), tools (tl) and plotting (pl). For single-cell calling cards anaysis, Pycallingcards interacts with Scanpy and the main structure of Pycallingcards also follows the Scanpy.

• Datasets contains four main published datasets for callingcards data.
• Reading (rd) includes several functions to read and save qbed and peak data.
• Preprocessing (pp) helps to preprocess data from qbed data to call peaks, make annotation, make Anndata object and filter peaks.
• Tools (tl) calls motif of the peaks, completes differential peaks and pair differential peaks with gene expression,
• Plotting (pl) proveides an allround plottting system. It could plot genome areas, link with WashU Epigenome Browser, show signal comparison with Chip-seq(BigWig file), display differential peaks, demonstrate potenial binding-gene expression relationships.

Documentation.

Please check here for detailed documentation.

Installation

pip install pycallingcards

Install via GitHub

pip install "git+https://github.com/The-Mitra-Lab/pycallingcards.git" --upgrade

Docker

Link to dockerhub of pycallingcards here.

Link to tips and codes for using pycallingcards docker here.

Development

Use pre-commit to format code at git commit.

pip install pre-commit
pre-commit install

Cite

@article{guo2024pycallingcards,
  title={Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting calling cards data},
  author={Guo, Juanru and Zhang, Wenjin and Chen, Xuhua and Yen, Allen and Chen, Lucy and Shively, Christian A and Li, Daofeng and Wang, Ting and Dougherty, Joseph D and Mitra, Robi D},
  journal={Bioinformatics},
  pages={btae070},
  year={2024},
  publisher={Oxford University Press}
}