19 Stars
Updated Last
12 Months Ago
Started In
March 2016


DOI Latest Release License

test-lts test-stable test-nightly codecov.io


The objective of PhyloTrees.jl is to provide fast and simple tools for working with rooted phylogenetic trees in Julia.


The current release can be installed from the Julia REPL with:

pkg> add PhyloTrees

The development version (master branch) can be installed with:

pkg> add PhyloTrees#master


There are several ways to add nodes and branches to our Tree, see below for examples

> # Initialize the tree
> exampletree = Tree()

Phylogenetic tree with 0 nodes and 0 branches

> # Add a node to the tree
> addnode!(exampletree)

Phylogenetic tree with 1 nodes and 0 branches

Branches have Float64 lengths

> # Add a node, connect it to node 1 with a branch 5.0 units in length
> branch!(exampletree, 1, 5.0)

Phylogenetic tree with 2 nodes and 1 branches

> # Add 2 nodes
> addnodes!(exampletree, 2)

Phylogenetic tree with 4 nodes and 1 branches

> # Add a branch from node 2 to node 3 10.0 units in length
> addbranch!(exampletree, 2, 3, 10.0)

Phylogenetic tree with 4 nodes and 2 branches

We can quickly look at the nodes present in our Tree:

> collect(exampletree.nodes)

[unattached node]
[branch 1]-->[internal node]-->[branch 2]
[branch 2]-->[leaf node]
[root node]-->[branch 1]

Other capabilities

Distance between nodes can be calculated using the distance function. A node visit ordering for postorder traversal of a tree can be found with postorder.

A plot recipe is provided for Trees. The following Tree has been generated and plotted using code in READMETREE.jl.

Tree Plot

There are many other functions available that are helpful when dealing with trees including: changesource!, changetarget!, indegree, outdegree, isroot, isleaf, isinternal, findroots, findleaves, findinternal, findnonroots, findnonleaves, findexternal, areconnected, nodepath, branchpath, parentnode, childnodes, descendantnodes, descendantcount, leafnodes, leafcount, ancestorcount, ancestornodes, and nodetype. These work nicely with Julia's elegant function vectorization. An example of this in action can be seen in the in our plot recipe code.

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