The above is a picture of the human genome. The red markers indicate the locations of some 31 SNPs that have been cited in GWAS and linkage studies to be associated with Breast Cancer. While painting a picture of chromosomes and plotting some points may seem easy on the surface, it sometimes takes a lot of work to transform a large genotype file along with some information on markers into a pretty picture.

Ideograms are important. Genotype data is boring. Just take a look at some of my genotype data (from 23andMe) on the right. Despite the incredible amount of information that it might carry, most people (scientists included) will be able to better appreciate a description of a genome that is visual. While ideograms may seem trivial, they can even make data analysis easier.

Take for example, an analysis of meitic recombinations that occurred in producing a child’s chromosomes. A list of rsids and positions where recombinations have occurred will likely be the result of any bioinformatic analysis of the child’s DNA. However, these numbers (especially in chromosomes that are multi-multi millions of bases long) fail to paint a great picture of where recombinations occurred. However, a well-made ideogram can easily convey the data to me. For example, look at the recombination map below.

So what is the best method for creating ideograms? One solution is the online tool, Ideographica. Ideographica lets you create on-demand ideograms with your own annotation in a number of different colors, formats and styles. This is a great tool for adding more emphasis to any genome-wide study.

What about people who have more specific needs? The truth is that if you are looking to create a very custom ideogram, then you will likely have to make it yourself (for now). The good news is that creating virtual chromosomes only takes some experience programming and a few key pieces of information for the chromosome template.

Personally, I program in Java, and have used a standard drawing library to draw simple shapes (lines, circules, triangles and other polygons) and colors on a coordinate plane. Along with the Java capabilities, I have used data from the Human Genome Refernce Sequence to draw the actual chromosomes.

Chromosome data can be obtained from the UCSC Genome Browser. Along with the length of each chromosome (in base pairs), I also used the positions for the start, middle, and endpoints of each centromere to create the nice (and accurate) distinctions between the short arm and the long arm.

Using the information from the RefSeq to draw the chromosomes is a great idea since any locus that has a RefSeq position (SNPs for instance) can be accurately plotted on your ideogram. As a benefit, visualizing single loci is helpful when thinking about testing for linkage. For anyone who is interested in drawing their own genome ideograms, I’ve provided a file containing the length of each chromosome along with the positions of the start, middle and ends of each centromere in the Resources section of this site. That’s all I have to say about ideograms. I would like to hear from others who have developed their own methods of visualizing genotype data.

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