Reliable and comprehensive monitoring data are required to trace and counteract biodiversity loss. Highthroughput metabarcoding using DNA extracted from community samples (bulk) or from water orsediment (environmenta...Reliable and comprehensive monitoring data are required to trace and counteract biodiversity loss. Highthroughput metabarcoding using DNA extracted from community samples (bulk) or from water orsediment (environmental DNA) has revolutionized biomonitoring, given the capability to assess biodiversity across the tree of life rapidly with feasible effort and at a modest price. DNA metabarcoding canbe upscaled to process hundreds of samples in parallel. However, while automated high-throughputanalysis workflows are well-established in the medical sector, manual sample processing still predominates in biomonitoring laboratory workflows limiting the upscaling and standardization for routinemonitoring applications. Here we present an automated, scalable, and reproducible metabarcodingworkflow to extract DNA from bulk samples, perform PCR and library preparation on a liquid handler.Key features are the independent sample replication throughout the workflow and the use of manynegative controls for quality assurance and quality control. We generated two datasets: i) a validationdataset consisting of 42 individual arthropod specimens of different species, and ii) a routine monitoringdataset consisting of 60 stream macroinvertebrate bulk samples. As a marker, we used the mitochondrialCOI gene. Our results show that the developed single-deck workflow is free of laboratory-derivedcontamination and produces highly consistent results. Minor deviations between replicates are mostlydue to stochastic differences for low abundant OTUs. Thus, we successfully demonstrated that roboticliquid handling can be used reliably from DNA extraction to final library preparation on a single deck,thereby substantially increasing throughput, reducing costs, and increasing data robustness for biodiversity assessments and monitoring.展开更多
文摘提出了一种基于相位相关法和加速鲁棒特性(SURF:Speeded-Up Robust Features)特征点匹配相结合的序列图像自动拼接算法。首先,利用相位相关法计算归一化相位相关度,通过最大相关度求交进行序列图像的自动排序,并计算得到平移参数;在平移参数指导下,粗估测特征检测感兴趣区域(ROI:Region of Interest)以改进SURF算法进行特征点提取,加快了结合快速最近邻搜索算法和随机采样一致性(RANSAC:Random Sample Consensus)算法的匹配过程的运算速度,提高了稳健性;最后,使用奇异值分解(SVD:Singular Value Decomposition)方法求得相邻图像变换参数,运用帧到拼接图像的思想进行图像拼接并融合得到无缝全景图。实验结果表明,本文算法不但有效地实现序列图像自动排序,与现有算法相比具有更好的实时性,而且合成的全景图具有高清晰度和良好的视觉一致性,具有较好的实用价值。
基金This study is a part of the GeDNA project funded by the German Environment Agency(FKZ 3719242040)All members are part of COST Action DNAqua-Net(CA15219)+1 种基金D..B is supported by a grant of the German Research Foundation(DFG,LE 2323/9-1)We thank Kristin Stolberg(LfU)for discussions and permission to use the metabarcoding data.
文摘Reliable and comprehensive monitoring data are required to trace and counteract biodiversity loss. Highthroughput metabarcoding using DNA extracted from community samples (bulk) or from water orsediment (environmental DNA) has revolutionized biomonitoring, given the capability to assess biodiversity across the tree of life rapidly with feasible effort and at a modest price. DNA metabarcoding canbe upscaled to process hundreds of samples in parallel. However, while automated high-throughputanalysis workflows are well-established in the medical sector, manual sample processing still predominates in biomonitoring laboratory workflows limiting the upscaling and standardization for routinemonitoring applications. Here we present an automated, scalable, and reproducible metabarcodingworkflow to extract DNA from bulk samples, perform PCR and library preparation on a liquid handler.Key features are the independent sample replication throughout the workflow and the use of manynegative controls for quality assurance and quality control. We generated two datasets: i) a validationdataset consisting of 42 individual arthropod specimens of different species, and ii) a routine monitoringdataset consisting of 60 stream macroinvertebrate bulk samples. As a marker, we used the mitochondrialCOI gene. Our results show that the developed single-deck workflow is free of laboratory-derivedcontamination and produces highly consistent results. Minor deviations between replicates are mostlydue to stochastic differences for low abundant OTUs. Thus, we successfully demonstrated that roboticliquid handling can be used reliably from DNA extraction to final library preparation on a single deck,thereby substantially increasing throughput, reducing costs, and increasing data robustness for biodiversity assessments and monitoring.
