Question: Hello, I'm trying to identify the image used to make this image, I cannot find out, and the author of the paper Figure 2A in DOI:10.1371/journal.pone.0120388 isn't answering his email, does anyone recognize what software was used to make this image?

an edit to BioStars: none of these free image hosting is working

[url=https://postimg.cc/jwzWvXhc][img]https://i.postimg.cc/jwzWvXhc/Screenshot-from-2019-02-15-20-41-50.png[/img][/url]

[IMG]http://i65.tinypic.com/fjkf2b.png[/IMG]![enter image description here][1]

https://ibb.co/ZSftjb8![enter image description here][2]

[1]: http://[IMG]http://i65.tinypic.com/fjkf2b.png[/IMG]
[2]: https://ibb.co/ZSftjb8

Answer: I don't know the exact software used to create that image but you can reproduce it using [karyoploteR](	http://bioconductor.org/packages/karyoploteR/).

![reproduction of the DMR plot using karyoploteR][1]

Here's the code. We start creating random regions on the genome with [regioneR](http://bioconductor.org/packages/regioneR/)'s `createRandomRegions`function. We the create the plot, using [plot.type=6](https://bernatgel.github.io/karyoploter_tutorial//Tutorial/PlotTypes/PlotTypes.html) with no ideograms. After that we use `kpAbline` to plot the horizontal black lines and [kpPlotRegions](https://bernatgel.github.io/karyoploter_tutorial//Tutorial/PlotRegions/PlotRegions.html) to plot the DMR regions in two colors. In total, 6 lines of code + 1 for the legend.

In the [karyoploteR tutorial page](https://bernatgel.github.io/karyoploter_tutorial/) you can find much more information, including how to add many other data types and how to customize the plot style.

library(karyoploteR)

#Create random regione with regioneR's createRandomRegions
up.dmr <- createRandomRegions(nregions = 40, length.mean = 2e6)
down.dmr <- createRandomRegions(nregions = 40, length.mean = 2e6)

#Initialize the plot
kp <- plotKaryotype(plot.type = 6, ideogram.plotter = NULL)

#Create the black lines
kpAbline(kp, h=0.5)

#And plot the up and down DMR regions
kpPlotRegions(kp, data=up.dmr, col = "red")
kpPlotRegions(kp, data=down.dmr, col="green")

#Add a legend
legend(x="bottomright", legend = c("Hypermethylated", "Hipomethylated"), fill=c("red", "green"))

As an option, if you want to have a clearer idea of the position of the regions, you can leave the cytoband plotting active removing `ideogram.plotter=NULL` and add a white semi-transparent rectangle covering the cytobands to dim them a bit so our data pops. In addition, setting [`r0`and `r1`](https://bernatgel.github.io/karyoploter_tutorial//Tutorial/DataPositioning/DataPositioning.html) so to 0.2 and 0.8, so the data only spans 60% of the vertical space makes the plot a bit more appealing.

#Initialize the plot
kp <- plotKaryotype(plot.type = 6, cex=2)
kpDataBackground(kp, col="#FFFFFFAA")

#And plot the up and down DMR regions
kpPlotRegions(kp, data=up.dmr, col = "red", r0=0.2, r1=0.8)
kpPlotRegions(kp, data=down.dmr, col="green", r0=0.2, r1=0.8)

#Add a legend
legend(x="bottomright", legend = c("Hypermethylated", "Hypomethylated"), fill=c("red", "green"), cex=2)

And this is the result

![enter image description here][2]

[1]: https://i.ibb.co/Qj500KT/DMR.png
[2]: https://i.ibb.co/TwYZPq1/DMR2.png