Circos Plot
Chart overview
A Circos plot arranges chromosomes or genomic sequences in a circle (the ideogram) and draws links between genomic regions as ribbons across the interior, making it possible to display chromosomal rearrangements, translocations, copy number variations, syntenic regions between species, and long-range chromatin interactions in Hi-C data in a single compact figure.
Key points
- Outer concentric tracks around the ideogram can display data such as gene density, GC content, SNP density, or ChIP-seq signal as histograms, heat maps, or scatter tracks.
- The circular layout naturally removes the linearity bias of linear genome plots and places all chromosomes at equal visual prominence.
- Circos plots are used in cancer genomics to illustrate the structural variant landscape of tumor genomes, in comparative genomics to display synteny between species, and in 3D genomics to visualize TAD boundaries and chromatin interaction frequency.
Example Visualization
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Generate publication-ready circos plots with AI in seconds. No coding required – just describe your data and let AI do the work.
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"Create a Circos plot from my structural variant data showing chromosomal translocations between human chromosomes 1-22 plus X and Y. Draw each chromosome as an arc of the outer ideogram sized by chromosomal length. Draw ribbon links for each translocation colored by the source chromosome. Add an outer track showing gene density as a histogram. Use a dark background with white chromosome labels. Format for a Cancer Cell figure at 300 DPI."
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AI Generation
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Python Code Example
Console Output
Figure saved: plotivy-circos-plot.png
Common Use Cases
- 1Cancer genomics: displaying the structural variant and chromosomal rearrangement landscape of tumor genomes
- 2Comparative genomics: showing syntenic regions and chromosomal inversions between two species
- 3Hi-C data visualization: displaying inter-chromosomal chromatin interaction frequencies
- 4Microbial genomics: visualizing plasmid gene transfer events and mobile genetic elements across genomes
Pro Tips
Scale chromosome arc lengths proportionally to actual chromosome size for accurate representation
Limit ribbons to the most biologically significant events - too many links create an uninterpretable tangle
Use distinct colors per source chromosome so ribbon origin is identifiable without labels
Add outer data tracks sparingly - one or two tracks keep the plot readable; more creates visual overload
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.