Benchmarking assembly free nanopore read mappers to classify complex millipede gut microbiota via Oxford Nanopore Sequencing Technology.

Journal of biological methods Pub Date : 2023-08-04 eCollection Date: 2023-01-01 DOI:10.14440/jbm.2023.376
Orlando J Geli-Cruz, Carlos J Santos-Flores, Matias J Cafaro, Alex Ropelewski, Alex R Van Dam
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Abstract

Millipedes are key players in recycling leaf litter into soil in tropical ecosystems. To elucidate their gut microbiota, we collected millipedes from different municipalities of Puerto Rico. Here we aim to benchmark which method is best for metagenomic skimming of this highly complex millipede microbiome. We sequenced the gut DNA with Oxford Nanopore Technologies' (ONT) MinION sequencer, then analyzed the data using MEGAN-LR, Kraken2 protein mode, Kraken2 nucleotide mode, GraphMap, and Minimap2 to classify these long ONT reads. From our two samples, we obtained a total of 87,110 and 99,749 ONT reads, respectively. Kraken2 nucleotide mode classified the most reads compared to all other methods at the phylum and class taxonomic level, classifying 75% of the reads in the two samples, the other methods failed to assign enough reads to either phylum or class to yield asymptotes in the taxa rarefaction curves indicating that they required more sequencing depth to fully classify this community. The community is hyper diverse with all methods classifying 20-50 phyla in the two samples. There was significant overlap in the reads used and phyla classified between the five methods benchmarked. Our results suggest that Kraken2 nucleotide mode is the most appropriate tool for the application of metagenomic skimming of this highly complex community.

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通过牛津纳米孔测序技术对无组装纳米孔读数映射器进行基准测试,以对复杂的千足虫肠道微生物群进行分类
千足虫是热带生态系统中将落叶回收到土壤中的关键参与者。为了阐明它们的肠道微生物群,我们收集了来自波多黎各不同城市的千足虫。在这里,我们的目标是确定哪种方法最适合对这种高度复杂的千足菌微生物组进行宏基因组撇除。我们使用Oxford Nanopore Technologies的(ONT)MinION测序仪对肠道DNA进行测序,然后使用MEGAN-LR、Kraken2蛋白模式、Kraken 2核苷酸模式、GraphMap和Minimap2对数据进行分析,以对这些长ONT读数进行分类。从我们的两个样本中,我们分别获得了87110和99749个ONT读数。在门和类分类学水平上,与所有其他方法相比,Kraken2核苷酸模式分类的读数最多,对两个样本中75%的读数进行了分类,其他方法未能将足够的读数分配给门或类,从而在分类群稀疏曲线中产生渐近线,这表明它们需要更多的测序深度才能对该群落进行完全分类。群落高度多样化,所有方法在两个样本中对20-50个门进行分类。五种基准方法之间使用的读数和分类的门有显著重叠。我们的结果表明,Kraken2核苷酸模式是应用宏基因组撇除这一高度复杂群落的最合适工具。
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