Supplementary Tables and Figures

Supplementary Tables and Figures for the following study:https://doi.org/10.1101/2021.03.03.433801Figure S1. Taxonomic relative abundance at the genus level with ribosomal protein S7 and S2. We used the GTDB database to assign taxonomy to the two most abundant ribosomal proteins (S7 and S2) identified in long-read metagenomes using HMMs. For the short-read metagenomes, we used the taxonomy of the ribosomal gene S7 with the Genome Taxonomy Database (GTDB). We performed a 16S rRNA amplicon sequencing on an additional sample (Amplicon) and used MED with Silva (v132) assignation at the genus level. Genera representing less than 1% of a sample were pooled as rare (light grey).Table S1. Read counts and cumulative length. (a) Number of sequences (and the cumulative length) of the MinION outputs, before and after quality filtering. We used a qscore of 7 as the quality filtering threshold. (b) Microbial read size distribution metrics. All percentages are relative to the total amount of reads (or total length) of the sequencing output, before filtering out human contamination, but after quality filtering (criteria: minimum Q-score of 7). (c) Impact of size selection on read-size distribution.Table S2. Oligotypes identified by Minimum Entropy Decomposition (MED). (a) Matrix count, (b) representative sequences and (c) associated Silva taxonomy.Table S3. Distribution of ribosomal proteins across assembled long-reads across extraction strategies.

本研究的补充表格与图表如下：https://doi.org/10.1101/2021.03.03.433801 图S1. 在属水平上基于核糖体蛋白S7和S2的物种丰度分类。本研究采用GTDB数据库，对通过隐马尔可夫模型（HMMs）在长读长宏基因组中识别出的两种最丰富的核糖体蛋白（S7和S2）进行物种分类。对于短读长宏基因组，我们采用了核糖体基因S7的物种分类，并与基因组分类数据库（GTDB）相结合。此外，对额外样本（Amplicon）进行了16S rRNA扩增子测序，并在属水平上使用MED与Silva（v132）进行分类。低于样本总丰度1%的属被视为稀有物种（以浅灰色表示）。表S1. 读段计数及累积长度。（a）MinION输出序列数（及累积长度）在质量过滤前后的变化。质量过滤的阈值设置为qscore 7。（b）微生物读段大小分布的计量指标。所有百分比均相对于测序输出中过滤掉人类污染前的总读段数（或总长度），但经过质量过滤（标准：最小Q-score为7）。（c）尺寸选择对读段大小分布的影响。表S2. 通过最小熵分解（MED）识别的寡核苷酸型。（a）矩阵计数，（b）代表性序列，（c）相关Silva分类。表S3. 在不同提取策略下组装的长读长中核糖体蛋白的分布。