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Epigenomics coverage data extraction and aggregation in R with tidyCoverage.

Jacques Serizay1, Romain Koszul1

  • 1Institut Pasteur, CNRS UMR 3525, Université Paris Cité, Unité Régulation Spatiale des Génomes, Paris 75015, France.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

The tidyCoverage R package offers a framework for analyzing genomic coverage data using tidy data principles. It enhances epigenomic research by improving data accessibility and reproducibility for genome-wide analyses.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genomic coverage data analysis is crucial for epigenomic research.
  • Existing tools may lack intuitive frameworks for exploring large-scale genomic datasets.
  • Tidy data principles offer a structured approach to data manipulation.

Purpose of the Study:

  • To introduce the tidyCoverage R package for intuitive investigation of genomic track data.
  • To provide a framework for efficient extraction, manipulation, and visualization of genome-wide coverage data.
  • To facilitate the integration of genomic data into epigenomic analysis workflows.

Main Methods:

  • Development of tidyCoverage R package based on tidy data manipulation.
  • Implementation of CoverageExperiment and AggregatedCoverage classes extending SummarizedExperiment.
  • Facilitation of data extraction and visualization at individual and aggregated genomic loci.

Main Results:

  • tidyCoverage enables intuitive exploration of genomic track collections.
  • The package allows rapid visualization of track coverage at thousands of genomic loci.
  • It seamlessly integrates with the Bioconductor ecosystem for advanced epigenomic analyses.

Conclusions:

  • tidyCoverage provides a valuable tool for advancing epigenomics research.
  • The package promotes consistency, reproducibility, and accessibility in genomic data analysis.
  • It empowers researchers to efficiently analyze and visualize genome-wide coverage data.