GenCore: Genomic distance estimation using Locally Consistent Parsing.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Genomic data is rapidly increasing, necessitating efficient string processing techniques.
• Locally Consistent Parsing (LCP) is an established method for partitioning strings and identifying core substrings.
• Previous work introduced Lcptools for iterative LCP implementation on DNA sequences.

Purpose of the Study:

• Introduce GenCore, a computational method leveraging LCP cores for genomic sketching and distance estimation.
• Evaluate GenCore's ability to reconstruct simulated progression trees.
• Assess GenCore's performance in recapitulating primate phylogeny using diverse genomic data.

Main Methods:

• GenCore applies iterative LCP to partition genomic sequences and extract core substrings.
• Genomic distances are estimated based on shared LCP cores.
• Phylogenetic reconstruction is performed using GenCore on telomere-to-telomere assemblies and PacBio HiFi reads.

Main Results:

• GenCore effectively sketches large, closely related genomes and estimates genomic distances.
• The method successfully reconstructs simulated progression trees.
• GenCore recapitulates known primate phylogeny, demonstrating its utility for both assembly-based and assembly-free comparisons.

Conclusions:

• GenCore provides an efficient and accurate method for genomic sequence representation and analysis.
• The LCP core-based approach offers a powerful tool for comparative genomics and phylogenetic studies.
• GenCore enhances the analysis of large-scale genomic datasets, including complex structures like telomere-to-telomere assemblies.