1. Installation

1.1. CustardPy Docker image

Docker image of CustardPy is available at DockerHub. This image contains various tools for 3D genome analysis in addition to CustardPy core components as below:

• Mapping

  • BWA

  • Bowtie2

  • chromap

• Hi-C/Micro-C analysis

  • Juicer (v1.6)

  • Juicertools

  • JuiceBox

  • Cooler

  • cooltools

  • Pairtools

  • coolpup.py

  • HOMER

  • FAN-C

  • HiC-Pro

  • HiCUP

  • HiCExplorer

  • HiC1Dmetrics

• TAD/subcompartment calling

  • OnTAD

  • CALDER2

  • chromosight

• Loop calling

  • FitHiC

  • Mustache

• Stripe analysis

  • STRIPENN

• Chromatin hub analysis

  • FIREcaller

• Sample comparison

  • GENOVA

  • CHESS

• 3D/4D modeling

  • PASTIS

  • PHi-C2

• Quality check

  • 3DChromatin_ReplicateQC

  • MultiQC

• Hi-ChIP

  • FitHiChIP

• ChIA-PET

  • Mango

  • ChIAPop

• peak calling

  • MACS2

• File processing

  • SAMtools

  • BEDtools

  • hictk

• Utility tools

  • SRAtoolkit

  • genomepy

For a full description of each tool, visit the original website.

1.1.1. Download and run the Docker image

For Docker:

# pull docker image
docker pull rnakato/custardpy:<version>

# container login
docker run --rm -it rnakato/custardpy:<version> /bin/bash

# execute a command
docker run --rm -it rnakato/custardpy:<version> <command>

For Apptainer:

# build image
apptainer build custardpy.<version>.sif docker://rnakato/custardpy:<version>

# execute a command
apptainer exec custardpy.<version>.sif <command>

1.2. CustardPy from PyPI

Core components of CustardPy (e.g., commands for visualization) can by installed using pip (>= Python 3.7):

pip3 install custardpy