This is the first prototype of a long-running idea of mine (and others) as a way of displaying big trees where our typical approach is either too slow and/or we run out of pixels. This sketch was from many years ago:

This prototype implements streams by allowing branch labels to cut the tree into monophylies / paraphylies and visualising each as streamgraphs. While the motivation was for this to display huge trees which Auspice can't currently display, it works and is useful (and fun) for smaller trees as well. Long-term I think this would be a good starting view into very large or diverse datasets.

How to use

nextstrain.org review app

Any dataset with a branch label (apart from amino acid) can have streamtrees. The dropdown in the sidebar can change the label used to partition the tree, and there's a toggle to go between streamtrees and normal tree view. My general view is that the appropriate partitioning of sample-sets will be best done in Augur, either algorithmically or manually. (There's also scope for dynamic partitioning in Auspice via genotype or color-by, but that's not implemented here.)

Suggested testing datasets

• EBOV
• Influenza
• Large 23k-tip ncov dataset. In particular scrubbing time / filtering is much much faster here than when viewing the normal tree.

Known caveats / bugs

• The tree must be temporal. You can change between temporal & divergence metrics, so long as temporal information exists.
• Only rectangular trees.
• Streams with only internal nodes cause a crash.
• Performance hasn't been optimised at all. Specifically, toggling between streamtrees & normal trees is very slow.
• Nested streams which aren't very ladder-like don't zoom well most of the time, and changing back to the normal view often gets the viewport completely wrong. This is fixable, but requires a rethinking & rewrite of how I calculate the vertical position of nodes.
• Only categorical color-bys work. Genotype colorings don't work.
• The vertical space within a single stream is consistent (i.e. half height = half the samples in that window) but it's not consistent between streams. The window size is also not consistent between streams.
• The curves drawn around streams are an off-the-shelf curve generator and aren't quite right.
• Dashed lines are no good.

Future directions

• Release the current prototype after feedback, UI improvements & bug-fixes.
• Auto-toggling between streamtrees & normal trees. Currently you have to manually do this and it's very slow, but the idea is that as you zoom the streams are replaced by either more fine-grained streams or the normal tree view.
• Allow JSONs to encode streams in a more compressed format (to allow massive trees) and then the switching of streams to normal trees involves a fetch of the associated dataset-JSON for that particular stream.
• Dynamic partitioning which isn't simple cuts in the tree. E.g. see certain mutations as individual streams with the lines between the streams representing the flows between states. (Maybe.)
• On a recent call we discussed collapsing identical sequences (tips) in order to better view large trees. One option here is making the tip size scale with the number of samples represented by the tip (expressing multiple colours / sampling dates is harder). Another option is to use streams (but be aware that n streams will always be a lot slower than n tips).

Screenshots