UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning

• URL: http://arxiv.org/abs/2509.26116v1
• Date: Tue, 30 Sep 2025 11:36:09 GMT
• Title: UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning
• Authors: Abdulkadir Celikkanat, Andres R. Masegosa, Mads Albertsen, Thomas D. Nielsen,
• Abstract summary: Metagenomic binning aims to cluster DNA fragments from mixed microbial samples into their respective genomes.<n>Existing methods rely on deterministic representations, such as k-mer profiles or embeddings from large language models.<n>We present the first probabilistic embedding approach, UncertainGen, representing each DNA fragment as a probability distribution in latent space.
• Score: 0.4666493857924358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Metagenomic binning aims to cluster DNA fragments from mixed microbial samples into their respective genomes, a critical step for downstream analyses of microbial communities. Existing methods rely on deterministic representations, such as k-mer profiles or embeddings from large language models, which fail to capture the uncertainty inherent in DNA sequences arising from inter-species DNA sharing and from fragments with highly similar representations. We present the first probabilistic embedding approach, UncertainGen, for metagenomic binning, representing each DNA fragment as a probability distribution in latent space. Our approach naturally models sequence-level uncertainty, and we provide theoretical guarantees on embedding distinguishability. This probabilistic embedding framework expands the feasible latent space by introducing a data-adaptive metric, which in turn enables more flexible separation of bins/clusters. Experiments on real metagenomic datasets demonstrate the improvements over deterministic k-mer and LLM-based embeddings for the binning task by offering a scalable and lightweight solution for large-scale metagenomic analysis.

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