2024/11/04 by Abdulkadir Çelikkanat, Celikkanat, Abdulkadir, Andres R. Masegosa +3 · 1 citation
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Genomics (q-bio.GN) #Genomics and Phylogenetic Studies #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Probiotics and Fermented Foods #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2411.02125
openalex publication_date 2024/11/04 · openalex created_date 2024/11/15 · openalex updated_date 2026/07/28
Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures of DNA fragments from biological samples with the aim of determining their microbial compositions. In this paper, we revisit k-mer-based representations of genomes and provide a theoretical analysis of their use in representation learning. Based on the analysis, we propose a lightweight and scalable model for performing metagenomic binning at the genome read level, relying only on the k-mer compositions of the DNA fragments. We compare the model to recent genome foundation models and demonstrate that while the models are comparable in performance, the proposed model is significantly more effective in terms of scalability, a crucial aspect for performing metagenomic binning of real-world datasets.