Increasingly available microbial reference data allow interpreting the composition and function of previously uncharacterized microbial communities in detail, via high-throughput sequencing analysis. However, efficient methods for read classification are required when the best database matches for short sequence reads are often shared among multiple reference sequences. Here, we take advantage of the fact that microbial sequences can be annotated relative to established tree structures, and we develop a highly scalable read classifier, PRROMenade, by enhancing the generalized Burrows-Wheeler transform with a labeling step to directly assign reads to the corresponding lowest taxonomic unit in an annotation tree. PRROMenade solves the multi-matching problem while allowing fast variable-size sequence classification for phylogenetic or functional annotation. Our simulations with 5% added differences from reference indicated only 1.5% error rate for PRROMenade functional classification. On metatranscriptomic data PRROMenade highlighted biologically relevant functional pathways related to diet-induced changes in the human gut microbiome.

越来越多的微生物参考数据可以通过高通量测序分析详细解释以前未表征的微生物群落的组成和功能。然而，当短序列读段的最佳数据库匹配通常在多个参考序列之间共享时，需要有效的读段分类方法。在这里，我们利用微生物序列可以相对于已建立的树结构进行注释的事实，我们开发了一种高度可扩展的读段分类器 PRROMenade，通过使用标记步骤增强广义 Burrows-Wheeler 变换，将读段直接分配给相应的读段。注释树中的最低分类单元。PRROMenade 解决了多重匹配问题，同时允许快速可变大小序列分类以进行系统发育或功能注释。我们的模拟与参考相比增加了 5% 的差异，表明 PRROMenade 功能分类的错误率仅为 1.5%。根据宏转录组数据，PRROMenade 强调了与饮食诱导的人类肠道微生物组变化相关的生物学相关功能途径。