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Processing and Analysis of Hi-C Data on Bacteria.

Andreas Hofmann1, Dieter W Heermann2

  • 1Institute for Theoretical Physics, Heidelberg University, Heidelberg, Germany.

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|August 16, 2018
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This study details processing next-generation sequencing Hi-C data to create genome contact maps. It provides methods for analyzing bacterial Hi-C datasets and assessing their quality.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Three-dimensional genome organization is crucial for cellular function.
  • Next-generation sequencing (NGS) technologies enable genome-wide contact detection.
  • Chromosome conformation capture (3C) based methods, like Hi-C, offer topological insights.

Purpose of the Study:

  • To review the computational steps for processing Hi-C sequencing data.
  • To generate a final contact probability map from raw data.
  • To present strategies for assessing Hi-C dataset quality.

Main Methods:

  • Utilizing publicly available Hi-C datasets from various bacterial species.
  • Describing the bioinformatics pipeline for Hi-C data processing.
  • Implementing quality control metrics for Hi-C experiments.

Main Results:

  • Demonstration of a complete Hi-C data processing workflow.
  • Generation of contact probability maps illustrating genome architecture.
  • Established methods for evaluating the reliability of Hi-C data.

Conclusions:

  • The described workflow enables the analysis of three-dimensional genome organization using Hi-C data.
  • Quality assessment strategies ensure the robustness of Hi-C findings.
  • This approach is applicable to diverse bacterial genomes.