SSUplex 发布：双链提取并区分来源的小亚基 rRNA 工具
SSUplex: fast, both-strand extraction and origin-sorting of small-subunit rRNA for environmental DNA metabarcoding
SSUplex 是一款用 Rust 实现的小亚基 rRNA 提取与来源判定工具，可同时检测双链上的 SSU rRNA，用保守区 profile HMM 把每条读段归为细菌、古菌、真核、线粒体或叶绿体五类来源，并提取目标基因区。它重写了 Metaxa2 的提取步骤，但按各区域得分之和而非均值排序来源。在 5,000 条 ZymoBIOMICS Nanopore 读段上，其准确率从 Metaxa2 提取步骤的 54.9% 提升到 95.9%，而 Metaxa2 完整流程配合相似性搜索分类为 99.96%。
来源：Bioinformatics Advances · doi.org
Motivation
Long-read sequencing has made full-length small-subunit ribosomal RNA metabarcoding routine, but broadly conserved primers also amplify organellar and cross-domain sequences, which distort profiles when sent to a target-domain classifier.
Results
SSUplex detects small-subunit ribosomal RNA on both strands with conserved-region profile hidden Markov models, assigns each read to one of five origins (bacteria, archaea, eukaryota, mitochondria, chloroplast) and extracts the gene region. It reimplements Metaxa2’s extraction step but ranks origins by summed rather than mean per-region score. On 5,000 ZymoBIOMICS Nanopore reads, this raised accuracy from 54.9% for Metaxa2’s extraction step to 95.9%, against 99.96% for Metaxa2’s full pipeline with similarity-search classification. On reads simulated from sources outside the Metaxa2 database, SSUplex was 93 to 98% accurate for bacteria-dominated and plant-root-like compositions (Metaxa2’s full pipeline: 96 to 99%), with mitochondria the weakest origin. In a rice-root 16S sample, about 40% of reads were organellar. SSUplex ran 3.2× faster than Metaxa2’s extraction step from 200,000 reads upward, with about 45% less peak memory, and 65 to 134× faster than Metaxa2’s full pipeline.
Availability and implementation
SSUplex is implemented in Rust and freely available at https://github.com/ayobi/ssuplex and from Bioconda; version 0.2.0 is archived at https://doi.org/10.5281/zenodo.23246210.