Characterization of microbial assemblages via environmental DNA metabarcoding is increasingly being used in routine monitoring programs due to its sensitivity and cost-effectiveness. Several programs have recently been developed which infer functional profiles from 16S rRNA gene data using hidden-state prediction (HSP) algorithms. These might offer an economic and scalable alternative to shotgun metagenomics. To date, HSP-based methods have seen limited use for benthic marine surveys and their performance in these environments remains unevaluated. In this study, 16S rRNA metabarcoding was applied to sediment samples collected at 0 and ≥1,200 m from Norwegian salmon farms, and three metabolic inference approaches (Paprica, Picrust2 and Tax4Fun2) evaluated against metagenomics and environmental data. While metabarcoding and metagenomics recovered a comparable functional diversity, the taxonomic composition differed between approaches, with genera richness up to 20× higher for metabarcoding. Comparisons between the sensitivity (highest true positive rates) and specificity (lowest true negative rates) of HSP-based programs in detecting functions found in metagenomic data ranged from 0.52 and 0.60 to 0.76 and 0.79, respectively. However, little correlation was observed between the relative abundance of their specific functions. Functional beta-diversity of HSP-based data was strongly associated with that of metagenomics (r ≥ 0.86 for Paprica and Tax4Fun2) and responded similarly to the impact of fish farm activities. Our results demonstrate that although HSP-based metabarcoding approaches provide a slightly different functional profile than metagenomics, partly due to recovering a distinct community, they represent a cost-effective and valuable tool for characterizing and assessing the effects of fish farming on benthic ecosystems.

中文翻译：

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超越分类法：在养鱼场影响评估的背景下验证功能推理方法

由于其敏感性和成本效益，通过环境 DNA 元条形码表征微生物组合越来越多地用于常规监测计划。最近开发了几个程序，它们使用隐藏状态预测 (HSP) 算法从 16S rRNA 基因数据推断功能概况。这些可能会提供一种经济且可扩展的替代鸟枪宏基因组学的方法。迄今为止，基于 HSP 的方法在底栖海洋调查中的使用有限，它们在这些环境中的性能仍未得到评估。在这项研究中，的16S rRNA metabarcoding施加到在从挪威鲑鱼养殖场0和≥1,200米收集沉积物样品和三个代谢推理方法（Paprica，Picrus吨2和Tax4Fun2）根据宏基因组学和环境数据进行评估。虽然元条形码和宏基因组学恢复了相当的功能多样性，但不同方法的分类组成不同，元条形码的属丰富度高达 20 倍。基于 HSP 的程序在检测宏基因组数据中发现的功能方面的敏感性（最高真阳性率）和特异性（最低真阴性率）之间的比较范围分别为 0.52 和 0.60 到 0.76 和 0.79。然而，在它们特定功能的相对丰度之间几乎没有观察到相关性。基于HSP-数据的功能的β-多样性强烈与宏基因组学的（R≥0.86关联Paprica和Tax4Fun2) 并对养鱼场活动的影响做出了类似的反应。我们的结果表明，尽管基于 HSP 的元条形码方法提供了与宏基因组学略有不同的功能特征，部分原因是恢复了一个独特的群落，但它们代表了一种具有成本效益且有价值的工具，用于表征和评估鱼类养殖对底栖生态系统的影响。