Tool:chromoMap- R package for Interactive visualization and Annotation of chromosomes or regions of any living organism.
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2.0 years ago

chromoMap is an R package that can be used to create interactive visualizations and annotation of entire chromosomes or chromosome regions of any living organism. It takes BED like files to specify the coordinates of the chromosomes and the annotations separately.chromosomes are rendered as a continuous composition of loci where you can hover on a locus to see detailed annotation for the genomic range of that locus. It is available on CRAN. It provides many features like:

  • visualize different sets of chromosomes(polyploidy) on the same plot that can be used for cross-species or multi-sample comparisons.
  • associate numeric data and create chromosome heatmaps or visualize group annotations
  • add hyperlinks to your favorite database and many more like add labelings, change colors and embellish your plots.

------- chromoMap 2021 UPDATE -----------

Check out version 3:

chromoMap v3

R genome annotation visualization Tool • 2.7k views
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I prepared a chromosome file (c_plot) as

NC_037545.1     1       202105980
NC_037546.1     1       188952477
NC_037547.1     1       175630833

Annotation file (de_lncrna.annotation) as:

LOC112581744    NC_037545.1     6566415 6567888
LOC112582356    NC_037545.1     8234378 8238149
LOC112582960    NC_037545.1     12240957        12245030

I did

annot_dat <- read.table(file = "~/lncrna/chromomap/de_lncrna.annotation", sep="\t", quote="", stringsAsFactors=FALSE)
chrom_dat <- read.table(file = "~/lncrna/chromomap/c_plot", sep="\t", quote="", stringsAsFactors=FALSE)

> chromoMap(chrom_dat, annot_dat)
**Error**: The number of data files(s) must same as the number of chromsome file(s)
**Error** in chromoMap(chrom_dat, annot_dat) :

I have one chromosome and one annotation file only, I specified it as per manual, but I am getting this error.

Kindly help!

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Hello,

chromoMap() takes the file-names directly as input. Try passing the filenames directly as:

chromoMap( "~/lncrna/chromomap/c_plot", "~/lncrna/chromomap/de_lncrna.annotation")

or you could save the names in character variable: annot_dat <- "~/lncrna/chromomap/de_lncrna.annotation" chrom_dat <- "~/lncrna/chromomap/c_plot"

and then do, chromoMap(chrom_dat, annot_dat)

I hope that is helpful.

Thank you for trying chromoMap. Cheers, Lakshay

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Hello, I used your package for making chromosome heatmaps (very pretty plots!) Just one thing: the automated color scale is going from red to blue, meaning positive values are blue. I would like to reverse the color scale but didn't find out how. I understood that with data_colors the annotation colors can be changed, but only for categorial variables and not for a continous scale. Am I missing something? thank you for the nice package!

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Hello, thank you for your praise, I appreciate that! you can use data_colors to change the heatmap colors also.

I have rolled the new upgrade to the package, version 3, with some new major features, please try it out to see if you can make the plot you are desire to make. please check it out! chromoMap v3

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Hi,

I think this package is brilliant! Really nice way of visualizing data across the chromosome. I'm using your chromoMap package to visualize a set of genes and I just wondered if there was any way to adjust the range of the loci? I know it says in the tutorial that the chromosome is arranged into loci based upon the length of the chromosome but is this range customizable? I ask this because some of the genes I am looking at are close together and so appear as one feature on the chromoMap plot but I would like to see them separately.

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Hello, Thank you very much for the compliment, I really appreciate that. As for your query, you can try changing the range of entire chromosomes through the chromosome files that will automatically adjust the loci range. The chromosome files define the start and stop coordinates of each chromosome. the chr start value doesn't have to be 1, you can choose any value and in this way, you can visualize a genomic/chromosomal region of interest (rather than the entire chromosome). You can split a chromosome into sub-regions and visualize them all in a single chromoMap plot.

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