UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning
This addresses the challenge of accurately binning DNA fragments in microbial communities, which is critical for downstream analyses, though it appears incremental as it builds on existing embedding methods by adding uncertainty modeling.
The paper tackles the problem of metagenomic binning, where DNA fragments from mixed microbial samples need to be clustered into genomes, by introducing UncertainGen, the first probabilistic embedding approach that models sequence-level uncertainty, resulting in improvements over deterministic methods like k-mer and LLM-based embeddings on real datasets.
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.