Year: 2025
Type: article
Abstract: The widespread adoption of k -mers in computational biology has enabled efficient methods for utilizing genomic sequences in a variety of biological tasks. However, understanding the influence of k -mer sizes within these methods remains a persistent challenge. Slicing sequences into a fixed size lacks grounding in biological insight, and complex bioinformatics pipelines obscure the effect of the parameter k due to various noisy factors. The choice of k -mer size is typically arbitrary, with justification omitted in both the literature and method tutorials. Furthermore, the lack of theoretical understanding has caused recent multi- k -mer approaches to face significant computational challenges. Nevertheless, most methods are built on well-defined objects related to k -mers, such as de Bruijn graphs, Jaccard similarity, Bray-Curtis dissimilarity, and k -mer spectra. The role of k -mer sizes within these objects is more intuitive and can be described by numerous quantities and metrics. Therefore, exploring these objects across k -mer sizes opens opportunities for robust analyses and new applications. However, the evolution of k -mer objects with respect to k -mer sizes is surprisingly elusive. We introduce a novel substring index, the Pro k rustean graph, that elucidates the transformation of k -mer sets across k -mer sizes. Our framework built upon this index rapidly computes k -mer-based quantities for all k -mer sizes, with computational complexity independent of the size range and dependent only on maximal repeats. For example, counting unitigs (maximal simple paths) in de Bruijn graphs for k = 1, …, 100 is achieved in seconds using our index on a gigabase-scale dataset. We present a variety of algorithms for applications relevant to pangenomics and metagenomics. The Pro k rustean graph is directly derived from the affix tree and can be constructed space-efficiently from the Burrows–Wheeler transform. This derivation grounded in modern substring indexes that are all theoretically based on longest common prefixes reveals that such extension-based substring representations inherently struggle to explore k -mer objects across different sizes, which motivated our data structure. Our implementation is available at: https://github.com/KoslickiLab/prokrustean .
Source: Journal of Computational Biology
Authors: Adam Park, David Koslicki
Institution: Pennsylvania State University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funder: National Institutes of Health
OpenAlex ID: W7117319255