Notos - a galaxy tool to analyze CpN observed expected ratios for inferring DNA methylation types.

Abstract:

BACKGROUND:DNA methylation patterns store epigenetic information in the vast majority of eukaryotic species. The relatively high costs and technical challenges associated with the detection of DNA methylation however have created a bias in the number of methylation studies towards model organisms. Consequently, it remains challenging to infer kingdom-wide general rules about the functions and evolutionary conservation of DNA methylation. Methylated cytosine is often found in specific CpN dinucleotides, and the frequency distributions of, for instance, CpG observed/expected (CpG o/e) ratios have been used to infer DNA methylation types based on higher mutability of methylated CpG. RESULTS:Predominantly model-based approaches essentially founded on mixtures of Gaussian distributions are currently used to investigate questions related to the number and position of modes of CpG o/e ratios. These approaches require the selection of an appropriate criterion for determining the best model and will fail if empirical distributions are complex or even merely moderately skewed. We use a kernel density estimation (KDE) based technique for robust and precise characterization of complex CpN o/e distributions without a priori assumptions about the underlying distributions. CONCLUSIONS:We show that KDE delivers robust descriptions of CpN o/e distributions. For straightforward processing, we have developed a Galaxy tool, called Notos and available at the ToolShed, that calculates these ratios of input FASTA files and fits a density to their empirical distribution. Based on the estimated density the number and shape of modes of the distribution is determined, providing a rational for the prediction of the number and the types of different methylation classes. Notos is written in R and Perl.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Bulla I,Aliaga B,Lacal V,Bulla J,Grunau C,Chaparro C

doi

10.1186/s12859-018-2115-4

subject

Has Abstract

pub_date

2018-03-27 00:00:00

pages

105

issue

1

issn

1471-2105

pii

10.1186/s12859-018-2115-4

journal_volume

19

pub_type

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