GenCore: Genomic distance estimation using Locally Consistent Parsing

2026 · doi:10.64898/2026.01.10.698768
preprint OA: closed CC-BY-NC-4.0
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The paper studies Locally Consistent Parsing (LCP) as a string-processing method for efficiently representing large genomic sequences, extending prior work that focused on iteratively identifying low-collision cores for the DNA alphabet (Lcptools). It introduces GenCore, which uses LCP-derived cores to sketch genomes and estimate genomic distances for closely related large genomes, including reconstructing simulated progression trees and recapitulating primate phylogeny using either telomere-to-telomere assemblies or PacBio HiFi reads in assembly-free comparisons. A key limitation explicitly implied by the scope is that the method targets closely related genomes and relies on LCP core-based sketching rather than a broader model of distant evolutionary divergence. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

In the era of exponential data generation, a fast, consistent, and efficient string processing technique is necessary to represent extensive genomic data. One of the earliest string processing techniques, predating MinHash and minimizer-based sketching, is Locally Consistent Parsing (LCP). This technique partitions an input string and identifies short, exactly occurring substrings called cores , which collectively cover the input string while maintaining Partition and Labeling Consistency . The iterative application of LCP yields progressively longer cores in a compressed format, thereby substantially enhancing the efficiency of genomic sequence representation and subsequent downstream analysis. We have previously developed Lcptools as the first iterative implementation of LCP for the DNA alphabet and demonstrated its effectiveness in identifying cores with minimal collisions. Here, we introduce GenCore, a computational method that leverages LCP cores for the first time to sketch and estimate genomic distances for closely related large genomes, and successfully reconstruct simulated progression trees. G en C ore also successfully recapitulates primate phylogeny using both telomere-to-telomere (T2T) assemblies and the PacBio HiFi reads for assembly-free comparisons. Availability GenCore is available at https://github.com/BilkentCompGen/gencore
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Abstract In the era of exponential data generation, a fast, consistent, and efficient string processing technique is necessary to represent extensive genomic data. One of the earliest string processing techniques, predating MinHash and minimizer-based sketching, is Locally Consistent Parsing (LCP). This technique partitions an input string and identifies short, exactly occurring substrings called cores, which collectively cover the input string while maintaining Partition and Labeling Consistency. The iterative application of LCP yields progressively longer cores in a compressed format, thereby substantially enhancing the efficiency of genomic sequence representation and subsequent downstream analysis. We have previously developed Lcptools as the first iterative implementation of LCP for the DNA alphabet and demonstrated its effectiveness in identifying cores with minimal collisions. Here, we introduce GenCore, a computational method that leverages LCP cores for the first time to sketch and estimate genomic distances for closely related large genomes, and successfully reconstruct simulated progression trees. GenCore also successfully recapitulates primate phylogeny using both telomere-to-telomere (T2T) assemblies and the PacBio HiFi reads for assembly-free comparisons. Availability GenCore is available at https://github.com/BilkentCompGen/gencore Competing Interest Statement The authors have declared no competing interest. Footnotes akmuhammet{at}bilkent.edu.tr, ege.sirvan{at}ug.bilkent.edu.tr, ecem.ilgun{at}bilkent.edu.tr, salem.malikic{at}nih.gov, cenk.sahinalp{at}nih.gov, t.batu{at}lse.ac.uk

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