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Revealing the Shape of Genome Space via K-mer Topology
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Despite decades of effort, understanding the shape of genome space in biology remains a challenge due to the similarity, variability, diversity, and plasticity of evolutionary relationships among species, genes, or other biological entities. We present a k-mer topology method, the first of its kind, to delineate the shape of the genome space. K-mer topology examines the topological persistence and the evolution of the homotopic shape of the sequences of k nucleotides in species, organisms, and genes using persistent Laplacians, a new multiscale combinatorial approach. We also propose a topological genetic distance between species by their topological invariants and non-harmonic spectra over scales. This new metric defines the topological phylogenetic trees of genomes, facilitating species classification and clustering. K-mer topology substantially outperforms state-of-the-art methods on a variety of benchmark datasets, including mammalian mitochondrial genomes, Rhinovirus, SARS-CoV-2 variants, Ebola virus, Hepatitis E virus, Influenza hemagglutinin genes, and whole bacterial genomes. K-mer topology reveals the intrinsic shapes of the genome space and can be directly applied to the rational design of viral vaccines.
Forward citations
Cited by 3 Pith papers
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CAKL: Commutative algebra k-mer learning of genomics
CAKL, a persistent Stanley-Reisner k-mer representation, outperforms five baseline methods on 11 genomic benchmarks, especially viral classification.
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Topological Sequence Analysis of Genomes: Delta Complex approaches
The paper introduces Delta-complex and classifying-space persistent homology for DNA sequences and shows it clusters Ebola and bacterial genomes faster, though less accurately, than k-mer topology.
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Topological Data Analysis and Topological Deep Learning Beyond Persistent Homology -- A Review
A survey organizing recent TDA and TDL methods beyond persistent homology and connecting them to data structures and vectorization.
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