Beyond taxonomic identification: integration of ecological responses to a soil bacterial 16S rRNA gene database
Beyond taxonomic identification: integration of ecological responses to a soil bacterial 16S rRNA gene database
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超越分类学鉴定：将生态反应整合到土壤细菌 16S rRNA 基因数据库
DOI：
10.1101/843847
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Jones B
中科院分区：
文献类型：
--
作者：
Jones B
高通量测序16 S rRNA基因调查使人们对土壤细菌多样性有了新的认识，并进一步了解了不同景观中丰度的生态驱动因素。然而，目前的分析方法是有限的使用，在正式合成的生态属性的分类群发现，因为衍生的分类单位通常是独特的个别研究和序列识别数据库的特征分类。为了解决这个问题，我们使用从一个大型的全国性土壤调查（GB农村调查，以下简称CS）获得的序列创建一个全面的土壤特定的16 S参考数据库，与耦合的生态信息来自调查元数据。具体而言，我们建模的分类响应土壤pH值的OTU水平使用分层逻辑回归（霍夫）模型，提供信息的形状景观规模的pH值-丰度的响应，pH值最佳（pH值时，OTU丰度是最大的）。我们确定，大多数的土壤OTU检查表现出非平坦的土壤pH值的关系。此外，pH值的最佳值不能概括广泛的分类，突出需要的工具和数据库综合生态性状在更精细的分类分辨率。我们进一步证明了该数据库的实用性，通过对地理上分散的查询16 S数据集进行测试;通过量化匹配来评估效率，以及从单独的大型土壤调查中预测查询序列的pH响应的准确性。我们发现，CS数据库提供了良好的覆盖率的优势类群，并指示土壤pH值的查询数据集的类群对应的pH值分类的CS数据库中的顶级匹配。此外，我们能够预测查询数据集的社区结构，使用基于查询土壤pH值数据和匹配的CS数据库类群的霍夫模型的优势类群的预测丰度。将具有相关霍夫模型输出的数据库发布为用于查询单个感兴趣序列的在线门户（https：//shiny-apps.ceh.ac.uk/ID-TaxER/），并且平面文件可用于生物信息学管道。先进的信息学基础设施的进一步发展，结合模拟的生态属性沿着与新的功能基因组信息可能会促进大规模的探索和预测土壤微生物功能生物多样性在当前和未来的环境变化情景。
High-throughput sequencing 16S rRNA gene surveys have enabled new insights into the diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances across landscapes. However, current analytical approaches are of limited use in formalizing syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterize taxonomy. To address this, we used sequences obtained from a large nationwide soil survey (GB Countryside Survey, henceforth CS) to create a comprehensive soil specific 16S reference database, with coupled ecological information derived from survey metadata. Specifically, we modeled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on both the shape of landscape scale pH-abundance responses, and pH optima (pH at which OTU abundance is maximal). We identify that most of the soil OTUs examined exhibited a non-flat relationship with soil pH. Further, the pH optima could not be generalized by broad taxonomy, highlighting the need for tools and databases synthesizing ecological traits at finer taxonomic resolution. We further demonstrate the utility of the database by testing against geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and accuracy in predicting pH responses of query sequences from a separate large soil survey. We found that the CS database provided good coverage of dominant taxa; and that the taxa indicating soil pH in a query dataset corresponded with the pH classifications of top matches in the CS database. Furthermore we were able to predict query dataset community structure, using predicted abundances of dominant taxa based on query soil pH data and the HOF models of matched CS database taxa. The database with associated HOF model outputs is released as an online portal for querying single sequences of interest (https://shiny-apps.ceh.ac.uk/ID-TaxER/), and flat files are made available for use in bioinformatic pipelines. The further development of advanced informatics infrastructures incorporating modeled ecological attributes along with new functional genomic information will likely facilitate large scale exploration and prediction of soil microbial functional biodiversity under current and future environmental change scenarios.