Computational identification and quantification of distinct microbes from high throughput sequencing data is crucial for our understanding of human health. Existing methods either use accurate but computationally expensive alignment-based approaches or less accurate but computationally fast alignment-free approaches, which often fail to correctly assign reads to genomes. Here we introduce CAMMiQ, a combinatorial optimization framework to identify and quantify distinct genomes (specified by a database) in a metagenomic dataset. As a key methodological innovation, CAMMiQ uses substrings of variable length and those that appear in two genomes in the database, as opposed to the commonly used fixed-length, unique substrings. These substrings allow to accurately decouple mixtures of highly similar genomes resulting in higher accuracy than the leading alternatives, without requiring additional computational resources, as demonstrated on commonly used benchmarking datasets. Importantly, we show that CAMMiQ can distinguish closely related bacterial strains in simulated metagenomic and real single-cell metatranscriptomic data.

从高通量测序数据中计算识别和定量不同微生物对于我们了解人类健康至关重要。现有方法要么使用准确但计算成本昂贵的基于比对的方法，要么使用不太准确但计算快速的无比对方法，这些方法通常无法正确地将读数分配给基因组。在这里，我们介绍 CAMMiQ，一个组合优化框架，用于识别和量化宏基因组数据集中的不同基因组（由数据库指定）。作为一项关键的方法创新，CAMMiQ 使用可变长度的子串以及出现在数据库中两个基因组中的子串，而不是常用的固定长度的唯一子串。这些子串可以准确地解耦高度相似的基因组的混合物，从而比领先的替代方案具有更高的准确性，而无需额外的计算资源，如常用的基准测试数据集所示。重要的是，我们证明 CAMMiQ 可以在模拟宏基因组和真实单细胞宏转录组数据中区分密切相关的细菌菌株。