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。     

口腔微生物与全身健康研究进展

口腔微生物是定植于人体口腔的微生物集合。众多研究证实,口腔微生物与多种口腔感染性疾病及系统性疾病紧密相关。随着“人类微生物组计划”及其他微生物宏基因组学相关项目的开展,人们对口腔微生物群落的认识不断深入。本文基于最新研究进展,就口腔微生物的组成、演替特点、与口腔和全身系统性疾病的关系及与肠道微生物的交互作用进行综述。     

微生物组学及其应用研究进展

随着高通量测序技术和生物信息学的发展,尤其是宏基因组在人类肠道微生物鉴定方面的应用,微生物组学应运而生。概述了微生物组的多样性及其在人体健康、农作物生长、畜牧业发展、环境治理、工业生物技术产品生产等方面的应用,并对微生物组学的研究方向和应用前景作了展望。     

基于高通量测序技术马肠道菌群研究进展

马肠道非常发达,其中定居着丰富又复杂的微生物菌群,这些微生物在宿主的生理、代谢、营养和免疫功能等方面有着重要作用.基于高通量测序的宏基因组学技术和分析手段的改进,对复杂环境中微生物的研究更加方便、透彻.本文就基于高通量测序的宏基因组技术在马肠道核心菌群、不同肠道段菌群结构、不同因素对肠道菌群结构的影响,以及马肠道微生物...     

肠道微生物测序研究中不同实验来源差异的综合评价以及应用

肠道微生物测序研究具有将微生物结果转化为人类健康的巨大潜力。16S扩增子测序和宏基因组鸟枪测序(whole-metagenome shotgun, WMS)是微生物组研究中的两大主要方法,各具优势。然而,研究样品的异质性、测序仪和文库制备方法的差异如何影响肠道微生物测序结果的可重复性仍有待深入研究。该研究旨在通过比较粪便样本中微生物组成的差异,为测序技术的选择提供参考标准。3种广泛采用的测序仪的结果显示,WMS法中技术重复相关性(r=0.94)较高,而生物学重复相关性(r=0.69)较低。Bray-Curtis距离表明,生物学重复的差异大于技术重复(P<0.001)。此外,16S和WMS数据集间具有明显的分类学图谱差异。研究结果表明,同质化是样品DNA提取前的一个必要步骤;测序仪对分类学变异的贡献小于文库制备方法。我们开发了经验贝叶斯方法,即在计算中“借用信息”,利用标准化数据和(非)参数先验分布分析批次效应参数,提高了16S和WMS之间的群体可比性,为进一步应用于融合分析已发表的16S和微生物数据集提供了依据。     

基于三代测序技术的微生物组学研究进展

微生物在人类生活中无处不在, 过去人们对微生物的认识仅停留在单菌培养和定性研究上, 而测序技术的发展极大地促进了微生物组学的研究。越来越多的证据表明: 人体共生微生物、特别是肠道微生物与人类健康息息相关。 二代测序技术凭借其高通量、高准确率和低成本的特点, 成为微生物组学研究中的主流测序技术。但是随着研究的深入, 二代测序技术的短读长(< 450 bp)增加了后续数据分析和基因组拼接难度, 也限制了该技术在未来研究中的应用。在此背景下, 第三代测序技术应运而生。第三代测序技术又称单分子测序, 能够直接对单个DNA分子进行实时测序, 而不需要经过PCR扩增。第三代测序技术的平均读长在2-10 kb左右, 最高可以达到2.2 Mb, 实现了长序列的高通量测序。凭借其超长的测序读长、无GC偏好性等优势, 三代测序技术为微生物基因组全长测序, 组装完整可靠的基因组提供了新的方法。本文在描述三代测序的技术特点和原理的基础上, 重点介绍了三代测序技术在微生物16S/18S rRNA基因测序、单菌的基因组组装以及宏基因组中的研究应用和进展。     

快速评价肠道微生物的qPCR方法的建立

【背景】近些年,16S rRNA基因测序与宏基因组分析常用于肠道微生物病原体检测。【目的】为了使检测不受限于高成本与耗时长的问题,基于荧光探针的实时荧光定量PCR(real-time fluorescence quantitative PCR, qPCR),建立一种评估人类肠道微生物群组成的平台用于检测肠道微生物丰度。【方法】从公共数据库筛选10种肠道中普遍存在的微生物分类群,使用20个粪便样本验证为10种靶标所设计的特异性引物与探针,最后通过比较qPCR方法和16SrRNA基因测序技术的检测结果来评估该平台的有效性。【结果】10对引物及其探针对靶标分类群具有特异性并且在HITdb数据库中靶向菌种的覆盖率超过70%;样本检测结果的变异系数(coefficient of variation,CV)小于10%,证明了该方法具有很高的稳定性;qPCR方法检测样本中物种的相对丰度与16S rRNA基因序列生物信息学分析结果大部分具有显著相关性(P<0.05)。【结论】本研究根据HITdb数据库设计的靶向微生物群的引物和探针检测到的粪便样本中微生物的相对丰度结果与16S rRNA基因测序结...     

微生物宏基因组数据分析方法研究进展

随着测序技术的迅速发展,人们对宏基因组的研究逐渐深入。通过宏基因组学对微生物群落的测序和分析,以理解微生物组成与环境之间的相互作用。微生物宏基因组的分析摆脱了传统研究中微生物分离培养的技术限制,并获得了微生物群落的相对丰度和群落的功能等信息。用于微生物数据分析的工具和软件较多,对于研究者选择合适的分析方法具有一定困难。概述了微生物宏基因组分析方法的流程,总结了分析中常用的工具及软件,为研究者快速筛选分析方法,揭示数据背后的生物学意义提供参考。     

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。     

宿主遗传因素对肠道菌群的影响

随着高通量测序技术的发展,人们逐渐认识到肠道菌群与人类的健康和疾病密切相关,并发现肠道菌群受很多因素的影响。除了研究传统饮食和药物对肠道菌群的改变外,近年来,科学家也开始注重遗传因素在塑造肠道菌群中的作用。遗传因素可决定宿主的饮食偏好、肠道的生理结构、肠道屏障功能和免疫功能等,而这些都直接与肠道菌群相互作用,参与肠道微生态平衡的构建和稳定。因此,在研究肠道菌群与疾病发生相关性的过程中也需要考虑遗传因素的重要性。随着基因敲除、无菌小鼠和菌群移植等实验技术的革新,以及主成分分析、数量性状基因座和全基因组关联性分析等大数据分析手段的提高,科学家能够深入研究宿主遗传基因与肠道菌群之间的关联性,从而证明宿主遗传基因在塑造肠道微生态的过程中具有重要作用。本文将首先简述肠道菌群与疾病发生之间可能存在的联系,然后从多方面综述遗传因素对肠道菌群的影响及主要的研究进展,从而为今后该领域的深入研究提供重要的指导,也为今后预防和治疗疾病提供新思路和新方法。     

MALINA: a web service for visual analytics of human gut microbiota whole-genome metagenomic reads

MALINA is a web service for bioinformatic analysis of whole-genome metagenomic data obtained from human gut microbiota sequencing. As input data, it accepts metagenomic reads of various sequencing technologies, including long reads (such as Sanger and 454 sequencing) and next-generation (including SOLiD and Illumina). It is the first metagenomic web service that is capable of processing SOLiD color-space reads, to authors’ knowledge. The web service allows phylogenetic and functional profiling of metagenomic samples using coverage depth resulting from the alignment of the reads to the catalogue of reference sequences which are built into the pipeline and contain prevalent microbial genomes and genes of human gut microbiota. The obtained metagenomic composition vectors are processed by the statistical analysis and visualization module containing methods for clustering, dimension reduction and group comparison. Additionally, the MALINA database includes vectors of bacterial and functional composition for human gut microbiota samples from a large number of existing studies allowing their comparative analysis together with user samples, namely datasets from Russian Metagenome project, MetaHIT and Human Microbiome Project (downloaded fromhttp://hmpdacc.org). MALINA is made freely available on the web athttp://malina.metagenome.ru. The website is implemented in JavaScript (using Ext JS), Microsoft .NET Framework, MS SQL, Python, with all major browsers supported.     

人类肠道微生物组与相关疾病研究进展

人体肠道共生着数以万亿计的微生物,肠道微生物在维持宿主正常生理功能中发挥重要作用,其成分和功能变化可导致严重的肠道和全身性疾病。以新一代测序技术和生物信息学分析为基础的元基因组学研究不仅极大地推动了对人类肠道微生物的整体认识,还加深了对肠道微生物代谢产物促进人类健康机理的理解,为肠道炎症、代谢性疾病和癌症等人类疾病的诊断与治疗提供了新思路。就肠道微生物元基因组学与肠道相关疾病的研究进展作一综述。     

基于机器学习的肠道菌群数据建模与分析研究综述

人体肠道菌群与人类的健康和疾病存在密切关系,对肠道菌群的宏基因组数据进行建模和分析,在疾病预测及诊断相关领域科学研究和社会应用方面均具有重要意义。本文从大数据分析和机器学习的角度,对人体肠道菌群数据的建模、分析和预测算法的原理、过程以及典型研究应用实例进行综述,以期推动肠道菌群分析相关研究发展以及探索结合机器学习算法进行肠道菌群分析的有效方式,同时也为开发基于肠道菌群数据的新型诊疗手段提供借鉴,推动我国精准医疗事业发展。     

Progress of analytical tools and techniques for human gut microbiome research

Massive DNA sequencing studies have expanded our insights and understanding of the ecological and functional characteristics of the gut microbiome. Advanced sequencing technologies allow us to understand the close association of the gut microbiome with human health and critical illnesses. In the future, analyses of the gut microbiome will provide key information associating with human individual health, which will help provide personalized health care for diseases. Numerous molecular biological analysis tools have been rapidly developed and employed for the gut microbiome researches; however, methodological differences among researchers lead to inconsistent data, limiting extensive share of data. It is therefore very essential to standardize the current methodologies and establish appropriate pipelines for human gut microbiome research. Herein, we review the methods and procedures currently available for studying the human gut microbiome, including fecal sample collection, metagenomic DNA extraction, massive DNA sequencing, and data analyses with bioinformatics. We believe that this review will contribute to the progress of gut microbiome research in the clinical and practical aspects of human health.     

Metaproteomics Analysis Reveals the Adaptation Process for the Chicken Gut Microbiota

The animal gastrointestinal tract houses a large microbial community, the gut microbiota, that confers many benefits to its host, such as protection from pathogens and provision of essential metabolites. Metagenomic approaches have defined the chicken fecal microbiota in other studies, but here, we wished to assess the correlation between the metagenome and the bacterial proteome in order to better understand the healthy chicken gut microbiota. Here, we performed high-throughput sequencing of 16S rRNA gene amplicons and metaproteomics analysis of fecal samples to determine microbial gut composition and protein expression. 16 rRNA gene sequencing analysis identified Clostridiales, Bacteroidaceae, and Lactobacillaceae species as the most abundant species in the gut. For metaproteomics analysis, peptides were generated by using the Fasp method and subsequently fractionated by strong anion exchanges. Metaproteomics analysis identified 3,673 proteins. Among the most frequently identified proteins, 380 proteins belonged to Lactobacillus spp., 155 belonged to Clostridium spp., and 66 belonged to Streptococcus spp. The most frequently identified proteins were heat shock chaperones, including 349 GroEL proteins, from many bacterial species, whereas the most abundant enzymes were pyruvate kinases, as judged by the number of peptides identified per protein (spectral counting). Gene ontology and KEGG pathway analyses revealed the functions and locations of the identified proteins. The findings of both metaproteomics and 16S rRNA sequencing analyses are discussed.     

一种新的定量16S rRNA基因扩增子测序方法

近年来,16S扩增子测序技术被广泛应用于肠道微生物菌群结构和多样性研究,同时也常被用于临床样本中未知病原菌的检测。然而其对样本中物种组成的分辨率只能到属水平的相对丰度,且实验过程中多种因素皆可对结果产生一定影响,如样本起始浓度、PCR循环数、扩增引物等。为解决以上问题,本研究采用随机标签和内参法相结合的方法,开发了一套定量16S扩增子测序方法,将常规的16S rRNA编码基因测序结果中的相对丰度转化为绝对定量的拷贝数,有效提高了肠道菌群结构检测的精准性,降低了实验操作对结果的影响,也提高了测序与其他分子生物学方法间的可比性,有利于未来技术的进一步研发和改进。     

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.     

A metagenomic study of the gut microbiome in PTB’S disease

BackgroundThere is an abundant link between the gut microbiota and human health and it plays a critical role in the clinic. It is recognized that microbial dysregulation contributes to the pathogenesis of tuberculosis (TB) but the underlying mechanisms remain unclear. In this study, we investigated the association of gut microbiome composition with TB as well as its possible roles in the development of this disease.MethodsFecal samples were collected from 10 TB patients and 20 healthy control samples. DNA extracted from fecal samples was subjected to 16S rDNA gene sequencing analysis on the Illumina MiSeq platform.ResultsCompared with healthy control samples, the gut microbiome of patients with TB was characterized by the decreased Alpha diversity. Perhaps, the decrease of microbial diversity which results in microbial dysregulation is the reason for clinical patients with more symptoms. The PTB group showed the most unique microbiota by higher abundance of Bifidobacteriaceae, Bifidobacteriales, Coriobacteriaceae, Coriobacteriales, Actinobacteria, Caulobacteraceae, Phyllobacteriaceae, Rhizobiales, Burkholderiaceae, Burkholderiaceae. Inflammatory status in PTB patients may be associated with the increased abundance of Clostridia and decreased abundance of Prevotella. We found that the abundance of Solobacterium and Actinobacteria was higher in the patients. There were 4 significant differences (p < 0.05) in the two groups which belonged to four metabolic categories, including endocytosis, phosphotransferase system (PTS), toluene degradation, and amoebiasis.ConclusionWe applied the approach of metagenomic sequencing to characterize the features of gut microbiota in PTB patients. The present study provided a detailed analysis of the characterization of the gut microbiota in patients based on the clinic. According to the metagenome analysis, our results indicated that the gut microbiota in PTB patients was significantly different from healthy control samples as characterized by the bacteria and metabolic pathway. The richness of the gut microbiota in patients was revealed. It was hypothesized that the above-mentioned changes of the gut microbiota could exert an impact on the development of PTB through the downstream regulation of the immune status of the host by way of the gut–lung axis.