Pycallingcards: Calling Cards Data Analysis in Python#

Welcome to 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/ccf and peak data.

  • Preprocessing (pp) helps to preprocess data from qbed/ccf 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.

If you find a model useful for your research, please consider citing the [Pycallingcards manuscript](to be done). Thank you!


Click here to view a brief Pycallingcards installation guide and prerequisites.


End-to-end tutorials showcasing key features in the package.

User guide

User guide provides some detail information of Pycallingcards.

API reference

Detailed descriptions of Pycallingcards API and internals.


Ask questions, report bugs, and contribute to Pycallingcards at our GitHub repository.

This documentation was heavily inspired and adapted from the scvi-tools documentation. Go check them out!