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MLML2R: an R package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions

Kiihl Samara F. (), Martinez-Garrido Maria Jose, Domingo-Relloso Arce, Bermudez Jose and Tellez-Plaza Maria
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Kiihl Samara F.: Department of Statistics, State University of Campinas, Campinas, Sao Paulo 13083-859, Brazil
Bermudez Jose: University of Valencia, Department of Statistics and Operations Research, Valencia 46100, Spain
Tellez-Plaza Maria: Institute for Biomedical Research Hospital Clinic of Valencia, Valencia 46010, Spain

Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 1, 6

Abstract: Accurately measuring epigenetic marks such as 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) at the single-nucleotide level, requires combining data from DNA processing methods including traditional (BS), oxidative (oxBS) or Tet-Assisted (TAB) bisulfite conversion. We introduce the R package MLML2R, which provides maximum likelihood estimates (MLE) of 5-mC and 5-hmC proportions. While all other available R packages provide 5-mC and 5-hmC MLEs only for the oxBS+BS combination, MLML2R also provides MLE for TAB combinations. For combinations of any two of the methods, we derived the pool-adjacent-violators algorithm (PAVA) exact constrained MLE in analytical form. For the three methods combination, we implemented both the iterative method by Qu et al. [Qu, J., M. Zhou, Q. Song, E. E. Hong and A. D. Smith (2013): “Mlml: consistent simultaneous estimates of dna methylation and hydroxymethylation,” Bioinformatics, 29, 2645–2646.], and also a novel non iterative approximation using Lagrange multipliers. The newly proposed non iterative solutions greatly decrease computational time, common bottlenecks when processing high-throughput data. The MLML2R package is flexible as it takes as input both, preprocessed intensities from Infinium Methylation arrays and counts from Next Generation Sequencing technologies. The MLML2R package is freely available at

Keywords: DNA hydroxymethylation; DNA methylation; Maximum likelihood (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/sagmb-2018-0031

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