Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?
Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?
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DOI：
10.1111/mec.14734
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发表时间：
2019-01
影响因子：
4.9
通讯作者：
Eveson JP
中科院分区：
文献类型：
--
作者：
Deagle BE;Thomas AC;McInnes JC;Clarke LJ;Vesterinen EJ;Clare EL;Kartzinel TR;Eveson JP
DNA测序技术的进步彻底改变了营养相互作用的分子分析领域，现在可以从广泛的饮食样品中恢复食物DNA序列的计数。但这些数字意味着什么呢？为了获得消费者饮食的准确估计，我们应该严格使用总结不同食物分类群出现频率的数据集，还是可以使用相对序列数？这两种方法都用于获得半定量饮食总结，但由于序列恢复中的分类群特异性偏倚，发生率数据通常被认为是更保守和可靠的选择。我们探索了代表性的饮食元条形码数据集，并指出基于发生率数据的饮食总结往往高估了少量食用食物（可能包括低水平污染物）的重要性，并且对用于定义发生率的计数阈值敏感。我们的模拟表明，使用相对读段丰度（RRA）信息通常可以更准确地了解群体水平的饮食，即使合并了中度恢复偏差;然而，RRA摘要对影响常见饮食分类群的恢复偏差敏感。当样本中食物类群的平均数量很小时，这两种方法都更准确。这里提出的想法强调需要考虑所有来源的偏见，并证明用于解释膳食元条形码研究计数数据的方法。我们鼓励研究人员继续解决方法上的挑战，并承认未回答的问题，以帮助刺激未来的调查在这个快速发展的研究领域。
Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of food DNA sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer's diet should we work strictly with data sets summarizing frequency of occurrence of different food taxa, or is it possible to use relative number of sequences? Both approaches are applied to obtain semi‐quantitative diet summaries, but occurrence data are often promoted as a more conservative and reliable option due to taxa‐specific biases in recovery of sequences. We explore representative dietary metabarcoding data sets and point out that diet summaries based on occurrence data often overestimate the importance of food consumed in small quantities (potentially including low‐level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that using relative read abundance (RRA) information often provides a more accurate view of population‐level diet even with moderate recovery biases incorporated; however, RRA summaries are sensitive to recovery biases impacting common diet taxa. Both approaches are more accurate when the mean number of food taxa in samples is small. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue addressing methodological challenges and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research.
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