Topological Sequence Analysis of Genomes: Delta Complex approaches

TL;DR

This paper introduces topological sequence analysis (TSA) methods using algebraic topology techniques like $\\Delta$-complexes and persistent homology to analyze genome sequences, demonstrating applications in phylogenetics and potential for other sequential data.

Contribution

It presents novel TSA techniques based on $\\Delta$-complexes and persistent Laplacians for genome analysis, improving efficiency over previous models.

Findings

Effective in phylogenetic analysis of Ebola virus and bacterial genomes

More efficient than earlier TSA models and k-mer topology

Potential applications in linguistics, music, and social data analysis

Abstract

Algebraic topology has been widely applied to point cloud data to capture geometric shapes and topological structures. However, its application to genome sequence analysis remains rare. In this work, we propose topological sequence analysis (TSA) techniques by constructing $\Delta$-complexes and classifying spaces, leading to persistent homology, and persistent path homology on genome sequences. We also develop $\Delta$-complex-based persistent Laplacians to facilitate the topological spectral analysis of genome sequences. Finally, we demonstrate the utility of the proposed TSA approaches in phylogenetic analysis using Ebola virus sequences and whole bacterial genomes. The present TSA methods are more efficient than earlier TSA model, k-mer topology, and thus have a potential to be applied to other time-consuming sequential data analyses, such as those in linguistics, literature, music, media, and social contexts.