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Related Experiment Video

Updated: May 26, 2026

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

slideimp: efficient imputation of DNA methylation data.

Hung Pham1,2, Adam P Lombroso1,2, E Cansu Cevik2

  • 1Yale Child Study Center, Yale School of Medicine, New Haven, CT 06520, United States.

Bioinformatics (Oxford, England)
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

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slideimp is a new R package for DNA methylation data imputation. It offers faster runtimes and reduced memory usage for microarray and whole-genome data using K-nearest neighbor and Principal Component Analysis methods.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • DNA methylation (DNAm) data imputation is crucial for analyzing high-throughput genomic studies.
  • Existing imputation methods may face challenges with large-scale datasets like whole-genome DNAm data.
  • Optimization of imputation algorithms is needed for improved accuracy and efficiency.

Purpose of the Study:

  • To develop and optimize an R package, slideimp, for accurate and efficient imputation of DNA methylation data.
  • To extend K-nearest neighbor (K-NN) and Principal Component Analysis (PCA) imputation methods with novel modes.
  • To address the computational demands of both microarray and whole-genome DNAm data imputation.

Main Methods:

  • Developed the R package 'slideimp' implementing K-NN and PCA imputation.

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Last Updated: May 26, 2026

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13:47

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Published on: February 24, 2015

Methylated DNA Immunoprecipitation
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  • Incorporated grouped and sliding-window modes for enhanced imputation.
  • Evaluated performance on DNA methylation microarray (GSE286313, EPICv2) and whole-genome datasets.

    • Main Results:

    • slideimp achieved significant speed-up (≈12-28× faster) and memory reduction (≈3-6× less peak memory) for DNAm microarray imputation.
    • Demonstrated high imputation accuracy for whole-genome DNAm data.
    • The package is available on CRAN and its code is accessible on GitHub.

    Conclusions:

    • slideimp provides an accurate and efficient solution for imputing both microarray and whole-genome DNA methylation data.
    • The package optimizes K-NN and PCA imputation through novel modes, enhancing performance.
    • slideimp offers a valuable tool for researchers working with large-scale DNA methylation datasets.