Taxonova：深度学习实现快速且带置信度的微生物物种级扩增子分类
Taxonova: Deep learning enables fast and confidence-aware microbiome species-level amplicon classification
作者发布 Taxonova，一个面向短读长和长读长核糖体扩增子的深度学习分类框架，可在 250 bp 至 4,900 bp 的扩增子长度范围内做物种级分类。在覆盖七个生物群系的真实与模拟 mock 群落（配套基准工具 Taxobench）上，短读长物种级 F-measure 为 0.76–1.00（中位数 0.94），长读长为 0.82–0.99（中位数 0.97）；物种级丰度估计误差（L1 范数）按扩增子区域中位数降低 0.52 至 1.50，物种级假阳性率中位数比其他分类器低 16.2 × 10⁻⁴。
来源：PLOS Computational Biology · journals.plos.org
Taxonova 在 250 bp 到 4,900 bp 的扩增子长度范围内统一做物种级分类，并给出置信度，方便读者判断它能否替代现有分类器。
by Kardokh Kakabra, Shriram Patel, Andriy Temko, Marcus J. Claesson
Accurate species-level taxonomic classification of microbial amplicon sequences remains challenging due to the difficulty of resolving subtle variations within conserved ribosomal regions. Traditional similarity-based methods struggle with short amplicons and sequences with poor database representation. No existing method performs robustly across the full spectrum of amplicon lengths, limiting applicability across diverse sequencing platforms and variable-region targets. We present Taxonova, a deep learning framework for both short- and long-read ribosomal amplicons. Evaluated across physical and biome-simulated mock communities representing seven biomes (via the accompanying benchmark tool Taxobench), Taxonova attains species-level F-measures of 0.76–1.00 (median 0.94) for short-read and 0.82–0.99 (median 0.97) for long-read amplicons spanning 250 bp to 4,900 bp, reduces the species-level abundance-estimation error (L1 norm) by a median of 0.52 to 1.50 depending on amplicon region, and has a species-level false-positive rate that is a median of 16.2 × 10−4 lower than that of the other classifiers. Species-level assignment is expected to be most reliable for well-characterised communities, such as human-associated microbiomes. To demonstrate real-world applicability, we also compared amplicon-derived taxonomic profiles to paired shotgun whole-genome sequencing from gut, soil and vaginal microbiomes; Taxonova achieves the strongest species-level concordance (Jaccard) in all three studies, with a species-level Jaccard index 0.009 to 0.022 higher than that of the next-best classifier across both high- and low-complexity communities. Taxonova runs at a throughput of roughly 100 sequences per second on a single CPU core, making it suitable for routine deployment in research settings.