A web-based bioinformatics software for correlating DNA methylation and gene expression. The web user-interface can be accessed through https://paoyang.ipmb.sinica.edu.tw/Software.html .
- Correlation analyses of genome-wide DNA methylation and gene expression
- Ordinal association analyses with genes ranked by gene expression level
- Distribution of DNA methylation by groups of genes with different expression level
- Average methylation level profiles with different expression groups around genes
- Gene level association between changes of DNA methylation and changes of gene expression
- Heatmap representation of DNA methylation and gene expression data together
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Linux environment
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Python 2.7 (Type " python -V" to see the installed version. Python2.7 could be downloaded from http://www.python.org/download/)
Python modules are needed. To install the packages, use the following commands on linux terminal:
$ pip install numpy
$ pip install pandas
$ pip install matplotlib
$ pip install seaborn
$ pip install scipy
$ pip install argparse
$ pip install sklearn
$ pip install openpyxl
$ pip install pyBigWig
Here is the example data (322MB) to explore the tool’s functions.
- DNA Methylation
CGmap.gz file (need gzip compressed format) is the output of BS-Seeker2.
chr1 G 13538 CG CG 0.6 6 10 chr1 G 13539 CHG CC 0.0 0 9 chr1 G 13541 CHH CA 0.0 0 9 chr1 G 13545 CHH CA 0.0 0 8The methylation calling files from other aligners/callers, MethGET provides a python script (methcalls2CGmap.py) to convert them to CGmap.gz, including CX report files generated by Bismark, the methylation calls generated by methratio.py in BSMAP (v2.73), the allc files by methylpy, and the TSV files exported from the methylation calling status with METHimpute.
usage: methcalls2CGmap.py [-h] [-n FILENAME] [-f {bismark,bsmap,methylpy,methimpute}] optional arguments: -h, --help show this help message and exit Input format: -n FILENAME, --filename FILENAME the file name that the users want to convert to CGMap format -f {bismark,bsmap,methylpy,methimpute}, --format {bismark,bsmap,methylpy,methimpute} the type of file to CGmapExample for comverting methylation calls to CGmap.gz:
# bismark to CGmap.gz python methcalls2CGmap.py -n CX_report.txt.gz -f bismark
- Gene Expression File
Tab-delimited text file: contain the gene names and gene expression values.
AT1G01310 0.152868
AT1G01320 9.06088
AT1G01340 14.1157
AT1G01350 24.8099
AT1G01355 0.082233
AT1G01360 42.5391
AT1G01370 8.28111
- Gene Annotation File
gene annotation in GTF file: It may be available from EnsemblPlant or ensemble FTP
- Sample Description File (tab-delimited)
Sample list
trt_1 trt_1_CGmap.gz trt_1_exp.txt trt
trt_2 trt_2_CGmap.gz trt_2_exp.txt trt
ctrl_1 ctrl_1_CGmap.gz ctrl_1_exp.txt ctrl
ctrl_2 ctrl_2_CGmap.gz ctrl_2_exp.txt ctrl
- Gene Annotation File
gene annotation in GTF file
Use the script preprocess.py to preprocess the data for downstream analyses
Usage:
preprocess.py {-s <samplelist> | -n <samplename> -f <cgmap> -e <expressionfile>} -g <gtf> [options]
optional arguments:
-h, --help show this help message and exit
For samplelist:
-s SAMPLELIST, --samplelist SAMPLELIST
input sample description file
For individual data:
-n SAMPLENAME, --samplename SAMPLENAME
setting the name of the set of data. This determines
the names of output files for downstream analyses
-f CGMAPFILENAME, --cgmapfilename CGMAPFILENAME
input CGmap file
-e EXPRESSIONFILE, --expressionfile EXPRESSIONFILE
input gene expression file
General options:
-g GTFFILE, --gtffile GTFFILE
input gene annotation file
-c CUTOFF, --cutoff CUTOFF
minimum cytosines that are covered by reads
--TE {True,False} The input GTF file is TE GTF. The TE methylation each
gene will be calculated in GeneMean.txt. The TE
analyses can be conducted if the target region is
'Gene_Body'Example for preprocess:
# individual data
python preprocess.py -n demo -f WT.CGmap.gz -e WT_exp.txt -g genes.gtf
# for samplelist
python preprocess.py -s samplelist.txt -g genes.gtf
Usage:
usage: correlation.py [-h] [-n SAMPLENAME] [-p {scatter,kernel}]
[-c {CG,CHG,CHH,all}]
[-t {Promoter,Gene_Body,Exon,Intron,all}]
[-cor {False,pearson,spearman}] [-re0 {True,False}]
[-thrs THRESHOLD] [-xlim XLIMIT] [-ylim YLIMIT]
[--dotsize DOTSIZE] [--textsize TEXTSIZE]
[--ticksize TICKSIZE] [--labelsize LABELSIZE]
[--titlesize TITLESIZE]
optional arguments:
-h, --help show this help message and exit
Required arguments:
-n SAMPLENAME, --samplename SAMPLENAME
the name of the set of data
-p {scatter,kernel}, --plot {scatter,kernel}
create scatterplot or kernel density plot, default is 'scatter'
-c {CG,CHG,CHH,all}, --context {CG,CHG,CHH,all}
choose the context of methylation, default 'all' is to choose them all
-t {Promoter,Gene_Body,Exon,Intron,all}, --target {Promoter,Gene_Body,Exon,Intron,all}
choose the genomic location of methylation, default 'all' is to choose them all
Important general arguments:
-cor {False,pearson,spearman}, --correlation {False,pearson,spearman}
select the type of correlation, default is 'pearson'
-re0 {True,False}, --skip0 {True,False}
Whether genes with 0 expression value would be included. Default 'False' is to include them
-thrs THRESHOLD, --threshold THRESHOLD
Whether skip genes with expression value that is too high, default is to skip genes higher than 2000
expression. If want to include them, please set 'None'
-xlim XLIMIT, --xlimit XLIMIT
Nemeric zoom in the gene expression value to clearly understand the distribution
-ylim YLIMIT, --ylimit YLIMIT
Nemeric zoom in the DNA methylation level to clearly understand the distribution
Graphing arguments:
--dotsize DOTSIZE dotsize, default is 30
--textsize TEXTSIZE textsize, default is 20
--ticksize TICKSIZE ticksize, default is 15
--labelsize LABELSIZE
labelsize, default is 20
--titlesize TITLESIZE
titlesize, default is 20
Example for correlation:
# scatter plot
python correlation.py –n demo –p scatter
Usage:
usage: ordinal.py [-h] [-n SAMPLENAME] [-c {CG,CHG,CHH,all}]
[-t {Gene_Body,Promoter,Exon,Intron,all}]
[-re0 {True,False}] [-thrs THRESHOLD] [-shsca {True,False}]
[-line {True,False}] [-smoo SMOOTH_N] [-ylim YLIMIT]
[--dotsize DOTSIZE] [--textsize TEXTSIZE]
[--ticksize TICKSIZE] [--labelsize LABELSIZE]
[--titlesize TITLESIZE] [--legendsize LEGENDSIZE]
optional arguments:
-h, --help show this help message and exit
Required arguments:
-n SAMPLENAME, --samplename SAMPLENAME
the name of the set of data
-c {CG,CHG,CHH,all}, --context {CG,CHG,CHH,all}
choose the context of methylation, default 'all' is to choose them all
-t {Gene_Body,Promoter,Exon,Intron,all}, --target {Gene_Body,Promoter,Exon,Intron,all}
choose the genomic location of methylation, default 'all' is to choose them all
Important general arguments:
-re0 {True,False}, --skip0 {True,False}
whether genes with 0 expression value would be included. Default 'False' is to include them
-thrs THRESHOLD, --threshold THRESHOLD
whether to skip genes with expression value that is too high, default is to skip genes higher than 2000.
If want to include them, please set 'None'
chart visulaization arguments:
-shsca {True,False}, --showscatterplot {True,False}
whether to show the scatterplot, default is to show
-line {True,False}, --smoothline {True,False}
whether to show the fitting curves, default is to show
-smoo SMOOTH_N, --smooth_N SMOOTH_N
set the number of ticks to average when drawing the fitting curve, default is 500
-ylim YLIMIT, --ylimit YLIMIT
numeric zoom in the DNA methylation level to clearly understand the distribution
Graphing arguments:
--dotsize DOTSIZE dotsize, default is 20
--textsize TEXTSIZE textsize, default is 25
--ticksize TICKSIZE ticksize, default is 15
--labelsize LABELSIZE
labelsize,, default is 25
--titlesize TITLESIZE
titlesize, default is 25
--legendsize LEGENDSIZE
legendsize, default is 20
Example for ordinal association:
# scatterplot and fitting curves
python ordinal.py -n demo
Usage:
usage: grouping.py [-h] [-n SAMPLENAME] [-p {boxplot,violinplot}]
[-c {CG,CHG,CHH,all}]
[-t {Gene_Body,Promoter,Exon,Intron,all}]
[-nb NUMBEROFGROUP] [-re0 {True,False}]
[-cor {False,pearson,spearman}] [-mean {True,False}]
[-sf {True,False}] [-ylim YLIMIT] [--dotsize DOTSIZE]
[--textsize TEXTSIZE] [--ticksize TICKSIZE]
[--labelsize LABELSIZE] [--titlesize TITLESIZE]
[--legendsize LEGENDSIZE]
optional arguments:
-h, --help show this help message and exit
Required arguments:
-n SAMPLENAME, --samplename SAMPLENAME
the name of the set of data
-p {boxplot,violinplot}, --plot {boxplot,violinplot}
create boxplot or violinplot, default is boxplot
-c {CG,CHG,CHH,all}, --context {CG,CHG,CHH,all}
choose the context of methylation, default 'all' is to choose them all
-t {Gene_Body,Promoter,Exon,Intron,all}, --target {Gene_Body,Promoter,Exon,Intron,all}
choose the genomic location of methylation, default 'all' is to choose them all
-nb NUMBEROFGROUP, --numberofgroup NUMBEROFGROUP
define how many group to seperate gene expression, default is 5
Important general arguments:
-re0 {True,False}, --skip0 {True,False}
whether genes with 0 expression value would be included. Default 'False' is to include them
-cor {False,pearson,spearman}, --correlation {False,pearson,spearman}
select the type of correlation in the table, default is pearson
Chart visulaization arguments:
-mean {True,False}, --showmeans {True,False}
whether to show the position of mean in boxplot or violin plot, default 'True' is to show
-sf {True,False}, --showfliers {True,False}
whether to show outliers in boxplots, default 'True' is to show
-ylim YLIMIT, --ylimit YLIMIT
numeric value. zoom in the DNA methylation level to understand the methylation distribution
Graphing arguments:
--dotsize DOTSIZE dotsize, default is 20
--textsize TEXTSIZE textsize, default is 20
--ticksize TICKSIZE ticksize, default is 15
--labelsize LABELSIZE
labelsize, default is 20
--titlesize TITLESIZE
titlesize, default is 20
--legendsize LEGENDSIZE
legendsize, default is 20
Example for grouping statistics:
# boxplot
python grouping.py -n demo -p boxplot
Usage:
usage: metagene.py [-h] [-n SAMPLENAME] [-p {region,site}] [-ma {True,False}]
[-nb NUMBEROFGROUP] [-re0 {True,False}]
[-No_bins NUMBEROFBINS] [-psn {TSS,TES}] [-bp BASEPAIR]
[-yaxis {auto,set100}] [-xtick XTICKSIZE]
[-ytick YTICKSIZE] [-label LABELSIZE] [-title TITLESIZE]
[-legend LEGENDSIZE]
optional arguments:
-h, --help show this help message and exit
Required arguments:
-n SAMPLENAME, --samplename SAMPLENAME
the name of the set of data
-p {region,site}, --plot {region,site}
choose the format of the metaplot, default is region
-ma {True,False}, --metavaluefile {True,False}
put in the metaplot value file if you have created, default is False
-nb NUMBEROFGROUP, --numberofgroup NUMBEROFGROUP
define how many group to seperate gene expression, default is 5
-re0 {True,False}, --skip0 {True,False}
remove genes that their expression value is equal to 0, default is not removing them.
Important arguments:
-No_bins NUMBEROFBINS, --numberofbins NUMBEROFBINS
for 'region' or 'site' plot. 'region' define the total of windows from upstream
to downstream, suggest 30. 'site' is the windows of one side, suggest 10.
-psn {TSS,TES}, --posname {TSS,TES}
for 'site' plot. Choose the specific position of the metagene,
default is TSS (transcription start site)
-bp BASEPAIR, --basepair BASEPAIR
for 'site' plot. The basepairs flanking by the chosen
position, default is 2000
-yaxis {auto,set100}, --yaxissetting {auto,set100}
choose if the ticks of yaxis set on 100, default
'auto' will automatically adjusted.
Graphing arguments:
-xtick XTICKSIZE, --xticksize XTICKSIZE
the size of the xticks (upstream, gene, downstream),
default is 20
-ytick YTICKSIZE, --yticksize YTICKSIZE
the size of the yticks (methylation level), default is
-label LABELSIZE, --labelsize LABELSIZE
labelsize, default is 20
-title TITLESIZE, --titlesize TITLESIZE
titlesize, default is 20
-legend LEGENDSIZE, --legendsize LEGENDSIZE
legendsize, default is 15
Example for metagene:
# region plot
python metagene.py -n demo -p region -No_bins 30
# site plot
python metagene.py -n demo -p site -No_bins 10 -bp 2000
# if created metagene 'region' value
python metagene.py -n demo -p region -No_bins 30 -ma True
Usage:
usage: comparison.py [-h] [-s SAMPLELIST] [-c {CG,CHG,CHH}]
[-t {Promoter,Gene_Body,Exon,Intron,all}]
[-pro PROB_CUTOFF] [-p {scatter,kernel}]
[-cor {False,pearson,spearman}]
[--shownumber {False,True}] [--cutoff {False,True}]
[-mthr METHTHRESHOLD] [-ethr EXPTHRESHOLD]
[--methmin METHMIN] [--methmax METHMAX] [--expmin EXPMIN]
[--expmax EXPMAX] [--dotsize DOTSIZE]
[--textsize TEXTSIZE] [--ticksize TICKSIZE]
[--labelsize LABELSIZE] [--titlesize TITLESIZE]
[--fontsize FONTSIZE]
optional arguments:
-h, --help show this help message and exit
Required arguments:
-s SAMPLELIST, --samplelist SAMPLELIST
put in the sample description file
-c {CG,CHG,CHH}, --context {CG,CHG,CHH}
choose the context of methylation, default is CG
-t {Promoter,Gene_Body,Exon,Intron,all}, --target {Promoter,Gene_Body,Exon,Intron,all}
choose the genomic location of methylation, default is
Promoter
-pro PROB_CUTOFF, --prob_cutoff PROB_CUTOFF
define the differential genes by their probrability in
Gaussian mixture model, default is 0.000001
Important general arguments:
-p {scatter,kernel}, --plot {scatter,kernel}
create scatterplot or kernel density plot, default is
scatter
-cor {False,pearson,spearman}, --correlation {False,pearson,spearman}
select the type of correlation, default is pearson
--shownumber {False,True}
whether to show the number of significant genes,
default False is not show
Define anomaly genes with methylation changes and gene expression changes:
--cutoff {False,True}
whether to use methylation and gene expression cutoff
to define outliers, default is False
-mthr METHTHRESHOLD, --meththreshold METHTHRESHOLD
set cutoff of differential methylated genes. default
'auto' uses methylation changes, CG:10, CHG:1, CHH:1
-ethr EXPTHRESHOLD, --expthreshold EXPTHRESHOLD
set cutoff to identify genes that have expression
changes, default uses expression changes: log2FC is 1
--methmin METHMIN minimum methylation changes for x-axis
--methmax METHMAX maximum methylation changes for x-axis
--expmin EXPMIN minimum gene expression changes for y-axis
--expmax EXPMAX maximum gene expression changes for y-axis
Graphing arguments:
--dotsize DOTSIZE dotsize, default is 20
--textsize TEXTSIZE textsize, default is 25
--ticksize TICKSIZE ticksize, default is 15
--labelsize LABELSIZE
labelsize, default is 25
--titlesize TITLESIZE
titlesize, default is 25
--fontsize FONTSIZE fontsize, default is 1.2
Example for comparison:
# show the correlation on scatterplot
python comparison.py -s samplelist.txt -c CG -t Promoter -cor pearson
# show number of differential genes on scatterplot
python comparison.py -s samplelist.txt -c CG -t Promoter -cor False --shownumber True
Usage:
usage: heatmap.py [-h] [-s SAMPLELIST] [-c {CG,CHG,CHH}]
[-t {Promoter,Gene_Body,Exon,Intron}] [-pro PROB_CUTOFF]
[-mmax MMAX] [-emax EMAX] [--cutoff {False,True}]
[-mthr METHTHRESHOLD] [-ethr EXPTHRESHOLD]
[--fontsize FONTSIZE]
optional arguments:
-h, --help show this help message and exit
Required arguments:
-s SAMPLELIST, --samplelist SAMPLELIST
put in the sample description file
-c {CG,CHG,CHH}, --context {CG,CHG,CHH}
choose the context of methylation, default is CG
-t {Promoter,Gene_Body,Exon,Intron}, --target {Promoter,Gene_Body,Exon,Intron}
choose the genomic location of methylation, default is
Promoter
-pro PROB_CUTOFF, --prob_cutoff PROB_CUTOFF
define the differential genes by their probrability in
Gaussian mixture model, default is '0.000001'
Important general arguments:
-mmax MMAX, --mmax MMAX
set the max methylation value for heatmap, default is
100
-emax EMAX, --emax EMAX
set the max expression value for heatmap, default is
20
Define anomaly genes with methylation changes and gene expression changes:
--cutoff {False,True}
whether to use methylation and gene expression cutoff
to define outliers, default is False
-mthr METHTHRESHOLD, --meththreshold METHTHRESHOLD
set cutoff of differential methylated genes. default
'auto' uses changes of methylation, CG:10, CHG:1,
CHH:1
-ethr EXPTHRESHOLD, --expthreshold EXPTHRESHOLD
set cutoff to identify genes that have expression
changes, default uses expression changes: log2FC is 1
Graphing arguments:
--fontsize FONTSIZE fontsize, default is 1.2
Example for heatmap:
# heatmap
python heatmap.py -c CG -t Promoter