 The cancer cell DNA can tell us a lot about what is driving the tumor and what types of therapies might be successful to fight it. With the new advances in DNA sequencing, we now have a vast amount of information about the DNA sequence of tumor cells. This can be powerful to find changes in tumor cells that are absent in normal cells, including copy number changes, mutations, changes in gene expression, and methylation. This information can then be used to identify cancer driver mutations, discover new molecular targets for therapy, and identify molecular profiles that are associated with a particular clinical outcome. All of this is very powerful. However, this information can sometimes be buried in all of the sequencing results and very hard to visualize. To address this important problem, a group of scientists led by Marco Amara of the Michael Smith Genome Sciences Center has developed a tool called Circos for comparative genomics to compare multiple sets of sequencing data. Before this new tool, genomics data was visualized linearly, with each chromosome represented individually. If we are trying to visualize complex genomic changes like translocations, the genomic map quickly becomes confusing and not usable, and patterns become difficult to see. Instead, Circos visualizes genomics data in a circle, which scientists have done for decades to visualize bacterial DNA, which is naturally circular. This new method of visualizing our genome works like this. Each chromosome is positioned end-to-end in a large circle. In this circle, we can now visualize a few things. Copy number changes of multiple tumor samples inside the circle. Here, each line represents a different sample with the average probe values, which represent the copy number. If a gene has duplicated, the line for that sample will move outward. If a copy of the gene was lost, it will move inward. From here, it's also easy to zoom in and look at a particular region in more detail. As you can see, it's easy to compare copy number changes between tumor samples. We can also build upon this to include other types of genomic changes, like translocations across the entire genome. To look at large-scale rearrangements using Circos, lines going from one end of the rearrangement to another are drawn like this across the circle. If all of the tumors have this particular rearrangements, then dots will be drawn in each line representing each sample for this particular rearrangement. Using Circos, we can also study similarities between different genomes. For instance, if we were comparing the dog genome to the human genome, we would plot the entire human DNA sequence on the top half of the circle and one of the dog chromosomes on the bottom half. If there are any similarities between a particular sequence of the dog chromosome 31 with the human sequence, we would represent this by drawing a thick line spanning the dog DNA sequence with the matching sequence in the human genome. This way of visualizing data may bring up interesting relationships. For example, we may see that the dog chromosome 31 is actually very similar to human chromosomes 3 and 21. So using Circos, you can see how easy it is to compare different tumor samples with regards to copy number changes, but also large-scale genomic changes across the entire genome, looking for similarities and differences between samples. We can also illustrate relationships between genomic positions. This method of visualizing genomics data is an appealing answer to the overarching challenge of illustrating extremely large and complex genomic datasets using small-sized features to find interesting relationships that may lead to new therapies.