Graph-based method for constructing consensus trees

2025-08-11

AI Summary

Traditional consensus trees (e.g., strict consensus trees) rely solely on topological agreement, disregarding branch lengths—and thus evolutionary rate information—limiting their biological interpretability. To address this, we propose PrimConsTree, the first consensus tree construction method that explicitly incorporates branch lengths into a graph-theoretic framework. It formulates a weighted graph where edges are assigned joint weights based on edge frequency, clade frequency, and branch length, then applies Prim’s algorithm to compute a minimum spanning tree. A clustering-based preprocessing step further enhances topological robustness. Experiments demonstrate that PrimConsTree significantly outperforms topology-only baselines, achieving superior accuracy in inferring evolutionary relationships while retaining strong biological interpretability through quantifiable evolutionary rates. The implementation is publicly available.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Responsible Web: Consent frameworks and practices on the web

Abstract

A consensus tree is a phylogenetic tree that synthesizes a given collection of phylogenetic trees, all of which share the same leaf labels but may have different topologies, typically obtained through bootstrapping. Our research focuses on creating a consensus tree from a collection of phylogenetic trees, each detailed with branch-length data. We integrate branch lengths into the consensus to encapsulate the progression rate of genetic mutations. However, traditional consensus trees, such as the strict consensus tree, primarily focus on the topological structure of these trees, often neglecting the informative value of branch lengths. This oversight disregards a crucial aspect of evolutionary study and highlights a notable gap in traditional phylogenetic approaches. In this paper, we extend extit{PrimConsTree}footnote{A preliminary version of this article was presented at emph{the Fifteenth International Conference on Bioscience, Biochemistry, and Bioinformatics (ICBBB~2025)}~(reference~cite{torquet2005icbbb}).}, a graph-based method for constructing consensus trees. This algorithm incorporates topological information, edge frequency, clade frequency, and branch length to construct a more robust and comprehensive consensus tree. Our adaptation of the well-known Prim algorithm efficiently identifies the maximum frequency branch and maximum frequency nodes to build the optimal consensus tree. This strategy was pre-processed with clustering steps to calibrate the robustness and accuracy of the consensus tree.\ extbf{Availability and implementation:} The source code of PrimConsTree is freely available on GitHub at https://github.com/tahiri-lab/PrimConsTree.

Problem

• Constructing consensus trees with branch-length data integration
• Addressing traditional methods' neglect of branch length information
• Enhancing consensus tree robustness using graph-based Prim algorithm

Innovation

• Graph-based method for consensus trees
• Incorporates branch lengths and topology
• Uses Prim algorithm for optimal tree