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。     

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。     

环境微生物学的组学技术应用和突破（英文稿）

由于研究环境变化和微生物群落的需要,近年来高通量组学技术得到了迅猛开发和应用.其中,基于测序和芯片技术的宏基因组学是一个关键的、最成熟的组学技术,为大多数的其它组学技术提供了支撑.相比较而言,宏转录组学、宏蛋白质组学和宏代谢组学也取得了少数的有限成功,但已经显示出可喜的潜力.所有的组学技术都有赖于生物信息学,使得后者成为组学技术应用的一个主要的技术瓶颈.这些新的组学技术对环境微生物学领域产生了革命性的影响,极大地丰富了我们对于环境微生物基因资源和功能活性的了解.     

宏基因组学技术在痤疮研究中的应用进展

高通量测序技术的发展提高了人们对微生物组的认识。宏基因组学技术因其全面和深入的分析功能被广泛应用于各种环境微生物组的研究中,尤其在阐明各种疾病与人体微生物组的关系中,宏基因组学技术具有重要作用。痤疮作为一种常见的皮肤疾病,严重影响人们皮肤美观度和心理健康。利用宏基因组学技术挖掘皮肤微生物与痤疮的关系,将有助于痤疮发病机理的研究和临床治疗方法的改进。通过介绍宏基因组学技术的发展背景、概述及其应用研究进展,探讨皮肤微生物与痤疮的关系,综述宏基因组学技术在痤疮研究中的应用现状,并总结目前宏基因组学技术在皮肤疾病研究中存在的问题,旨在为痤疮的宏基因组研究提供参考。     

环境耐药组及其健康风险的宏基因组学研究策略和方法

抗生素耐药性在环境中的发展和传播对人体健康造成潜在风险。随着高通量测序技术和生物信息学方法的不断发展,宏基因组学技术被广泛应用于不同环境样本的抗生素耐药组研究。本文介绍了两种针对环境耐药组筛查的宏基因组学分析方法,总结了当前主流的生物信息学软件和数据库,并阐述了环境耐药组的风险评估框架和基于宏基因组学技术的相关实践,以期为环境耐药组的监测、风险评估和管控提供可行的路线图。     

环境微生物的宏基因组学研究新进展

宏基因组学以环境中微生物的基因组的总和为研究对象,从而规避了传统方法中绝大部分微生物不能培养的缺陷,因此近年来在环境微生物学研究中得到了广泛应用.本文重点介绍了宏基因组学技术中关键的两类技术:即以罗氏454及Illumina为代表的高通量测序技术和以基因芯片(GeoChip)为代表的基因芯片技术在微生物研究中的应用.测序技术可以发现新物种和新基因,但由于测序深度有限,定量性差,不易发现低丰度物种,且易受污染物干扰.芯片技术很好地克服了这些局限,但不易于发现新基因.本文介绍了这些技术近年来在气候变化、水处理工程系统、极端环境、人体肠道、石油污染修复、生物冶金等方面取得的部分代表性成果.在此基础上,对宏基因组技术在环境微生物研究方面的未来发展方向提出了预判和展望.我们认为由于两种技术各自的优缺点,今后将两类技术结合起来的综合研究会越来越多.另外,由于大量数据的处理方法已成为制约宏基因组学发展的瓶颈,相应的生物信息学技术开发将是未来科研的热点和难点.     

环境微生物宏基因组学数据库利用

宏基因组学技术产生的数据是研究环境微生物的宝贵资源,国际上已有微生物计划、海洋计划、生命普查等大项目,采集和测序的样本量数以百万计,产生了海量的环境宏基因组学数据,并以此建立了几十个相关宏基因组数据库和平台。主要从以下几个方面综述环境宏基因组学的研究进展和已有资源:环境宏基因组学国际合作大项目、宏基因组学数据库和宏基因组学数据在线分析平台。将结合相应的数据库网站介绍其项目详情、样本来源、数据类型、使用方式和分析结果等,以便研究者全面了解此类数据并能快速找到和利用相关资源。     

昆虫肠道的宏基因组学:微生物大数据的新疆界

微生物作为自然界中普遍存在的生命体,通常以"微生物群落"的形式共存。这些物种相互协作适应环境变化的同时,也对环境产生了长期而深刻的影响。随着人类对于微生物了解的深入,微生物群落基础研究及其在健康和环境等领域的应用研究日益重要。昆虫肠道内存在种类繁多、数量庞大的微生物,一方面,这些肠道微生物种群结构的多样性与昆虫种类、龄期、消化道形式、食物的来源、环境等都息息相关。另一方面,这些菌群也对宿主的一些生理活动有着一定的影响。随着高通量测序技术、组学技术的发展,昆虫肠道宏基因组大数据挖掘和应用已经成为研究热点,极大地推动人类微生物资源利用的能力。本文概述了昆虫肠道微生物宏基因组学的发展现状和发展趋势,特别是肠道宏基因组学大数据的挖掘工具和应用,以及现阶段昆虫肠道宏基因组学的研究进展、应用、优势和瓶颈,并对今后昆虫肠道微生物组大数据研究方向进行展望。     

宏基因组学研究方法及其在病原体检测中的应用

在自然环境中存在着大量的微生物。这些微生物种群,尤其是许多新发性病毒,会对人和动物体的健康产生重大影响,而它们大多数都是不可培养的微生物。宏基因组学(metagenomics)的研究对象主要是非培养微生物。在特定环境中,它可有效地获取微生物种群的遗传组成、群落结构及生态作用等信息。随着高通量测序技术的快速发展,宏基因组学分析方法也已经应用到病毒学领域中,出现了病毒宏基因组学(viral metagenomics)。这一技术在新病毒鉴定等方面都取得了很多重要的研究成果。该综述主要对宏基因组学的发展历史、应用前景、生物信息学分析流程,及其在病毒鉴定中的应用进行简要介绍。     

活性污泥微生物群落宏组学研究进展

活性污泥是全球最常用的废水生物处理人工生态系统,微生物是驱动其污染净化能力的关键。活性污泥微生物群落所有物种与基因(简称"微生物组")的研究先后经历了"显微镜观察和纯菌培养分离"(1915)、"PCR扩增-测序"(1994)和"高通量测序-宏组学分析"(2006)三个重要阶段的发展变迁。相应地,我们对活性污泥微生物组的认知经历了从最早对微型动物(如钟虫和轮虫)及其他微生物的形貌观察和纯种培养鉴定到今天对整个微生物组的全局多样性认识的飞跃。近13年来,基于高通量测序的宏组学方法被广泛应用于揭示活性污泥微生物群落组成结构和功能,我们现在充分意识到活性污泥微生物组蕴藏着大量不可培养新物种和基因多样性,驱动着各类污染物的降解与转化。目前,特异性分子标记基因的扩增子测序技术已经被广泛应用于揭示城市和工业废水处理活性污泥微生物组和典型功能种群(如硝化细菌和聚磷菌)的时空多样性和群落构建机制,进而为未来实现活性污泥微生物组功能的精准调控奠定理论基础。宏基因组学研究在群落、种群和个体基因组水平全面解析了活性污泥微生物组驱动的碳、氮、磷元素循环过程,以及有机微污染物的生物降解和转化机理。将来活性污泥微生物组学研究需要在"标准化的组学分析方法和绝对定量""高通量培养组学""高通量功能基因组学"和"多组学方法的结合及多种方法并用"4个方面取得实现精准生态基因组学所需的技术突破,以最大限度发掘活性污泥微生物组在污水处理与资源回收领域的生态学与工程学价值。     

Web Resources for Metagenomics Studies

The development of next-generation sequencing(NGS) platforms spawned an enormous volume of data. This explosion in data has unearthed new scalability challenges for existing bioinformatics tools. The analysis of metagenomic sequences using bioinformatics pipelines is complicated by the substantial complexity of these data. In this article, we review several commonly-used online tools for metagenomics data analysis with respect to their quality and detail of analysis using simulated metagenomics data. There are at least a dozen such software tools presently available in the public domain. Among them, MGRAST, IMG/M, and METAVIR are the most well-known tools according to the number of citations by peer-reviewed scientific media up to mid-2015. Here, we describe 12 online tools with respect to their web link, annotation pipelines, clustering methods, online user support, and availability of data storage. We have also done the rating for each tool to screen more potential and preferential tools and evaluated five best tools using synthetic metagenome. The article comprehensively deals with the contemporary problems and the prospects of metagenomics from a bioinformatics viewpoint.     

微生物组大数据管理与分析

高通量技术的迅猛发展促使微生物生态学研究获得了重大突破,掀起了元基因组学(Metagenomics)研究的热潮。元基因组学通常被定义为对未培养的环境样本中微生物群体的DNA序列分析。随着微生物组学数据的日益剧增,微生物大数据的高效管理与分析越来越受到研究者的关注。如何从海量的微生物组数据中挖掘出具有科研价值的数据信息并应用于实际问题成为当前的研究热点。目前已有很多计算生物学程序工具及数据库用于元基因组数据的分析与管理。本文主要综述了随着高通量测序技术的进步,国际上主要的微生物组计划及微生物组数据平台,如人类微生物组项目(human microbiome project,HMP)、地球微生物组项目(earth microbiome project,EMP)、欧盟的肠道微生物组计划(metagenomics of human intestinal tract,MetaHIT)、MG-RAST、i Microbe、整合微生物组(integration microbial genomes,IMG)以及EBI Metagenomics等;介绍了微生物数据分析的主要流程与工具;提出了建设多源异构的微生物生态数据管理与分析系统的必要性。     

From genomics to metagenomics

Next-generation sequencing has changed metagenomics. However, sequencing DNA is no longer the bottleneck, rather, the bottleneck is computational analysis and also interpretation. Computational cost is the obvious issue, as is tool limitations, considering most of the tools we routinely use have been built for clonal genomics or are being adapted to microbial communities. The current trend in metagenomics analysis is toward reducing computational costs through improved algorithms and through analysis strategies. Data sharing and interoperability between tools are critical, since computation for metagenomic datasets is very high.     

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。     

Functional metagenomics: a tool to gain knowledge for agronomic and veterinary sciences

The increased global demand for food production has motivated agroindustries to increase their own levels of production. Scientific efforts have contributed to improving these production systems, aiding to solve problems and establishing novel conceptual views and sustainable alternatives to cope with the increasing demand. Although microorganisms are key players in biological systems and may drive certain desired responses toward food production, little is known about the microbial communities that constitute the microbiomes associated with agricultural and veterinary activities. Understanding the diversity, structure and in situ interactions of microbes, together with how these interactions occur within microbial communities and with respect to their environments (including hosts), constitutes a major challenge with an enormous relevance for agriculture and biotechnology. The emergence of high-throughput sequencing technologies, together with novel and more accessible bioinformatics tools, has allowed researchers to learn more about the functional potential and functional activity of these microbial communities. These tools constitute a relevant approach for understanding the metabolic processes that can occur or are currently occurring in a given system and for implementing novel strategies focused on solving production problems or improving sustainability. Several ‘omics’ sciences and their applications in agriculture are discussed in this review, and the usage of functional metagenomics is proposed to achieve substantial advances for food agroindustries and veterinary sciences.     

Bioinformatic Approaches Reveal Metagenomic Characterization of Soil Microbial Community

As is well known, soil is a complex ecosystem harboring the most prokaryotic biodiversity on the Earth. In recent years, the advent of high-throughput sequencing techniques has greatly facilitated the progress of soil ecological studies. However, how to effectively understand the underlying biological features of large-scale sequencing data is a new challenge. In the present study, we used 33 publicly available metagenomes from diverse soil sites (i.e. grassland, forest soil, desert, Arctic soil, and mangrove sediment) and integrated some state-of-the-art computational tools to explore the phylogenetic and functional characterizations of the microbial communities in soil. Microbial composition and metabolic potential in soils were comprehensively illustrated at the metagenomic level. A spectrum of metagenomic biomarkers containing 46 taxa and 33 metabolic modules were detected to be significantly differential that could be used as indicators to distinguish at least one of five soil communities. The co-occurrence associations between complex microbial compositions and functions were inferred by network-based approaches. Our results together with the established bioinformatic pipelines should provide a foundation for future research into the relation between soil biodiversity and ecosystem function.     

宏基因组学与动物病原微生物检测

宏基因组学（ metagenome）是直接从土壤、海水、人及动物胃肠道、口腔、呼吸道、皮肤等环境中获取样品DNA,利用载体将其克隆到替代宿主细胞中构建宏基因文库,以高通量检测为主要技术来研究特定环境中全部微生物的基因组及筛选活性物质和基因的新兴学科。利用宏基因组学技术不仅能够有效地检测特定环境的微生物群落结构,扩展了微生物资源的利用空间,发展了新兴的高通量检测技术,丰富了生物信息学内容。基于宏基因组学研究方法在环境微生物研究中的优势,对近年来相关领域、方法及其在人及动物病原微生物研究中的应用进行综述,以期将此方法用于实验动物病原微生物的调查分析及动物疫情、生物安全的监测。     

Quality control of microbiota metagenomics by k-mer analysis

Background

The biological and clinical consequences of the tight interactions between host and microbiota are rapidly being unraveled by next generation sequencing technologies and sophisticated bioinformatics, also referred to as microbiota metagenomics. The recent success of metagenomics has created a demand to rapidly apply the technology to large case–control cohort studies and to studies of microbiota from various habitats, including habitats relatively poor in microbes. It is therefore of foremost importance to enable a robust and rapid quality assessment of metagenomic data from samples that challenge present technological limits (sample numbers and size). Here we demonstrate that the distribution of overlapping k-mers of metagenome sequence data predicts sequence quality as defined by gene distribution and efficiency of sequence mapping to a reference gene catalogue.

Results

We used serial dilutions of gut microbiota metagenomic datasets to generate well-defined high to low quality metagenomes. We also analyzed a collection of 52 microbiota-derived metagenomes. We demonstrate that k-mer distributions of metagenomic sequence data identify sequence contaminations, such as sequences derived from “empty” ligation products. Of note, k-mer distributions were also able to predict the frequency of sequences mapping to a reference gene catalogue not only for the well-defined serial dilution datasets, but also for 52 human gut microbiota derived metagenomic datasets.

Conclusions

We propose that k-mer analysis of raw metagenome sequence reads should be implemented as a first quality assessment prior to more extensive bioinformatics analysis, such as sequence filtering and gene mapping. With the rising demand for metagenomic analysis of microbiota it is crucial to provide tools for rapid and efficient decision making. This will eventually lead to a faster turn-around time, improved analytical quality including sample quality metrics and a significant cost reduction. Finally, improved quality assessment will have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1406-7) contains supplementary material, which is available to authorized users.