baltic3

baltic(Backronymed Adaptable Lightweight Tree Import Code) is a phylogenetic tree visualization library. Originally developed by Dr. Gytis Dudas, a postdoc at Trevor Bedford's lab, it wraps matplotlib with its own proprietary tree object. The original Python2 version is available over at the Bedford Lab. That readme's worth a read to explain how the class structures are set up.

• baltic isn't as good as Biopython at tree manipulation...
• But it's far superior at visualization for presentation and publication-standard figures.
• baltic trades off ease-of-use with flexibility. I'd call it difficult to learn, but easy to master.

The modules are:

• baltic3 - contains the tree, node and leaf class definitions, and class methods.
• baltic3_utils - function library that contains public methods.
• experimental - function library that contains new methods which are still under development.

Installation

There's no installation per se; git clone or download this repo to a known location (say, your desktop), and, in Jupyter, point your PYTHONPATH to it. That is, in Jupyter:

import sys
sys.path.append("path/to/baltic3")

# import the modules of this package
# Only 2 modules
import baltic3 as bt
import baltic3_utils as btu

Tutorials

baltic is meant to be executed in Jupyter, since the final output is probably a pdf of your final image. There's a series of example notebooks in the /tutorials directory of this repo. In recommended order:

• Tutorial 1 - Required input format for baltic.
• Tutorial 2 - About the baltic tree object.
• Gallery - Gallery of examples, though so far there's only 1 example in there.
• (upcoming) - Drawing simple shapes with matplotlib
• (upcoming) - matplotlib artist objects: Drawing many, many simple shapes very quickly.

The rest of the examples are more complex examples of different visualisations.

Is this easy to learn?

(This is the most frequently asked question I get)

Short answer: no.

Long answer: It's easy if you know how to do it, which is a perfectly accurate but perfectly useless answer. If you're new to Python, this is not a good starting point.

Demo: A/H3N2 Influenza virus in Australia (with Treetime)

ML skyline plot generated from treetime.

• Jupyter notebook here
• Note the slight uptick in diversity at the end (~2017) of the dataset. Given that this is a log-scale, this represents quite a large jump in diversity (10X increase!).
• Is "skyline diversity" a meaningful measure? I'm not sure, because, personally, I don't find a single scalar measure of diversity that informative. I'd be more interested in where the diversity is occurring (i.e. which nucleotide sites are particularly mutable), and subsequent structure-function changes at the 3D structure level that could result from changes in mutation. Given that this is the HA sequence, some immunology contextual information (where antibodies tend to target) would be useful as well.

Dev Notes

The very long to-do list here.