Circos Collinearity Chord Diagram
Use Circos Collinearity Chord Diagram
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Upload an interval-mapping CSV to generate a Circos collinearity chord diagram: ring segments are continuous one-dimensional tracks with coordinate ticks, and thin curves encode mapping / synteny from one interval to another. Built for genome collinearity, block migration, address-range mapping and batch reassignment. Dark mode and PNG / SVG export, all processed locally.
About this tool
This chart is not about “which node connects more strongly”, but about “which interval maps to which interval”. Every segment on the ring is a continuous track with its own 0-based coordinate scale, and thin center curves connect one source interval to one target interval. The more crossings you see, the more reordering, rearrangement or fragmentation has happened. That makes it a natural fit for genome synteny, block migration and address-range remapping.
- Export crisp PNG / SVG you can keep editing
- Multiple built-in themes, one-click switch
- Light and dark canvas support
- Free, no watermark, no limits
Compared with general charting workflows
| Aspect | This tool | Typical alternative |
|---|---|---|
| Visual direction | Optimized for a specific chart grammar | More general, but more setup |
| Export | Fast PNG/SVG export first | More formats, but heavier workflow |
| Best fit | Slides, landing pages, and social visuals | Building bespoke analytical charts from scratch |
Docs
Free online Circos collinearity chord diagram generator. Upload a seven-column CSV in the form source_group,source_start,source_end,target_group,target_start,target_end,color and the tool turns ring segments into continuous one-dimensional tracks with coordinate ticks. Thin curves in the middle encode interval-to-interval mapping or synteny correspondence. A default sample is included and you can export PNG / SVG immediately.
What this chart is
An ordinary chord diagram treats each node as a discrete category and each connection as weighted flow. A Circos collinearity plot is different: every ring segment is a continuous track with its own position scale, so the picture is not “node A to node B”, but “interval on track A maps to interval on track B”. That is why this layout is useful for genome synteny, storage block migration, address-range remapping, order-batch reassignment, stratigraphic alignment and similar problems. Crossings are not noise here — they are the visual expression of permutation, rearrangement, remap or displacement.
What you can do
- Upload interval-mapping CSV rows.
- Automatically render continuous tracks, sparse coordinate ticks, thin mapping curves and right-side leader labels for dense short targets.
- Download a template that matches the default sample exactly.
- Switch light / dark mode without changing the layout logic.
- Export clean PNG / SVG.
- Keep all processing local in the browser.
How to use it
- Open the page and inspect the built-in sample, which mimics upper
AtChr.*tracks mapped to right-lowerSca.* / ApChr.*tracks. - Upload your own CSV. Each row describes one interval mapping, not a simple discrete
source,target,valuerelationship. - Hover curves or tracks to inspect track names and interval ranges.
- Export PNG or SVG when the layout looks right.
Example:
source_group,source_start,source_end,target_group,target_start,target_end,color
AtChr.1,2,9,Sca.186,1,4,purple
AtChr.2,9,16,ApChr.12,4,10,red
AtChr.4,11,18,ApChr.1,4,13,purple
Where it fits
- Genome collinearity and synteny
- Storage block migration or rebalance
- NAT / route remapping by address ranges
- Static-to-runtime memory relocation
- Batch reassignment in supply chains
- Depth, time or energy interval correspondence in science
Notes
This tool is about interval mapping on continuous coordinates, not category-to-category traffic totals. If your question is simply “how much flows from A to B”, a regular chord diagram is usually the better fit. The current template keeps source tracks on the upper arc and target tracks on the right-lower arc, with Sca.*-style short targets using leader labels to stay readable and to match the uploaded reference image closely.
Last verified: 2026-09-11
How to read it
Start with the outer ring: each segment is a continuous track, and the 0 / 10 / 20 / 30 labels are position ticks, not category IDs. Then read the center curves: every curve means “this interval on the source track corresponds to that interval on the target track”. Heavy crossing suggests permutation, rearrangement or remap; mostly parallel curves suggest better order preservation. The right-side Sca.* labels use leader lines because those tracks are short and dense.
FAQ
What do the seven CSV columns mean?
They are: source track name, source interval start, source interval end, target track name, target interval start, target interval end, and link color class. In other words, each row is a full interval mapping, not a simple weighted node relation.
When should I use this instead of a normal chord chart?
Use it when position itself matters — chromosome coordinates, disk block addresses, IP ranges, batch IDs or depth intervals. If you only care about the total from A to B and not which interval maps to which, a normal chord diagram is usually cleaner.
Is this chart tool better for slides, reports, or social visuals?
All three can work, but each needs a different emphasis. Slides care about legibility, reports care about structure and annotation, and social visuals care about density and style. Tune colors, labels, and export size for the destination.
When should I export PNG instead of SVG?
PNG is usually better for slides, chats, or social sharing, while SVG is better if you need to keep editing, scale for print, or embed the graphic in a design system. If the target platform does not support SVG, export a higher-resolution PNG.
How is this different from a general charting tool?
These tools are optimized for a specific visual grammar, so you can get to a strong result faster. General charting tools are more flexible across many chart families, but usually take more setup and styling work.
Can I use this tool on mobile phones or tablets?
Most tools work in modern mobile browsers, but complex charts, larger files, and precision drag-and-drop tasks usually feel better on desktop. If speed and control matter, desktop is the safer choice.
Is this tool better for one-off tasks or recurring workflows?
It is strongest for fast one-off tasks, quick verification, and temporary exports. If you need long-term automation, team collaboration, or deeper integration, you will usually pair it with desktop software, scripts, or a larger system.
How can I get a better result on the first try?
Start with the built-in example or with the smallest realistic sample you have. Once the format, layout, and export look right, switch to the full dataset or final asset. This is usually the fastest way to catch formatting issues early.
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