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Analysis and Visualization of Multiple Hi-C and Micro-C Data with CustardPy.

Yuya Nagaoka1, Ryuichiro Nakato2

  • 1Laboratory of Computational Genomics, Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan. nagaoka_uya@iqb.u-tokyo.ac.jp.

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Summary

CustardPy simplifies 3D genome analysis by providing a Docker-based pipeline for Hi-C and Micro-C data. This tool streamlines the entire workflow, from mapping to visualization, aiding researchers in studying genome structure and disease.

Keywords:
3D genome structureCustardPyHi-CMicro-CVisualization

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Three-dimensional (3D) genome structure is vital for biological processes and disease.
  • Hi-C and Micro-C are established methods for analyzing 3D genome structures.
  • Challenges exist in selecting analysis pipelines and setting up computational environments.

Purpose of the Study:

  • To introduce CustardPy, a Docker-based pipeline for 3D genome analysis.
  • To provide a user-friendly solution for analyzing and visualizing Hi-C and Micro-C data.
  • To facilitate the comparison and evaluation of multiple samples.

Main Methods:

  • Developed CustardPy, a Docker-based pipeline.
  • Integrated existing tools for a comprehensive workflow.
  • Covered steps from FASTQ mapping to data visualization.

Main Results:

  • Demonstrated the analysis and visualization of Hi-C data using CustardPy.
  • Showcased the pipeline's ability to handle multiple samples.
  • Introduced several 3D genome features observable in Hi-C data.

Conclusions:

  • CustardPy offers a streamlined and accessible approach to 3D genome analysis.
  • The pipeline simplifies complex workflows, aiding researchers in exploring genome architecture.
  • Facilitates the identification and study of 3D genome structures relevant to biology and disease.