Phylo2Vec: a vector representation for binary trees
This provides a unified encoding for phylogenetic trees, improving computational efficiency for biologists and researchers in evolutionary biology, though it is incremental as it builds on existing tree representation methods.
The authors tackled the problem of computationally expensive inference of binary phylogenetic trees by introducing Phylo2Vec, a vector representation that enables fast tree sampling, compressed representation, quick verification, and systematic traversal of tree space, demonstrating its efficiency in maximum likelihood inference on real-world datasets.
Binary phylogenetic trees inferred from biological data are central to understanding the shared history among evolutionary units. However, inferring the placement of latent nodes in a tree is computationally expensive. State-of-the-art methods rely on carefully designed heuristics for tree search, using different data structures for easy manipulation (e.g., classes in object-oriented programming languages) and readable representation of trees (e.g., Newick-format strings). Here, we present Phylo2Vec, a parsimonious encoding for phylogenetic trees that serves as a unified approach for both manipulating and representing phylogenetic trees. Phylo2Vec maps any binary tree with $n$ leaves to a unique integer vector of length $n-1$. The advantages of Phylo2Vec are fourfold: i) fast tree sampling, (ii) compressed tree representation compared to a Newick string, iii) quick and unambiguous verification if two binary trees are identical topologically, and iv) systematic ability to traverse tree space in very large or small jumps. As a proof of concept, we use Phylo2Vec for maximum likelihood inference on five real-world datasets and show that a simple hill-climbing-based optimisation scheme can efficiently traverse the vastness of tree space from a random to an optimal tree.