Title: UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: metagenomic binning, probabilistic embeddings, dna, genome

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.

TL;DR: We introduce the first probabilistic embedding framework for metagenomic binning, modeling each DNA fragment as a probability distribution to capture uncertainty, with closed-form contrastive learning and theoretical guarantees.