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Effective normalization for copy number variation in Hi-C data.

Nicolas Servant1,2,3, Nelle Varoquaux4,5, Edith Heard6

  • 1Institut Curie, PSL Research University, Paris, F-75005, France. nicolas.servant@curie.fr.

BMC Bioinformatics
|September 8, 2018
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Summary
This summary is machine-generated.

Copy-number variations challenge Hi-C data normalization. New methods, CAIC and LOIC, accurately model these effects, improving analysis of rearranged genomes.

Keywords:
CancerCopy-numberHi-CNormalization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate normalization of sequencing data, especially Hi-C, is crucial for analysis.
  • Standard Hi-C normalization methods assume equal genomic region interaction, which is often not the case.

Purpose of the Study:

  • To investigate the impact of copy-number variations (CNVs) on Hi-C data normalization.
  • To develop improved normalization methods for Hi-C data affected by CNVs.

Main Methods:

  • Developed a simulation model to predict CNV effects on Hi-C contact maps.
  • Extended matrix balancing methods to model CNV effects, creating LOIC (retain effects) and CAIC (remove effects) approaches.

Main Results:

  • Standard normalization methods fail to correct for CNVs.
  • The proposed LOIC and CAIC methods effectively model CNV effects in Hi-C data.
  • These methods improve downstream analysis of three-dimensional genome organization in rearranged genomes.

Conclusions:

  • Dedicated methods are essential for analyzing Hi-C data from rearranged genomes, such as in cancer.
  • Both CAIC and LOIC methods demonstrate robust performance on simulated and real Hi-C data, catering to different analytical needs.