Setup and Input

The setup screen has two entry points:

Entry point	Use when	Backend required
Example Library	You want to open bundled precomputed datasets.	No for generated browser examples; yes for backend-driven processing.
New Project	You want to upload local trees and/or an MSA.	Yes for uploads, interpolation, and tree inference.

The backend status badge reports whether upload processing and MSA inference are available. In GitHub Pages demo mode, use generated examples unless you are running the local backend, Docker workflow, or desktop app.

Input Files

Input	Accepted role	Notes
Tree file	Ordered tree series for interpolation.	Use Newick-style tree files such as .nwk, .newick, or .tree.
MSA file	Alignment context or sliding-window tree inference.	Backend-supported parsers include FASTA, CLUSTAL, PHYLIP, Nexus, and MSF-style alignments.
CSV taxa metadata	Taxa coloring groups.	Loaded from the Taxa Coloring window, not from the setup screen.

Workflow Modes

Files provided	Processing mode
Tree file only	Normalize the ordered tree series and build transition frames.
Tree file plus MSA	Build tree transitions and map alignment columns to the tree sequence.
MSA only	Split the alignment into overlapping windows, infer one tree per window, then build transition frames.

Sliding Window Settings

These settings apply when an MSA is uploaded.

Setting	Meaning
Window Size (sites)	Number of alignment columns included in each window.
Step Size (sites)	Distance between consecutive window starts. Smaller steps create more overlapping windows and more input trees.

When trees and an MSA are uploaded together, the same window and step values map alignment coordinates onto the uploaded tree sequence. When only an MSA is uploaded, they control the slices used for inference.

Tree Adjustments

Setting	Meaning
Midpoint rooting	Roots inferred or uploaded trees at the midpoint before transition construction.

Use midpoint rooting when the input series is unrooted or inconsistent and you want a stable visual reference. Preserve input rooting when the biological interpretation depends on a known root.

Tree Inference Engine

Tree inference settings apply only to MSA-only workflows.

Engine	Use when
IQ-TREE	You want model-based inference with optional branch support annotations.
FastTree	You want faster approximate inference for responsive exploratory runs.

IQ-TREE Settings

Setting	Meaning
IQ-TREE Fast Search	Sends IQ-TREE fast-search options for more responsive runs. This is disabled when UFBoot is selected.
Substitution Model: JC/GTR	JC assumes equal rates and frequencies. GTR estimates rates and base frequencies.
Gamma Rate Heterogeneity	Adds site-rate variation to the selected model.
Support Mode	Selects no support run, UFBoot, SH-aLRT, or SH-aLRT plus UFBoot.
UFBoot replicates	Number of ultrafast bootstrap replicates when UFBoot is enabled.
SH-aLRT replicates	Number of SH-aLRT replicates when SH-aLRT is enabled.
Bootstrap NNI	Sends IQ-TREE -bnni for bootstrap support refinement.

Branch support annotations can later be selected in the workspace under Style -> Geometry & Labels -> Branch Annotation.

FastTree Settings

Setting	Meaning
Pseudocounts	Sends FastTree -pseudo.
Skip ML Optimization	Sends FastTree -noml.

FastTree settings trade detail for speed. They are most useful for exploratory datasets where rapid feedback is more important than a final inference run.