1 Introduction
Understanding organism adaptation to variable environmental conditions
is pivotal for weighting the relevance of natural selection over species
and population evolution. Phenotypic plasticity, stress responses and
acclimation display significant contribution from epigenetic mechanisms
(Moler et al. , 2019). Among epigenetic modifications, DNA
methylation has been shown to be key in the control of several
biological phenomena in eukaryotes and prokaryotes (Jones, 2012) and in
the last years the study of variation in epigenetic response is stirring
the attention of several investigators (Chen et al. , 2020).
Third-generation sequencing technologies, namely single molecule
real-time (SMRT) (Flusberg et al. , 2010; Fang et al. ,
2012) and nanopore ONT (Clarke et al. , 2009; Simpson et
al. , 2017) sequencing allow to directly identify the most commonly
methylated bases (Gouil and Keniry, 2019; Sánchez-Romero and Casadesús,
2020; Rand et al. , 2017). These methods are boosting genome-wide
DNA methylation studies, especially in prokaryotes, where the compact
size of genomes allows the generation of whole-genome methylome with
relative ease. In prokaryotic microorganisms DNA methylation is playing
various roles, which span from the control of cell cycle, the protection
against phages (e.g. Restriction-Modification systems), and regulation
of gene expression (see for examples (Sánchez-Romero and Casadesús,
2021)). Concerning cell cycle control, genome-wide DNA methylation
profiles have been shown to vary in ecologically relevant contexts (e.g.
bacterial differentiation, (diCenzo et al. , 2022)), as well as
for Restriction-Modification systems strain-by-strain or population
variation are documented (diCenzo et al. , 2022).
Consequently, the interest toward computational pipelines which can
easily profile DNA methylation features in a genome-wide manner (thus
allowing to compare strains and individuals across multiple conditions)
is growing. Several tools have been developed for the analysis of DNA
methylation profiles deriving from bisulphite sequencing and microarrays
(e.g. (Müller et al. , 2019; Teng et al. , 2020; Hillary and
Marioni, 2021; Aryee et al. , 2014; Bock et al. , 2005)),
for a recent benchmarking see (Nunn et al. , 2021)). Recently,
three packages have been released (Su et al. , 2021; Leger, 2020;
De Coster et al. , 2020), which allow to visualize methylation
profiles from SMRT or ONT sequencing data. A recent tool on GitHub has
also been developed to specifically analyse DNA methylation profiles on
metagenomic data (https://github.com/hoonjeseong/Meta-epigenomics).
However, to the best of our knowledge, no specific pipeline has been
developed for extracting DNA methylation information from sequencing
data and allowing a direct quantification/comparison of the position of
methylated sites with respect to genome-derived features, such as coding
and noncoding sequences and report outputs which can be used in
population epigenomic analyses.
Here we present MeStudio, a pipeline for SMRT sequencing methylation
data integration and visualization. MeStudio combines methylation data
with genome sequence and annotation to facilitate the extraction of
biological information from DNA methylation profiles and to visualize
the results of these analyses. We show the usage of MeStudio on a set of
SMRT outputs from two strains of the bacterial speciesSinorhizobium meliloti .