Structural Compression for Phylogenetic Inference under Alignment Instability and Indel-Rich Evolution
Phylogenetic inference traditionally relies on aligned characters under substitution models, but this framework becomes less reliable when alignments are unstable or when evolution is dominated by insertions, deletions, repeats, and other structural changes. We adapt Ladderpath as an alignment-free distance approach for phylogenetic inference. Motivated by algorithmic information theory, Ladderpath decomposes sequences into derived, reusable units (``ladderons'', rather than fixed-length $k$-mers) organized hierarchically, from which pairwise distances are computed. The premise is that shared derived sequence structure, including repeated or reused segments that are poorly represented by column-wise substitutions, can retain phylogenetic information. The bacteriophage T7 known lineage, the cpSSR repeat-rich marker, and a cytochrome~$c$ protein dataset confirm that Ladderpath recovers topologies consistent with the known experimental history or with established alignment-based methods. Its advantage emerges under stress: in block-translocation and indel-dominated simulations Ladderpath remains stable while alignment-dependent pipelines deteriorate; on banana mitochondrial and plastome genomes it scales to genome length and captures the expected contrast between organellar histories, all from unaligned input. These results support Ladderpath as an alignment-free, structurally informed method that could complement standard pipelines in cases where higher-order sequence structure carries phylogenetic signal.