v1.3.5

COMPETITIVE LEARNING · PHYLOGENETICS

KomPhy

Explore phylogenetic relationships in aligned DNA sequences.

Read the thesis ↗
Generate a synthetic test with a known tree

Create a new tree and evolve aligned DNA along it under JC69. These are generated tests, separate from the four original checkout examples, whose true trees are unconfirmed.

Up to 1,000,000 bases. The same settings and seed reproduce the same browser test. Scale multiplies branch lengths in expected substitutions per site; larger values allow more divergence and saturation. Browser seeds do not reproduce the separate DendroPy research-study datasets.

Your alignment

Paste aligned sequences or open a FASTA or sequential PHYLIP file. Use A, C, G and T; every sequence must have the same length.

Original checkout examples — reference trees unavailable. To test accuracy, use “Generate a synthetic test with a known tree” above.

2–256 taxa · up to 10,000 sites · 1,000,000 bases total · 2 MiB upload

Learning controls

Set conscience to 0 to disable its bias. A convergence threshold of 0 uses every epoch; otherwise a partition stops when RMS falls below the threshold and both groups are populated.

Exact plane views require Euclidean distance, updates after each epoch, and conscience 0. Other settings retain selectable splits with an explanation.

No known reference tree: accuracy cannot be measured.

Each analysis has a 120-second time limit. Results remain available in this browser for 24 hours. Download them to keep a copy.

Inferred topology

Load an alignment to begin.

Your tree and convergence history will appear here.

Using and interpreting KomPhy

KomPhy recursively separates sequences using two learned prototypes. Start with an example, adjust the controls, then select Build tree. You can cancel a running analysis and download completed trees as Newick, SVG or a JSON record containing settings, warnings and convergence history.

Nearest selection follows the thesis diagram. Farther selection with Euclidean distance and updates after each epoch follows the archived source. The archived Jukes–Cantor function instead selects nearest; farther with Jukes–Cantor is an experimental combination. These choices can produce very different trees: nearest selection can leave similar sequences unresolved, while farther selection can alternate prototype roles between epochs. Changing the distance sign alone has not been shown to improve accuracy.

Conscience is disabled by default, as in the thesis experiments. All taxa are retained. Groups that training cannot separate remain unresolved and are reported. Continuous Jukes–Cantor saturation is also reported. The original experiment datasets have not been recovered; the two interpretations are being assessed on new synthetic data.