\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{MeStudio: crossing methylation and genomic features for comparative
epigenomic analyses}



\author[1]{Christopher Riccardi}%
\author[1]{Iacopo Passeri}%
\author[1]{Lisa Cangioli}%
\author[1]{Camilla Fagorzi}%
\author[1]{Alessio Mengoni}%
\author[1]{Marco Fondi}%
\affil[1]{University of Florence}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
DNA methylation is one of the most relevant epigenetic modifications. It
is present in eukaryotes and prokaryotes and is related to several
biological phenomena, including gene flow and adaptation to
environmental conditions. The widespread use of third-generation
sequencing technologies allows direct and easy detection of genome-wide
methylation profiles, offering increasing opportunities to understand
and exploit the epigenomics landscape of individuals and populations.
Here, we present MeStudio, a pipeline which allows to analyse and
combine genome-wide methylation profiles with genomic features. Outputs
report the presence of DNA methylation in coding sequences (CDS) and
noncoding sequences, including both intergenic sequences, and sequences
upstream to CDS. We show the usage and performances of MeStudio on a set
of single-molecule real time sequencing outputs from strains of the
bacterial species Sinorhizobium meliloti. MeStudio is freely available
under an open source GPLv3 license at
https://github.com/combogenomics/MeStudio%
\end{abstract}%



\sloppy


\textbf{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 \emph{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 \emph{et al.} , 2020).
Third-generation sequencing technologies, namely single molecule
real-time (SMRT) (Flusberg \emph{et al.} , 2010; Fang \emph{et al.} ,
2012) and nanopore ONT (Clarke \emph{et al.} , 2009; Simpson \emph{et
al.} , 2017) sequencing allow to directly identify the most commonly
methylated bases (Gouil and Keniry, 2019; S\selectlanguage{ngerman}ánchez-Romero and Casadesús,
2020; Rand \emph{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 \emph{et al.} , 2022)), as well as
for Restriction-Modification systems strain-by-strain or population
variation are documented (diCenzo \emph{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 \emph{et al.} , 2019; Teng \emph{et al.} , 2020; Hillary
and Marioni, 2021; Aryee \emph{et al.} , 2014; Bock \emph{et al.} ,
2005)), for a recent benchmarking see (Nunn \emph{et al.} , 2021)).
Recently, three packages have been released (Su \emph{et al.} , 2021;
Leger, 2020; De Coster \emph{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
species\emph{Sinorhizobium meliloti} .

\textbf{2 Design and implementation}

MeStudio consists of several tools that can be run individually or as
part of a pipeline and uses a naive string matching algorithm to map
motif sequences to the reference genome. The required input data consist
in only three files: i) a FASTA file containing the genome sequence, ii)
a genomic annotation file in GFF3 format and iii) another GFF3
containing the methylated nucleotide positions. The latter is
automatically generated from the output of the SMRTlink software of
Pacific Biosciences DNA sequencers. As a result, MeStudio produces
several files including: (i) a text file with summarized statistics
concerning the methylation occurrences along the genomic features, (ii)
distribution plots and, (iii) BED files containing protein annotation of
the genes in which methylated motifs have been found. A workflow is
provided in Figure 1.

\emph{2.1 Pre-processing}

To run MeStudio, a pre-processing python script named
\emph{ms\_replacR}has been implemented to produce consistent formatting
on the sequence identifiers from the genomic annotation,
sequencer-produced modified base calls, and the genomic sequence file.
To avoid possible inconsistencies at the sequence identifiers level (the
``seqid'' field) between FASTA and annotation files, we have implemented
a quality check in this regard. More details are provided in the
MeStudio manual on GitHub.

\emph{2.2 Core-processing}

The processing of the input files is handled by five executables which
we refer to as ``MeStudio core''. These components match the nucleotide
motifs to the genomic sequence and map them to the corresponding
category, which are extracted from the annotation file. Categories are
defined as follows: i) protein-coding genes with accordant (sense)
strand (CDS), ii) discordant (antisense) strand (nCDS), iii) regions
that fall between annotated genes (true intergenic, tIG), iv) regions
upstream to the reading frame of a gene, with accordant strand (US)
(Figure 1B). The current implementation uses a naive matching algorithm
to map motif sequences to the reference genome. During the matching
stage, each replicon or chromosome gets loaded into and both strands are
scanned for the presence of the motif sequences, which can hold
ambiguity characters. The resulting binary files are then processed by
another executable that is called for the task at hand. MeStudio core
crosses methylated bases positions relative to the reference sequence
start with the previously described features, producing GFF3 files that
serve as input for the final analysis stage. This is a computationally
expensive part of the pipeline in which multiple nested for loops and
calculations are performed. Integrating one motif on a four-contigs
genome (6,973,268 bp, 23,433 GANTC motif matches) took 0m27.116s on a
single AMD Opteron 6380 processor (2.5GHz).

\emph{2.3 Post-processing}

MeStudio implements a post-processing python script
named\emph{ms\_analyzR} which takes MeStudio core output as input. In
addition, to integrate comparative genomic analyses a
``gene\_presence\_abscence.csv'' file produced by Roary (Page \emph{et
al.} , 2015) can be used to define the methylation level and patterns of
core and dispensable genome fractions, as well as to annotate the
genes-coded proteins. \emph{ms\_analyzR} logs the total number of genes
found for each category (CDS, nCDS, tIG, US). Additionally, methylation
data are shown, such as i) total number of methylated sites, ii) total
number of methylated genes, iii) the ID of the most methylated gene
(geneID) and, iv) the product of that gene. Integrating data from Roary
is functional to characterize the geneID associated with the name of the
protein (as annotated by Prokka (Seemann, 2014)) as part of the core or
dispensable genome. All the information is saved into a log file,
together with plots accounting for the distribution of the methylations
(Fig. 2A). To ensure customizability, \emph{ms\_analyzR} also includes
two optional flags named ``---make\_chrom'' and ``---make\_bed''. The
``---make\_chrom'' flag saves into the previously specified output
directory the GFFs at ``chromosome level'' rather than ``category
level''. Each GFF produced will be characterized not by category (CDS,
nCDS, tIG and US) but by chromosomes (or contigs), maintaining the
MeStudio core-derived contents and layout unaltered. The
``---make\_bed'' flag produces a BED file for each feature in which is
reported: i) the \emph{chrom} column, with the name of each chromosome
or contig, ii) start and iii) end of the feature, iv) the name of the
geneID found in that interval, v) the number of methylations found for
geneID and lastly vi) the protein product of the ID. Information
contained in BED files can be readily used to plot the distribution of
the methylation density for each feature, making use of
the\emph{circlize} R package (https://github.com/jokergoo/circlize)
(Fig. 2B).

\emph{2.4 Tool-wide comparison}

MeStudio provides a novel amount of feature-level information that is
not present in other widely used genomic software packages. For
instance, Bedtools (https://bedtools.readthedocs.io/en/latest/) is a
well-known toolset for genomic applications through which it is possible
to detect methylation features regarding CpG island, but it is not
possible to extract information about CDS, nCDS, tIG and US regions as
it does not provide any figure about methylated motif occurrences.
Bioconductor also supplies packages that can be used for methylation
analysis such as ``\emph{GenomicRanges} ''
(https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html)
and ``\emph{motifmatchr} ''
(\emph{https://bioconductor.org/packages/release/bioc/html/motifmatchr.html}).
GenomicRanges allows to split the genome in predefined intervals but no
information about the genomic feature is produced. The
package\emph{motifmatchr} allows to find motifs along the genome but no
gene or protein annotations are included in the output. Moreover,
MeStudio simply takes as input for the motifs a text file which is a
more user-friendly format compared to the one required by
\emph{motifmatchr} . Table 1 provides a comparison of the features of
MeStudio and possible alternative tools.

\textbf{3 Case study}

In order to show the performance of MeStudio, a recently published SMRT
dataset was used (diCenzo \emph{et al.} , 2022) comparing some of the
methylation features of two \emph{Sinorhizobium meliloti} strains, 1021
and FSM-MA, grown until stationary phase in minimal medium (Table 2,
Figure 2B) (diCenzo \emph{et al.} , 2022). On the SMRT assembled reads
of the genomes of the two strains, MeStudio was able to identify a total
of 28 motifs (Table 2). All but six motifs (namely AGAAAAT, DCTGCAGGS,
RAGCWGCTY, RAGCWGCTY, RCTGCAGGS, TGGGCA) were common to both strains.
The number of retrieved methylated sites ranged from a few units
(especially for private motifs, those present in one strain only) to
several thousands (as GANTC, which is a classical motif methylated by
the CcrM DNA methylase and its involved in cell cycle regulation
(Mouammine and Collier, 2018). CDS and nCDS showed similar values, as
expected for methylation being present on both DNA strands. Intergenic
sequences (tIG) showed the lowest number of methylated sites, while
upstream sequences to a gene (UP), \emph{bona fide} corresponding to
putative promoter regions reported values generally one order of
magnitude higher than tIG and in some cases differences in values
between strains ranged around two-fold (e.g., CTYCCAG and GCCAGG).
Finally, the presence of motifs in one strain only, may suggest the
occurrence of strain-specific Restriction-Modification systems, though
the small number of methylated sites may also suggest alternative
hypotheses (i.e., methylation on some genomic regions only related to
regulation of expression at specific loci). Demo files for input and
output are available at\emph{https://github.com/combogenomics/MeStudio}.

\subsection*{4 Discussion}

{\label{discussion}}

We have reported here the description of a novel software (MeStudio) for
the analysis of DNA methylation profiles obtained by single molecule
real time sequencing. MeStudio has several novel and useful features
compared to the few existing tools, as it provides outputs in the form
of GFF and BED files which contain information on the position of
methylated sites and methylated motifs, the number of methylated sites
and profiles for each genomic feature and graphical outputs. The genomic
features analysed include genic and intergenic regions (hence comprising
putative promoters), allowing the formulation of hypotheses related to
the importance of DNA methylation on regulation of gene expression and
on other relevant biological phenomena. Besides being developed for
prokaryotic genomes, MeStudio can handle any kind of sequence, by simply
providing a suitable set of input files (Figure 1). By providing
information on motif occurrence and genomic localization, MeStudio
provides the basis for comparative analyses of DNA methylation profiles
among strains, in terms of evolutionary studies on populations and
species and epigenomic modifications during adaptation and development.

Finally, MeStudio is very user friendly given its easy installation and
its possibility to be run as a pipeline, in a single command line call.
We've developed the scripts in a Mac and Linux kernel environments, with
the possibility in the near future to expand to Windows platforms as
well.

\textbf{Funding}

This work was supported by MIUR, Programma Nazionale di Ricerche in
Antartide 2018, (grant PNRA18\_00335,\emph{https://www.pnra.aq/}) and
PRIN (Programmi di Ricerca Scientifica di Rilevante Interesse Nazionale
Escaping the ESKAPEs: integrated pipelines for new antibacterial drug,
20208LLXEJ\textbackslash{}\_002) grants to MF and by the grant
MICRO4Legumes, D.M.n.89267 (Italian Ministry of Agriculture) to AM. LC
is supported by a PhD fellowship from MICRO4Legumes. CF is supported by
a post-doctoral fellowship from the H2020 ERA-NETs SUSFOOD2 and CORE
Organic Cofund, under the Joint SUSFOOD2/CORE Organic Call 2019.

\textbf{Data Availability statement}

The data that support the findings of this study are openly available in
GitHub at https://github.com/combogenomics/MeStudio.

\emph{Conflict of Interest} : none declared

\textbf{References}

Aryee,M.J. \emph{et al.} (2014) Minfi: a flexible and comprehensive
Bioconductor package for the analysis of Infinium DNA methylation
microarrays. \emph{Bioinformatics} , \textbf{30} , 1363--1369.

Bock,C. \emph{et al.} (2005) BiQ Analyzer: visualization and quality
control for DNA methylation data from bisulfite
sequencing.\emph{Bioinformatics} , \textbf{21} , 4067--4068.

Chen,P. \emph{et al.} (2020) Bacterial Epigenomics: Epigenetics in the
Age of Population Genomics. In, Tettelin,H. and Medini,D.
(eds),\emph{The Pangenome: Diversity, Dynamics and Evolution of Genomes}
. Springer, Cham (CH).

Clarke,J. \emph{et al.} (2009) Continuous base identification for
single-molecule nanopore DNA sequencing. \emph{Nat. Nanotechnol.}
,\textbf{4} , 265--270.

De Coster,W. \emph{et al.} (2020) Methplotlib: analysis of modified
nucleotides from nanopore sequencing. \emph{Bioinformatics} ,\textbf{36}
, 3236--3238.

diCenzo,G.C. \emph{et al.} (2022) DNA Methylation in Ensifer Species
during Free-Living Growth and during Nitrogen-Fixing Symbiosis with
Medicago spp. \emph{mSystems} .

Fang,G. \emph{et al.} (2012) Genome-wide mapping of methylated adenine
residues in pathogenic Escherichia coli using single-molecule real-time
sequencing. \emph{Nat. Biotechnol.} , \textbf{30} , 1232--1239.

Flusberg,B.A. \emph{et al.} (2010) Direct detection of DNA methylation
during single-molecule, real-time sequencing. \emph{Nat. Methods}
,\textbf{7} , 461--465.

Gouil,Q. and Keniry,A. (2019) Latest techniques to study DNA
methylation. \emph{Essays Biochem.} , \textbf{63} , 639--648.

Hillary,R.F. and Marioni,R.E. (2021) MethylDetectR: a software for
methylation-based health profiling.

Jones,P.A. (2012) Functions of DNA methylation: islands, start sites,
gene bodies and beyond. \emph{Nat. Rev. Genet.} , \textbf{13} ,
484--492.

Leger,A. (2020) a-slide/pycoMeth.

Moler,E.R.V. \emph{et al.} (2019) Population Epigenomics: Advancing
Understanding of Phenotypic Plasticity, Acclimation, Adaptation and
Diseases. In, Rajora,O.P. (ed), \emph{Population Genomics: Concepts,
Approaches and Applications} , Population Genomics. Springer
International Publishing, Cham, pp. 179--260.

Mouammine,A. and Collier,J. (2018) The impact of DNA methylation in
Alphaproteobacteria. \emph{Mol. Microbiol.} , \textbf{110} , 1--10.

Müller,F. \emph{et al.} (2019) RnBeads 2.0: comprehensive analysis of
DNA methylation data. \emph{Genome Biol.} , \textbf{20} , 55.

Nunn,A. \emph{et al.} (2021) Comprehensive benchmarking of software for
mapping whole genome bisulfite data: from read alignment to DNA
methylation analysis. \emph{Brief. Bioinform.} , \textbf{22} , bbab021.

Page,A.J. \emph{et al.} (2015) Roary: rapid large-scale prokaryote pan
genome analysis. \emph{Bioinformatics} , \textbf{31} , 3691--3693.

Rand,A.C. \emph{et al.} (2017) Mapping DNA methylation with
high-throughput nanopore sequencing. \emph{Nat. Methods} , \textbf{14} ,
411--413.

Sánchez-Romero,M.A. and Casadesús,J. (2020) The bacterial
epigenome.\emph{Nat. Rev. Microbiol.} , \textbf{18} , 7--20.

Sánchez-Romero,M.A. and Casadesús,J. (2021) Waddington's Landscapes in
the Bacterial World. \emph{Front. Microbiol.} , \textbf{12} .

Seemann,T. (2014) Prokka: rapid prokaryotic genome
annotation.\emph{Bioinformatics} , \textbf{30} , 2068--2069.

Simpson,J.T. \emph{et al.} (2017) Detecting DNA cytosine methylation
using nanopore sequencing. \emph{Nat. Methods} , \textbf{14} , 407--410.

Su,S. \emph{et al.} (2021) NanoMethViz: An R/Bioconductor package for
visualizing long-read methylation data. \emph{PLOS Comput. Biol.}
,\textbf{17} , e1009524.

Teng,C.-S. \emph{et al.} (2020) MethGET: web-based bioinformatics
software for correlating genome-wide DNA methylation and gene
expression. \emph{BMC Genomics} , \textbf{21} , 375.

\textbf{Table 1} . MeStudio features compared to existing tools\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Tool} & \textbf{Programming language} & \textbf{Motif
recognition} & \textbf{Motif matching with respect to genomic features}
& \textbf{Graphical outputs} & \textbf{Reference}\tabularnewline
\midrule
\endhead
\textbf{MeStudio} & Python, C & yes & yes & yes & This
study\tabularnewline
GenomicRanges & R, C & No & No & Yes & Bioncoductor
package\tabularnewline
motifmatchr & R, C++ & Yes & Yes (only providing genomic ranges) & Yes &
Bioncoductor package\tabularnewline
Meta-epigenomics & & & & &
https://github.com/hoonjeseong/Meta-epigenomics\tabularnewline
\textbf{Methplotlib} & Python, Bash & No & No & Yes & De Coster \emph{et
al.} (2020)\tabularnewline
\textbf{a-slide/pycoMeth} & Python, Bash & No & No & Yes & Leger.
(2020)\tabularnewline
\textbf{NanoMethViz} & Python, Bash & No & No & Yes & Su \emph{et al}.
(2021)\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2. Number of methylated sites detected in S. meliloti
strains FSMA-MA and 1021.} CDS, coding sequence; nCDS, coding sequence
reverse strand; tIG, intergenic sequence between two genes in opposite
directions; US, upstream sequence to a coding sequence. N.d., not
detected.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
& \textbf{FSM-MA} & \textbf{FSM-MA} & \textbf{FSM-MA} & \textbf{FSM-MA}
& \textbf{1021} & \textbf{1021} & \textbf{1021} &
\textbf{1021}\tabularnewline
\midrule
\endhead
\textbf{Motif} & \textbf{CDS} & \textbf{nCDS} & \textbf{tIG} &
\textbf{UP} & \textbf{CDS} & \textbf{nCDS} & \textbf{tIG} &
\textbf{UP}\tabularnewline
ACGGAG & 50 & 53 & 7 & 66 & 64 & 55 & 8 & 69\tabularnewline
AGAAAAT & ~N.d. & N.d. & N.d. & N.d. & 6 & 8 & 1 & 9\tabularnewline
BNNCGATCGV & 368 & 397 & 16 & 364 & 386 & 450 & 15 & 408\tabularnewline
BYCGATCG & 80 & 119 & 8 & 125 & 91 & 110 & 5 & 114\tabularnewline
CCCGGG & 26 & 35 & 3 & 42 & 33 & 43 & 3 & 46\tabularnewline
CGATCGV & 405 & 402 & 19 & 372 & 409 & 426 & 16 & 387\tabularnewline
CTCGAG & 143 & 137 & 8 & 144 & 127 & 170 & 14 & 176\tabularnewline
CTYCCAG & 14 & 28 & 2 & 31 & 20 & 51 & 4 & 58\tabularnewline
DCTGCAGGS & 13 & 15 & 1 & 17 & ~N.d. & ~N.d. & ~N.d. &
~N.d.\tabularnewline
GANTC & 4193 & 4193 & 1590 & 2719 & 4196 & 4196 & 1575 &
2731\tabularnewline
GCCAGG & 22 & 50 & 3 & 52 & 84 & 111 & 4 & 113\tabularnewline
GCCGGCH & 360 & 292 & 28 & 294 & 436 & 412 & 32 & 384\tabularnewline
GCCGGCYD & 151 & 143 & 10 & 151 & 201 & 189 & 17 & 197\tabularnewline
GCRDB & 3312 & 3234 & 476 & 1780 & 3714 & 3604 & 588 &
1929\tabularnewline
GNCGATCGVC & 97 & 90 & 4 & 90 & 113 & 111 & 2 & 108\tabularnewline
RAGCWGCTY & 12 & 16 & 3 & 21 & ~N.d. & ~N.d. & ~N.d. &
~N.d.\tabularnewline
RCCAGCC & 39 & 64 & 2 & 68 & 61 & 70 & 2 & 70\tabularnewline
RCGATCGGC & 66 & 25 & 2 & 27 & 59 & 30 & 3 & 36\tabularnewline
RCTGCAGGS & 13 & 14 & 1 & 16 & ~N.d. & ~N.d. & ~N.d. &
~N.d.\tabularnewline
RGATCY & 61 & 62 & 3 & 63 & 84 & 92 & 9 & 106\tabularnewline
SCTCGAG & 112 & 114 & 7 & 117 & 107 & 163 & 11 & 167\tabularnewline
TCGWCGA & 291 & 224 & 8 & 222 & 197 & 132 & 9 & 143\tabularnewline
TGGGCA & ~N.d. & ~N.d. & ~N.d. & ~N.d. & 35 & 32 & 2 & 36\tabularnewline
VGCCGGCCC & 11 & 16 & 2 & 19 & 20 & 25 & 3 & 30\tabularnewline
VNCGATCGV & 396 & 388 & 15 & 360 & 419 & 431 & 16 & 392\tabularnewline
YCGATCGD & 94 & 127 & 9 & 127 & 80 & 100 & 3 & 95\tabularnewline
YCGGCCGRV & 123 & 135 & 16 & 153 & 158 & 158 & 12 & 171\tabularnewline
YCTGCAG & 41 & 45 & 1 & 48 & 51 & 54 & 5 & 61\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 1} : MeStudio overview. A) Workflow. Each blue block
represents input files. The green blocks indicate the scripts. The gray
boxes indicate output files. B) Graphical representation of the used
terminology; CDS, coding sequence; nCDS, coding sequence reverse strand;
tIG, intergenic sequence between two genes in opposite directions; US,
upstream sequence to a coding sequence, viz. intergenic sequence between
two genes having the same orientation. See text for details.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2} : Main graphical outputs of MeStudio and comparison of
methylation profiles between genomes. A) Sample plot of GANTC motif
in\emph{S. meliloti} FSM-MA. Y-axis reports geneIDs, whereas X-axis
reports the number of methylations found for each geneID. GeneIDs are
referred to the annotation (see GitHub repository for the annotation
files:\emph{https://github.com/combogenomics/MeStudio}). B) Circular
density plot of GANTC and GCCCGGCH motifs in FSM-MA and 1021 strains of
\emph{Sinorhizobium meliloti} . The outer circle represents the genome
annotation of the contigs of the strain (black lines indicate the
position of CDS). Each inner circle represents a different category of
methylated sites, CDS (red), nCDS (blue), tIG (purple) and US (yellow).
The bars of each plot indicate the values for each category.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig1-MeStudio/Fig1-MeStudio}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig2-MeStudio-6/Fig2-MeStudio-6}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

