Kyle Kathleen E, Klassen Jonathan L
Department of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut, USA.
Institute for Systems Genomics, University of Connecticut, Storrs, Connecticut, USA.
Appl Environ Microbiol. 2025 Jan 31;91(1):e0153724. doi: 10.1128/aem.01537-24. Epub 2024 Dec 26.
Advances in DNA metabarcoding have greatly expanded our knowledge of microbial communities in recent years. Pipelines and parameters have been tested extensively for bacterial metabarcoding using the 16S rRNA gene and best practices are largely established. For fungal metabarcoding using the internal transcribed spacer (ITS) gene, however, only a few studies have considered how such pipelines and parameters can affect community prediction. Here, we report a novel bias uncovered during ITS region 2 (ITS2) sequencing of -infected ant fungus gardens and confirmed this bias using mock communities. Abnormally low forward read quality caused ITS2 reads to be computationally filtered before and during read pair merging, thus almost entirely eliminating amplicon sequence variants from the resulting fungal community profiles. Sliding window quality trimming before filtering allowed most of these reads to pass filtering and merge successfully, producing community profiles that now correlated with visual signs of infection and matched the composition of the mock communities. Applying such sliding window trimming to a previously generated environmental ITS2 data set increased the detected fungal diversity and again overcame read quality biases against to detect it in nearly every sample instead and often at high relative abundances. This analysis additionally identified a similar, but distinct, bias against a second fungal genus . The prevalence of such quality biases against other fungal ITS sequences is unknown but may be widespread. We, therefore, advocate for the routine use of sliding window quality trimming as a best practice in ITS2 metabarcoding analysis.
Metabarcode sequencing produces DNA abundance profiles that are presumed to reflect the actual microbial composition of their corresponding input samples. However, this assumption is not always tested, and taxon-specific biases are often not apparent, especially for low-abundance taxa in complex communities. Here, we identified internal transcribed spacer region 2 (ITS2) read quality aberrations that caused dramatic reductions in the relative abundances of specific taxa in multiple data sets characterizing ant fungus gardens. Such taxon-specific biases in read quality may be widespread in other environments and for other fungal taxa, thereby causing incorrect descriptions of these mycobiomes.
近年来,DNA宏条形码技术的进展极大地扩展了我们对微生物群落的认识。使用16S rRNA基因进行细菌宏条形码分析的流程和参数已经得到广泛测试,并且在很大程度上确立了最佳实践。然而,对于使用内转录间隔区(ITS)基因进行真菌宏条形码分析,只有少数研究考虑了此类流程和参数如何影响群落预测。在此,我们报告了在感染蚁巢真菌园的ITS区域2(ITS2)测序过程中发现的一种新偏差,并使用模拟群落证实了这种偏差。正向读数质量异常低导致ITS2读数在读取对合并之前和期间被计算过滤,从而几乎完全消除了所得真菌群落图谱中的扩增子序列变体。在过滤之前进行滑动窗口质量修剪使这些读数中的大多数能够通过过滤并成功合并,从而生成与感染的视觉迹象相关且与模拟群落组成相匹配的群落图谱。将这种滑动窗口修剪应用于先前生成的环境ITS2数据集增加了检测到的真菌多样性,并且再次克服了针对[未提及的真菌属]的读数质量偏差,几乎在每个样本中都能检测到它,而且通常相对丰度较高。该分析还发现了针对另一个真菌属的类似但不同的偏差。针对其他真菌ITS序列的这种质量偏差的普遍性尚不清楚,但可能很普遍。因此,我们主张在ITS2宏条形码分析中常规使用滑动窗口质量修剪作为最佳实践。
宏条形码测序产生的DNA丰度图谱被假定反映其相应输入样本的实际微生物组成。然而,这一假设并非总是经过检验,而且分类群特异性偏差往往并不明显,特别是对于复杂群落中的低丰度分类群。在此,我们确定了内转录间隔区2(ITS2)读数质量异常,这些异常导致在多个表征蚁巢真菌园的数据集里特定分类群的相对丰度大幅降低。读数质量方面的这种分类群特异性偏差可能在其他环境和其他真菌分类群中广泛存在,从而导致对这些真菌群落的错误描述。