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A varying-coefficient model for the analysis of methylation sequencing data

Published: 24 July 2024 Publication History

Abstract

DNA methylation is an important epigenetic modification involved in gene regulation. Advances in the next generation sequencing technology have enabled the retrieval of DNA methylation information at single-base-resolution. However, due to the sequencing process and the limited amount of isolated DNA, DNA-methylation-data are often noisy and sparse, which complicates the identification of differentially methylated regions (DMRs), especially when few replicates are available. We present a varying-coefficient model for detecting DMRs by using single-base-resolved methylation information. The model simultaneously smooths the methylation profiles and allows detection of DMRs, while accounting for additional covariates. The proposed model takes into account possible overdispersion by using a beta-binomial distribution. The overdispersion itself can be modeled as a function of the genomic region and explanatory variables. We illustrate the properties of the proposed model by applying it to two real-life case studies.

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Highlights

Flexible modeling of bisulfite sequencing data assuming beta-binomial distribution.
Estimation of smooth effects of explanatory variables.
Detection of differentially methylated regions.
Applicable to targeted and whole-genome-sequencing data.

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Published In

cover image Computational Biology and Chemistry
Computational Biology and Chemistry  Volume 111, Issue C
Aug 2024
138 pages

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 24 July 2024

Author Tags

  1. Methylation sequencing
  2. CpG site
  3. Differentially methylated regions
  4. Beta-binomial model
  5. Varying-coefficient model
  6. Smoothing splines

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