16S Amplicon sequence variants (ASVs) data of NEREA Augmented Observatory

Metabarcoding - 16S ASV generation and taxonomic assignment. Raw 16S paired-end sequences were subjected to a data quality control step and subsequently imported into the QIIME2 pipeline v.2022.2.0. Leftover primers and adapter sequences were removed through cutadapt. The amplicon sequence variants (ASV) table, which represent true biological sequences within each sample, was generated using the denoised-paired method including truncation, denoising, dereplication, merging, and chimera filtering of the DADA2 (Divisive Amplicon Denoising Algorithm 2) plugin inside QIIME2. Default parameters were used with the exception of the forward and reverse sequence length (--p-trunc-len-f and --p-trunc-len-r), that were set to 220 and 180, respectively. Processed reads that passed all these filters were used for taxonomy classification. The V4-V5 regions were extracted from the pre-formatted reference sequences and taxonomy file built on the SILVA 138 99% OTUs database and the vsearch v.2.6.2 global alignment implemented in QIIME2 was used.元条形码（Metabarcoding）分析：16S扩增子序列变体（Amplicon Sequence Variant, ASV）生成与分类学注释。 原始16S双端测序序列先进行数据质控，随后导入QIIME2 v2022.2.0分析流程。通过cutadapt工具去除序列中的残留引物与接头序列。 代表各样本真实生物学序列的扩增子序列变体表，通过QIIME2内置的DADA2（分裂式扩增子去噪算法2，Divisive Amplicon Denoising Algorithm 2）插件的双端去噪方法生成，该方法涵盖序列截短、去噪、去重复、序列合并与嵌合体过滤步骤。本次分析仅调整了正向与反向序列长度参数（--p-trunc-len-f与--p-trunc-len-r），分别设置为220和180，其余参数均采用默认值。 通过所有过滤步骤的处理后读段将用于分类学分类。从基于SILVA 138 99% OTU数据库构建的预格式化参考序列与分类学文件中提取V4-V5高变区，并采用QIIME2中集成的vsearch v.2.6.2全局比对工具完成分类学注释。