Maximising signal-to-noise ratios in environmental DNA-based monitoring.
Maximising signal-to-noise ratios in environmental DNA-based monitoring.
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最大限度地提高基于 DNA 的环境监测中的信噪比。
DOI：
10.1016/j.scitotenv.2022.159735
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Wilding TA
中科院分区：
文献类型：
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作者：
Wilding TA
人类对全球生态系统的影响正在增加，对这些活动进行适当监测的需求日益增加。监测需要测量响应度量（“信号”），该响应度量（“信号”）随监测活动的变化最大且一致，而不受其他因素（“噪声”）的影响，从而使信噪比最大化。耗时的基于形态的生物分类鉴定得出的指数是许多监测计划的核心部分。元条形码是基于形态学的鉴定的一种替代方法，涉及同时对来自多个分类群的DNA短片段（“标记”）进行测序。适合元条形码的DNA包括从环境样品中提取的DNA （eDNA）。元条形码输出的DNA序列可以通过将它们与存档的注释序列进行匹配来识别（注释）。然而，来自大多数生物体的序列并没有被存档，这阻碍了注释，并潜在地限制了元条形码在监测应用中的应用。因此，在监测程序中使用无注释序列作为响应指标的兴趣越来越大。比较了16S （V3/V4区）、18S （V1/V2区）和COI三种常用标记的序列，沿较陡的冲击梯度采样，发现16S和COI序列的信噪比分别最大和最小。我们试验了四种独立的、直观的降噪方法，结果表明，去除频率较低的序列提高了信噪比，在非参数方差分析（NPA）中，将噪声与解释因素额外分割了25 %，并降低了数据中的离散度。对于16S标记，每个样本只保留最频繁观察到的序列，在150个样本中产生9个序列，产生了接近最大的信噪比（NPA中解释的方差的95 %）。我们建议将NPA与严格淘汰频率较低的序列相结合，用于监测应用中使用的序列/分类群的预过滤。我们的方法将简化下游分析，例如关键分类群和功能关联的识别。
Man's impacts on global ecosystems are increasing and there is a growing demand that these activities be appropriately monitored. Monitoring requires measurement of a response metric (‘signal’) that changes maximally and consistently in response to the monitored activity irrespective of other factors (‘noise’), thus maximising the signal-to-noise ratio. Indices derived from time-consuming morphology-based taxonomic identification of organisms are a core part of many monitoring programmes. Metabarcoding is an alternative to morphology-based identification and involves the sequencing of short fragments of DNA (‘markers’) from multiple taxa simultaneously. DNA suitable for metabarcoding includes that extracted from environmental samples (eDNA). Metabarcoding outputs DNA sequences that can be identified (annotated) by matching them against archived annotated sequences. However, sequences from most organisms are not archived - preventing annotation and potentially limiting metabarcoding in monitoring applications. Consequently, there is growing interest in using unannotated sequences as response metrics in monitoring programmes.We compared the sequences from three commonly used markers (16S (V3/V4 regions), 18S (V1/V2 regions) and COI) and, sampling along steep impact gradients, showed that the 16S and COI sequences were associated with the largest and smallest signal-to-noise ratio respectively. We trialled four separate, intuitive, noise-reduction approaches and demonstrated that removing less frequent sequences improved the signal-to-noise ratio, partitioning an additional 25 % from noise to explanatory factors in non-parametric ANOVA (NPA) and reducing dispersion in the data. For the 16S marker, retaining only the most frequently observed sequence, per sample, resulting in nine sequences across 150 samples, generated a near-maximal signal-to-noise ratio (95 % of the variance explained in NPA). We recommend that NPA, combined with rigorous elimination of less frequent sequences, be used to pre-filter sequences/taxa being used in monitoring applications. Our approach will simplify downstream analysis, for example the identification of key taxa and functional associations.