BACKGROUND Nowadays, not only are single genomes commonly analyzed, but also metagenomes, which are sets of, DNA fragments (reads) derived from microbes living in a given environment. Metagenome analysis is aimed at extracting crucial information on the organisms that have left their traces in an investigated environmental sample.In this study we focus on the MetaSUB Forensics Challenge (organized within the CAMDA 2018 conference) which consists in predicting the geographical origin of metagenomic samples. Contrary to the existing methods for environmental classification that are based on taxonomic or functional classification, we rely on the similarity between a sample and the reference database computed at a reads level. RESULTS We report the results of our extensive experimental study to investigate the behavior of our method and its sensitivity to different parameters. In our tests, we have followed the protocol of the MetaSUB Challenge, which allowed us to compare the obtained results with the solutions based on taxonomic and functional classification. CONCLUSIONS The results reported in the paper indicate that our method is competitive with those based on taxonomic classification. Importantly, by measuring the similarity at the reads level, we avoid the necessity of using large databases with annotated gene sequences. Hence our main finding is that environmental classification of metagenomic data can be proceeded without using large databases required for taxonomic or functional classification. REVIEWERS This article was reviewed by Eran Elhaik, Alexandra Bettina Graf, Chengsheng Zhu, and Andre Kahles.

中文翻译：

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用于构建微生物组指纹的环境宏基因组分类。

背景技术如今，不仅单个基因组被普遍分析，而且宏基因组也被广泛分析，宏基因组是来自生活在给定环境中的微生物的DNA片段（读数）的集合。宏基因组分析旨在提取在所调查的环境样本中留下痕迹的生物体的关键信息。在这项研究中，我们重点关注 MetaSUB 取证挑战赛（在 CAMDA 2018 会议内组织），其中包括预测宏基因组样本的地理起源。与基于分类或功能分类的现有环境分类方法相反，我们依赖于在读取级别计算的样本和参考数据库之间的相似性。结果我们报告了我们广泛的实验研究的结果，以调查我们的方法的行为及其对不同参数的敏感性。在我们的测试中，我们遵循 MetaSUB 挑战赛的协议，这使我们能够将获得的结果与基于分类和功能分类的解决方案进行比较。结论 本文报告的结果表明我们的方法与基于分类学分类的方法相比具有竞争力。重要的是，通过测量读取水平的相似性，我们避免了使用带有注释基因序列的大型数据库的必要性。因此，我们的主要发现是，可以在不使用分类或功能分类所需的大型数据库的情况下进行宏基因组数据的环境分类。审稿人 本文由 Eran Elhaik、Alexandra Bettina Graf、Chengsheng Zhu 和 Andre Kahles 审阅。