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
Additional contact information
Kiihl Samara F.: Department of Statistics, State University of Campinas, Campinas, Sao Paulo 13083-859, Brazil
Martinez-Garrido Maria Jose: University of Valencia, Department of Statistics and Operations Research, Valencia 46100, Spain
Domingo-Relloso Arce: University of Valencia, Department of Statistics and Operations Research, Valencia 46100, Spain
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 https://CRAN.R-project.org/package=MLML2R.

Keywords: DNA hydroxymethylation; DNA methylation; Maximum likelihood (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2018-0031 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:18:y:2019:i:1:p:6:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2018-0031

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().