CLMB：用于稳健宏基因组分箱的深度对比学习
CLMB: deep contrastive learning for robust metagenomic binning
摘要：
从大型宏基因组数据集重建微生物基因组是发现未培养微生物种群并确定其微生物功能作用的关键步骤。为了实现这一点，我们需要执行宏基因组分箱，将组装的重叠群聚类到基因组草案中。尽管现有的计算工具，大多数都忽略了宏基因组数据的一个重要属性，即噪声。为了进一步改进宏基因组分箱步骤，重构出更好的宏基因组，本文提出了一种用于宏基因组分箱的深度对比学习框架（CLMB），该框架可以有效地消除噪声的干扰，产生更稳定和鲁棒的结果。从本质上讲，我们不是显式地对数据进行去噪，而是将模拟噪声添加到训练数据中，并迫使深度学习模型为无噪声数据和失真数据生成相似且稳定的表示。因此，经过训练的模型将对噪声具有鲁棒性，并在使用过程中隐式地处理它。CLMB显著优于之前最先进的分箱方法，在几乎所有基准数据集上恢复最接近完整的基因组（与第二好的方法相比，重建的基因组多17%）。它还提高了bin细化的性能，比第二好的结果多重建8 - 22个高质量基因组和15 - 32个中等质量基因组。令人印象深刻的是，除了与分级细化器兼容之外，单个CLMB甚至比VAMB和Maxbin的细化器在基准数据集上平均多恢复15个HQ基因组。CLMB是开源的，可在www.example.com上获得。
英文摘要：
The reconstruction of microbial genomes from large metagenomic datasets is a critical procedure for finding uncultivated microbial populations and defining their microbial functional roles. To achieve that, we need to perform metagenomic binning, clustering the assembled contigs into draft genomes. Despite the existing computational tools, most of them neglect one important property of the metagenomic data, that is, the noise. To further improve the metagenomic binning step and reconstruct better metagenomes, we propose a deep Contrastive Learning framework for Metagenome Binning (CLMB), which can efficiently eliminate the disturbance of noise and produce more stable and robust results. Essentially, instead of denoising the data explicitly, we add simulated noise to the training data and force the deep learning model to produce similar and stable representations for both the noise-free data and the distorted data. Consequently, the trained model will be robust to noise and handle it implicitly during usage. CLMB outperforms the previous state-of-the-art binning methods significantly, recovering the most near-complete genomes on almost all the benchmarking datasets (up to 17\% more reconstructed genomes compared to the second-best method). It also improves the performance of bin refinement, reconstructing 8-22 more high-quality genomes and 15-32 more middle-quality genomes than the second-best result. Impressively, in addition to being compatible with the binning refiner, single CLMB even recovers on average 15 more HQ genomes than the refiner of VAMB and Maxbin on the benchmarking datasets. CLMB is open-source and available at https://github.com/zpf0117b/CLMB/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
