David Koslicki

Sep 15, 2024 | biorxiv.org | Adam Park |David Koslicki

AbstractDespite the widespread adoption of k-mer-based methods in bioinformatics, understanding the influence of k-mer sizes remains a persistent challenge. Selecting an optimal k-mer size or employing multiple k-mer sizes is often arbitrary, application-specific, and fraught with computational complexities. Typically, the influence of k-mer size is obscured by the outputs of complex bioinformatics tasks, such as genome analysis, comparison, assembly, alignment, and error correction.

Jul 30, 2024 | biorxiv.org | Wei Wei |David Koslicki

AbstractDistance-guided tree construction with unknown tree topology and branch lengths has been a long studied problem. In contrast, distance-guided branch lengths assignment with fixed tree topology has not yet been systematically investigated, despite having significant applications. In this paper, we provide a formal mathematical formulation of this problem and propose two representative methods for solving this problem, each with its own strength.

Jun 1, 2024 | biorxiv.org | Adam Park |David Koslicki

AbstractDespite the widespread adoption of k-mer-based methods in bioinformatics, a fundamental question persists: How can we quantify the influence of k sizes in applications? With no universal answer available, choosing an optimal k size or employing multiple k sizes remains application-specific, arbitrary, and computationally expensive.

Apr 8, 2024 | biorxiv.org | Shaopeng Liu |David Koslicki

AbstractMotivation: Sketching methods provide scalable solutions for analyzing rapidly growing genomic data. A recent innovation in sketching methods, syncmers, has proven effective and has been employed for read alignment. Syncmers share fundamental features with the FracMinHash technique, a recent modification of the popular MinHash algorithm for set similarity estimation between sets of different sizes.

Mar 16, 2024 | biorxiv.org | Chunyu Ma |Shaopeng Liu |David Koslicki

AbstractMotivation: The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes.