FeGenie: A Comprehensive Tool for the Identification of Iron Genes and Iron Gene Neighborhoods in Genome and Metagenome Assemblies
FeGenie: A Comprehensive Tool for the Identification of Iron Genes and Iron Gene Neighborhoods in Genome and Metagenome Assemblies
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DOI：
10.3389/fmicb.2020.00037
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发表时间：
2020-01-31
影响因子：
5.2
通讯作者：
Merino, Nancy
中科院分区：
文献类型：
--
作者：
Garber, Arkadiy I.;Nealson, Kenneth H.;Merino, Nancy
铁是地球上几乎所有生命的微量营养素。它可以被铁氧化和铁还原微生物用作电子供体和电子受体，并用于各种生物过程，包括光合作用和呼吸作用。虽然它是地壳中第四丰富的金属，但铁通常限制在氧化环境中的生长，因为它很容易氧化和沉淀。我们对微生物如何竞争和利用铁的理解大部分是基于实验室实验。然而，下一代测序的出现和公开序列数据的激增使得探测环境中微生物群落的结构和功能成为可能。为了弥合我们对铁的获取，铁的氧化还原循环，铁的储存和模型微生物中磁小体形成的理解与环境研究中大量的序列数据之间的差距，我们创建了一个基于细菌和细菌中与铁的获取，储存和还原/氧化相关的基因的隐马尔可夫模型（HMRM）的综合数据库。沿着这个数据库，我们提出了FeGenie，一个生物信息学工具，接受基因组和宏基因组组件作为输入，并使用我们全面的HMM数据库来注释所提供的数据集与铁相关的基因和基因邻域。该工具的一个重要贡献是有效识别参与铁氧化和异化铁还原的基因，这些基因在很大程度上被标准注释管道所忽视。我们验证了FeGenie对一组选定的28个分离基因组，并展示了其在探索铁基因中的实用性，铁基因存在于27个宏基因组中，4个分离基因组来自人类口腔生物膜，17个基因组来自候选生物体，包括候选门辐射的成员。我们表明，FeGenie准确地识别分离株中的铁基因。此外，使用FeGenie对宏基因组的分析表明，每个环境的铁基因库和丰度与铁丰富度相关。虽然该工具不会取代微生物生理学的培养依赖性分析的可靠性，但它提供了来自最新遗传标记的可靠预测。FeGenie的数据库将得到维护，并随着新基因的发现而不断更新。FeGenie是免费提供的：.
Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth's crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie's database will be maintained and continually updated as new genes are discovered. FeGenie is freely available: .