用概率分布表示DNA序列，提升微生物基因组分箱精度。
UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning
- 将DNA片段建模为潜空间中的概率分布，显式捕捉序列不确定性。
- 在真实数据集上优于传统确定性方法，提升分箱准确率。
- 适合大规模宏基因组分析，轻量高效，可扩展性强。
宏基因组分箱旨在将混合微生物样本中的DNA片段聚类到对应的基因组中，是微生物群落下游分析的关键步骤。现有方法依赖于确定性表示（如k-mer谱或大语言模型嵌入），无法捕捉因物种间DNA共享及片段高度相似带来的固有不确定性。本文提出首个概率嵌入方法UncertainGen，将每个DNA片段表示为潜空间中的概率分布，自然建模序列级不确定性，并提供嵌入可区分性的理论保证。该框架通过引入数据自适应度量扩大可行潜空间，实现更灵活的分箱分离。在真实宏基因组数据集上的实验表明，相较于确定性k-mer和LLM嵌入，UncertainGen显著提升分箱性能，提供一种可扩展、轻量化的大型宏基因组分析解决方案。
原文摘要 · Abstract (English)
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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