Mo Fangyu, Qian Qinghong, Lu Xiaolin, Zheng Dihuai, Cai Wenjie, Yao Jie, Chen Hongyu, Huang Yujie, Zhang Xiang, Wu Sanling, Shen Yifei, Bai Yinqi, Wang Yongcheng, Jiang Weiqin, Fan Longjiang
Hainan Institute, Zhejiang University, Zhenzhou Road, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, Hainan Province, China.
Institute of Crop Science, Zhejiang University, 866 Yuhangtang Road, Xihu District, Hangzhou 310058, Zhejiang Province, China.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf227.
The advanced single-microbe RNA sequencing (smRNA-seq) technique addresses the pressing need to understand the complexity and diversity of microbial communities, as well as the distinct microbial states defined by different gene expression profiles. Current analyses of smRNA-seq data heavily rely on the integrity of reference genomes within the queried microbiota. However, establishing a comprehensive collection of microbial reference genomes or gene sets remains a significant challenge for most real-world microbial ecosystems. Here, we developed an unbiased embedding algorithm utilizing K-mer signatures, named mKmer, which bypasses gene or genome alignment to enable species identification for individual microbes and downstream functional enrichment analysis. By substituting gene features in the canonical cell-by-gene matrix with highly conserved K-mers, we demonstrate that mKmer outperforms gene-based methods in clustering and motif inference tasks using benchmark datasets from crop soil and human gut microbiomes. Our method provides a reference genome-free analytical framework for advancing smRNA-seq studies.
先进的单微生物RNA测序（smRNA-seq）技术满足了人们迫切需要，即了解微生物群落的复杂性和多样性，以及由不同基因表达谱定义的独特微生物状态。目前对smRNA-seq数据的分析严重依赖于所查询微生物群中参考基因组的完整性。然而，对于大多数实际的微生物生态系统而言，建立全面的微生物参考基因组或基因集仍然是一项重大挑战。在此，我们开发了一种利用K-mer特征的无偏嵌入算法，名为mKmer，它绕过基因或基因组比对，能够对单个微生物进行物种鉴定并进行下游功能富集分析。通过用高度保守的K-mer替代标准基因对细胞矩阵中的基因特征，我们证明在使用来自作物土壤和人类肠道微生物群的基准数据集进行聚类和基序推断任务时，mKmer优于基于基因的方法。我们的方法为推进smRNA-seq研究提供了一个无需参考基因组的分析框架。
