宏基因组研究的生物信息学平台现状

由Handelsman et al(1998)提出的宏基因组(metagenome)泛指特定环境样品(例如:人类和动物的肠道、母乳、土壤、湖泊、冰川和海洋等环境)中微生物群落所有物种的基因组。宏基因组技术起源于环境微生物学研究,而新一代高通量测序技术使其广泛应用成为可能。与基因组学研究相类似,目前宏基因组学发展的瓶颈在于如何高效分析高通量测序产生的海量数据,因此,相关的生物信息学分析方法和平台是宏基因组学研究的关键。该文介绍了目前宏基因组研究领域中主要的生物信息学软件及工具;鉴于目前宏基因组研究所采用的"全基因组测序"(whole genome sequencing)和"扩增子测序"(amplicon sequencing)两大测序方法所获得的数据和相应分析方法有较大差异,文中分别对相应软件平台进行了介绍。     

微生物组数据分析方法与应用

高通量测序技术的发展衍生出一系列微生物组(microbiome)研究技术,如扩增子、宏基因组、宏转录组等,快速推动了微生物组领域的发展。微生物组数据分析涉及的基础知识、软件和数据库较多,对于同领域研究者开展学习和选择合适的分析方法具有一定困难。本文系统概述了微生物组数据分析的基本思想和基础知识,详细总结比较了扩增子和宏基因组分析中的常用软件和数据库,并对高通量数据下游分析中常用的几种方法,包括统计和可视化、网络分析、进化分析、机器学习和关联分析等,从可用性、软件选择以及应用等几个方面进行了概述。本文拟通过对当前微生物组主流分析方法的整理和总结,为同领域研究者更方便、灵活的开展数据分析,快速选择研究分析工具,高效挖掘数据背后的生物学意义提供参考,进一步推动微生物组研究在生物学领域的发展。     

基于16S rRNA基因测序分析微生物群落多样性

微生物群落多样性的研究对于挖掘微生物资源,探索微生物群落功能,阐明微生物群落与生境间的关系具有重要意义。随着宏基因组概念的提出以及测序技术的快速发展,16S rRNA基因测序在微生物群落多样性的研究中已被广泛应用。文中系统地介绍了16S rRNA基因测序分析流程中的四个重要环节,包括测序平台与扩增区的选择、测序数据预处理以及多样性分析方法,就其面临的问题与挑战进行了探讨并对未来的研究方向进行了展望,以期为微生物群落多样性相关研究提供参考。     

宏基因组样本数据的分析比较与分类

宏基因组学研究试图通过测序并分析微生物群落的DNA序列,以理解环境微生物的组成及其与环境的交互作用。宏基因组学革命性地改变了微生物学,使得以免培养的方式研究复杂生物系统中的微生物群落成为可能。第二代测序技术的不断进步和生物信息学的高速发展促进了高通量宏基因组研究的发展,大批高质量的宏基因组数据不断产生并对科学界开放,宏基因组学的重要作用被科学界广泛认可。与此同时,对应个体不同健康状态和人体不同部位的大量宏基因组样本数据不断产生,使得比较和分类宏基因组样本在微生物学研究上变得更加重要,比较宏基因组学成为宏基因组学的重要分支。主要介绍了宏基因组数据的分析比较,以及样本分类的相关研究和算法。     

人体肠道宏基因组生物信息分析方法

最近5年来,微生物组与人体健康之间的微妙关系已成为全球研究热点,特别是基于高通量测序的宏基因组技术推动了这个领域的发展。然而宏基因组生物信息学分析往往是开展研究过程中的难点。本文对宏基因组生物信息常规分析方法进行了介绍。     

宏基因组学技术在微生物功能酶开发中的应用

宏基因组学技术以特定环境样品中微生物复杂群落的基因组总和为研究对象,突破了传统微生物纯培养方法的局限,为不可培养微生物中丰富的基因资源的开发和利用提供了强有力的工具,已经取得了令人瞩目的研究进展。对宏基因组学技术及其在微生物功能酶新基因发现中的应用进行综述。     

宏基因组方法揭示草地土壤微生物群落响应全球变化

草地在生物圈中发挥着重要的生态服务功能,而草地土壤微生物又是维持生态系统功能和稳定的关键要素之一。过去十几年间,宏基因组方法的进步为微生物群落分析提供了有力的工具。本文综述了宏基因组方法应用于草地土壤微生物群落响应全球变化的最新研究进展,特别是针对气候变化、大气组成变化、土地利用方式改变和外来物种入侵等条件下微生物群落的响应规律和反馈机制的研究。这些研究对于我们认识和了解微生物群落的生态功能十分重要,同时也对维持地球生态系统平衡具有积极的意义。最后,我们对未来应用宏基因组方法研究草地微生物群落进行了展望。     

阴道微生态宏基因组学研究进展

女性阴道内寄居着多种微生物群落,这些微生物种群间的平衡状态与妇女阴道疾病的发生密切相关。鉴定女性阴道菌群结构多样性的特征,有助于了解其在阴道疾病发生和转归中所发挥的作用。目前基于16SrRNA的聚合酶链式反应(PCR)及宏基因组相关技术在微生物群落研究中被广泛运用,这不仅可以帮助人们最大程度地获得阴道菌群的宏基因组信息,还可有效弥补单纯微生物培养法所产生的实验数据不充足等弊端。本文对阴道微生物菌群多样性的研究中应用的宏基因组学技术如基因测序、变性/温度梯度凝胶电泳(DGGE/TGGE)、分子克隆、末端限制性酶切长度多态性(T-RFLP)等进行综述。     

宏基因组学及其在口腔微生物研究中的应用

宏基因组是指环境中所有微生物的遗传物质总和。宏基因组学技术可以最大限度地利用环境中的微生物资源,受到了国内外微生物研究者的重点关注。口腔中寄居着大量的微生物群落,以往对口腔疾病微生物的研究大多局限于单纯的细菌培养技术,然而,由于培养技术的局限性,部分微生物很难或根本不能培养,宏基因组学技术打破了这一局限性,帮助人类发掘更丰富的口腔微生物资源。最近,以宏基因组学测序为基础的研究描绘出了口腔生态系统的图谱,越来越多的实验证明口腔微生物组在各种口腔疾病甚至全身系统性疾病中的重要作用。同时,这也为基于人类微生物组的诊断和治疗开辟了新的途径。本综述旨在说明宏基因组学是研究人类口腔疾病及全身疾病相关微生物的得力工具,而且具有广阔的发展前景,同时也讨论了宏基因组学在应用中有待克服的局限性。     

宏基因组的生物信息分析

基于高通量测序的宏基因组学研究是近年来的研究热点之一。宏基因组的生物信息分析正在逐渐完善成熟．各种分析软件和流程的开发与应用,极大地促进了宏基因组研究的发展,特别是在遗传与进化、基因发现、宏基因组和人类疾病的相关研究等方面取得了显著成果。本文旨在结合宏基因组学的研究内容和研究方向,对宏基因组的生物信息分析方法进行综述,探讨宏基因组的生物信息分析面临的机遇和挑战。     

16S rRNA基因在微生物生态学中的应用

16S rRNA(Small subunit ribosomal RNA)基因是对原核微生物进行系统进化分类研究时最常用的分子标志物(Biomarker),广泛应用于微生物生态学研究中。近些年来随着高通量测序技术及数据分析方法等的不断进步,大量基于16S rRNA基因的研究使得微生物生态学得到了快速发展,然而使用16S rRNA基因作为分子标志物时也存在诸多问题,比如水平基因转移、多拷贝的异质性、基因扩增效率的差异、数据分析方法的选择等,这些问题影响了微生物群落组成和多样性分析时的准确性。对当前使用16S rRNA基因分析微生物群落组成和多样性的进展情况做一总结,重点讨论当前存在的主要问题以及各种分析方法的发展,尤其是与高通量测序技术有关的实验和数据处理问题。     

Environmental microbiology through the lens of high-throughput DNA sequencing: Synopsis of current platforms and bioinformatics approaches

The incursion of High-Throughput Sequencing (HTS) in environmental microbiology brings unique opportunities and challenges. HTS now allows a high-resolution exploration of the vast taxonomic and metabolic diversity present in the microbial world, which can provide an exceptional insight on global ecosystem functioning, ecological processes and evolution. This exploration has also economic potential, as we will have access to the evolutionary innovation present in microbial metabolisms, which could be used for biotechnological development. HTS is also challenging the research community, and the current bottleneck is present in the data analysis side. At the moment, researchers are in a sequence data deluge, with sequencing throughput advancing faster than the computer power needed for data analysis. However, new tools and approaches are being developed constantly and the whole process could be depicted as a fast co-evolution between sequencing technology, informatics and microbiologists. In this work, we examine the most popular and recently commercialized HTS platforms as well as bioinformatics methods for data handling and analysis used in microbial metagenomics. This non-exhaustive review is intended to serve as a broad state-of-the-art guide to researchers expanding into this rapidly evolving field.     

Statistical methods for characterizing diversity of microbial communities by analysis of terminal restriction fragment length polymorphisms of 16S rRNA genes

The analysis of terminal restriction fragment length polymorphisms (T-RFLP) of 16S rRNA genes has proven to be a facile means to compare microbial communities and presumptively identify abundant members. The method provides data that can be used to compare different communities based on similarity or distance measures. Once communities have been clustered into groups, clone libraries can be prepared from sample(s) that are representative of each group in order to determine the phylogeny of the numerically abundant populations in a community. In this paper methods are introduced for the statistical analysis of T-RFLP data that include objective methods for (i) determining a baseline so that 'true' peaks in electropherograms can be identified; (ii) a means to compare electropherograms and bin fragments of similar size; (iii) clustering algorithms that can be used to identify communities that are similar to one another; and (iv) a means to select samples that are representative of a cluster that can be used to construct 16S rRNA gene clone libraries. The methods for data analysis were tested using simulated data with assumptions and parameters that corresponded to actual data. The simulation results demonstrated the usefulness of these methods in their ability to recover the true microbial community structure generated under the assumptions made. Software for implementing these methods is available at http://www.ibest.uidaho.edu/tools/trflp_stats/index.php.     

Application of Meta-Mesh on the analysis of microbial communities from human associated-habitats

With the current fast accumulation of microbial community samples and related metagenomic sequencing data, data integration and analysis system is urgently needed for in-depth analysis of large number of metagenomic samples (also referred to as “microbial communities”) of interest. Although several existing databases have collected a large number of metagenomic samples, they mostly serve as data repositories with crude annotations, and offer limited functionality for analysis. Moreover, the few available tools for comparative analysis in the literature could only support the comparison of a few pre-defined set of metagenomic samples. To facilitate comprehensive comparative analysis on large amount of diverse microbial community samples, we have designed a Meta-Mesh system for a variety of analyses including quantitative analysis of similarities among microbial communities and computation of the correlation between the meta-information of these samples. We have used Meta-Mesh for systematically and efficiently analyses on diverse sets of human associate-habitat microbial community samples. Results have shown that Meta-Mesh could serve well as an efficient data analysis platform for discovery of clusters, biomarker and other valuable biological information from a large pool of human microbial samples.     

Advancing analytical algorithms and pipelines for billions of microbial sequences

The vast number of microbial sequences resulting from sequencing efforts using new technologies require us to re-assess currently available analysis methodologies and tools. Here we describe trends in the development and distribution of software for analyzing microbial sequence data. We then focus on one widely used set of methods, dimensionality reduction techniques, which allow users to summarize and compare these vast datasets. We conclude by emphasizing the utility of formal software engineering methods for the development of computational biology tools, and the need for new algorithms for comparing microbial communities. Such large-scale comparisons will allow us to fulfill the dream of rapid integration and comparison of microbial sequence data sets, in a replicable analytical environment, in order to describe the microbial world we inhabit.     

Phred/Phrap/Consed/Polyphred:类Unix平台的测序数据管理和SNP识别软件包

尽管二代基因组测序技术日渐流行,Sanger测序依旧是SNP识别和分析的金标准。传统对于Sanger测序结果的分析多依赖Seq Man等软件进行。然而这类软件大多依靠人工操作来识别和记录测序结果中的SNP位点,效率低下且容易发生错误。此外,当对多个个体进行序列测定时,这类软件无法完成对群体数据的管理和输出,给研究人员造成了一定的不便。Phred/Phrap/Consed/Polyphred是华盛顿大学开发的基于类Unix平台的软件包,在大规模测序数据的管理和SNP自动识别、标记与输出方面具有强大的功能。然而,由于其安装和使用较为复杂,在国内较少使用。本研究对该软件包的功能、使用流程、特点等进行了介绍,并将其安装于Ubuntu12.04操作系统并置于VMware虚拟机中,方便遗传学者的下载和使用。     

Ecological inference on bacterial succession using curve-based community fingerprint data analysis, demonstrated with rhizoremediation experiment

Nucleic acid-based community fingerprinting methods are valuable tools in microbial ecology, as they offer rapid and robust means to compare large series of replicates and references. To avoid the time-consuming and potentially subjective procedures of peak-based examination, we assessed the possibility to apply direct curve-based data analysis on community fingerprints produced with bacterial length heterogeneity PCR (LH-PCR). The dataset comprised 180 profiles from a 21-week rhizoremediation greenhouse experiment with three treatments and 10 sampling times. Curve-based analysis quantified the progressive effect of the plant (Galega orientalis) and the reversible effect of the contaminant (fuel oil) on bacterial succession. The major observed community shifts were assigned to changes in plant biomass and contamination level by canonical correlation analysis. A novel method to extract relative abundance data from the fingerprint curves for Shannon diversity index revealed contamination to reversibly decrease community complexity. By cloning and sequencing the fragment lengths, recognized to change in time in the averaged LH-PCR profiles, we identified Aquabacterium (Betaproteobacteria) as the putative r-strategic fuel oil degrader, and K-strategic Alphaproteobacteria growing in abundance later in succession. Curve-based community fingerprint analysis can be used for rapid data prescreening or as a robust alternative for the more heavily parameterized peak-based analysis.     

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Background

Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.

Results

Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16?S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).

Conclusions

The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.     

基于分子标记的宏基因组16S rRNA基因高变区选择策略

随着新一代DNA测序技术出现,人们能够同时对多个DNA样本的宏基因组进行并行分析,尤其是以16S rRNA基因高变区为分子标记的测序已经成为微生物多样性研究最为简洁有效的方法. 目前二代高通量测序的读长不能覆盖16S rRNA基因的全长,需要选择一个有效的高变区进行测序.十多年来,对于16S rRNA基因高变区的选择策略没有统一的标准.本文分析了常用的高变区选择策略,指出不同环境条件是影响高变区选择的重要因素之一.在此基础上,提出了高变区选择的参考准则,同时建议应对选择的高变区进行有效评估.     

Sparse and Compositionally Robust Inference of Microbial Ecological Networks

16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.