mKmer: an unbiased K -mer embedding of microbiomic single-microbe RNA sequencing data

作者：Fangyu Mo, Qinghong Qian, Xiaolin Lu, Dihuai Zheng, Wenjie Cai, Jie Yao, Hongyu Chen, Yujie Huang, Xiang Zhang, Sanling Wu, Yifei Shen, Yinqi Bai, Yongcheng Wang, Weiqin Jiang, Longjiang Fan · 发表于：Briefings in Bioinformatics · 年份：2025 · DOI：10.1093/bib/bbaf227 · 被引用次数：3 · 研究领域：Gut microbiota and health、Microbial Community Ecology and Physiology、Genomics and Phylogenetic Studies

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.