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。     

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。     

引物应用策略：基于2对古菌16S rRNA基因通用引物对环境样品古菌多样性分析

【目的】找到适宜的16S rRNA基因通用引物应用策略,应对复杂环境微生物多样性调查,尤其目前高速发展的高通量测序技术带来的巨大挑战。【方法】用Oligocheck软件分别将两对应试的古菌16S rRNA基因通用引物与RDP(Ribosomal database project)数据库中古菌16S rRNA基因序列进行匹配比对。用两对应试引物分别构建海洋沉积物样品的古菌16S rRNA基因文库。【结果】软件匹配结果显示引物f109/r958与目的基因的匹配程度高于引物f21/r958。该结果与古菌16S rRNA基因文库RFLP分析、古菌多样性指数分析结果相吻合。数据还表明,2对引物的综合文库能更好满足该沉积物样品的古菌多样性分析。【结论】选用与数据库中目的基因匹配性高的通用引物和多个引物的联合使用,可以有效提高环境样品微生物多样性调查的分辨率。     

Windows操作系统下二代测序数据处理平台的建立及高通量信息分析标准流程在个人计算机上的实现

当前二代测序数据的处理广泛使用基于标准版本的Linux操作系统分析方法。这一系统专业性强,成本较高,操作界面不够友好,严重限制了大多数科研人员对数据的自主分析。本文创建了一个基于微软Windows操作系统的全功能二代测序数据的生物信息学分析系统,利用该系统经优选实现当前多种高通量测序数据的主流标准化分析流程。通过RNA-Seq的代表性案例,演算实测数据与传统Linux系统驱动的数据分析结果相比较,结果显示,本系统的组件和流程在常用的数据分析过程中,可以基本取代目前主流的Linux服务器或云计算平台,在运行效率相近的情况下,其操作极为简便且成本大大降低。本系统与所配附的编译软件及流程脚本,不仅为测序数据的生物信息学分析实操演练提供全面的解决方案,而且可以直接应用于专业的测序数据分析中。     

16S rRNA基因两个可变区对反映肠道菌群多样性与物种鉴定能力的比较分析

【摘 要】 目的 目前基于新一代测序技术开展的人类肠道元基因组学研究已成为微生物学乃至整个生物学中最活跃和最有潜力的学科方向。现阶段绝大部分以肠道菌群为靶标的研究主要基于16S rRNA基因可变区测序。本研究关注的是测序技术发展使得序列的读长能力延长后,选择16S rRNA基因V1-V3区与V3-V5区进行测序所反映的多样性与物种组成信息的异同点。方法 以两个真实的16S rRNA基因全长Sanger测序数据为基础,对其中的V1-V3和V3-V5两种片段进行了模拟数据的分析,将它们分别与16S rRNA基因的全长片段在OTU多样性水平和物种组成信息方面进行比较。结果 结果显示V1-V3区在OTU多样性上较V3-V5区更为接近全长序列;在物种组成表现上,两个可变区鉴定出的大部分的属的丰度与全长序列分析结果一致,但是各自有少数属的丰度结果与全长序列丰度结果存在差异。结论 在多样性分析上,选择V1-V3区片段能得到与全长更为接近的结果;而具体到菌种的组成分析中,V1-V3区和V3-V5区都有其局限性。     

16S rRNA测序技术在肠道微生物中的应用研究进展

16S rRNA测序是高通量测序依赖的肠道微生物研究方法之一,该方法可以对肠道微生物中的所有菌种进行精确定量,因此正逐渐成为研究肠道微生物菌种丰度变化的主流。肠道微生物16S rRNA测序的应用过程中有两个问题至关重要,一是如何根据需要选择测序方案;二是面对高通量测序得到的海量数据,如何进行生物信息学分析,以得到具有生物学意义的结果。从测序平台、测序片段、测序数据量的选择3个方面讨论了如何选择测序方案,并从序列聚类与注释、群落结构分析、关键分类单位的筛选与功能分析等方面对目前常用的生物信息学分析手段进行综述。     

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...     

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.     

分析方法对细菌群落16S rRNA基因扩增测序分析结果的影响

细菌16S rRNA基因扩增测序是当前环境微生物组学研究中应用最为广泛的方法之一。然而,测序序列最小分类单元的划分有多种方式,其对微生物多样性下游分析结果的影响还有待进一步探究。本研究通过提取5组环境样本(森林、农田、湿地土壤、湖泊沉积物和水体)的DNA进行16S rRNA基因扩增测序,对测序结果同时采用5种最小分类单元的划分方式(基于97%、98%、99%和100%序列相似性聚类的OTU以及基于DADA2算法得到的ASV)进行划分,比较分析最小分类单元划分方法对微生物群落多样性、组成、以及其与环境因子关联性分析造成的影响。结果表明,提高分类分辨率,能够获得更高的群落α多样性(Chao1和Shannon)和β多样性(P < 0.05),而相对于按序列相似性聚类的OTU,ASV方法会在一定程度上降低Chao1和PD指数。对于群落组成,分类单元的划分方式主要影响微生物组一些低丰度属(< 0.2%)的占比,而对较高的分类学水平(门水平)组成的影响较小。此外,冗余分析的结果表明,提高分类分辨率水平,能够使得环境因子对微生物群落能够获得更高的解释度,同时也会影响各环境因子对群落组成的...     

基于16S rRNA高通量测序技术分析福州地区慢传输型便秘患者肠道菌群的变化

目的 运用16S rRNA高通量测序技术分析福州地区慢传输型便秘(STC)患者肠道菌群变化的特征.方法 纳入60例STC患者和健康志愿者20例,留取新鲜粪便样本,冰块运送至实验室并存放于-80度冰箱,应用16S rRNA高通量测序技术,分析各组肠道菌群的生物多样性与结构组成.结果 测序后共得到1 702 004 524...     

SESAME (SEquence Sorter & AMplicon Explorer): genotyping based on high-throughput multiplex amplicon sequencing

SUMMARY: Characterizing genetic diversity through genotyping short amplicons is central to evolutionary biology. Next-generation sequencing (NGS) technologies changed the scale at which these type of data are acquired. SESAME is a web application package that assists genotyping of multiplexed individuals for several markers based on NGS amplicon sequencing. It automatically assigns reads to loci and individuals, corrects reads if standard samples are available and provides an intuitive graphical user interface (GUI) for allele validation based on the sequences and associated decision-making tools. The aim of SESAME is to help allele identification among a large number of sequences. AVAILABILITY: SESAME and its documentation are freely available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported Licence for Windows and Linux from http://www1.montpellier.inra.fr/CBGP/NGS/ or http://tinyurl.com/ngs-sesame.     

NINJA-OPS: Fast Accurate Marker Gene Alignment Using Concatenated Ribosomes

The explosion of bioinformatics technologies in the form of next generation sequencing (NGS) has facilitated a massive influx of genomics data in the form of short reads. Short read mapping is therefore a fundamental component of next generation sequencing pipelines which routinely match these short reads against reference genomes for contig assembly. However, such techniques have seldom been applied to microbial marker gene sequencing studies, which have mostly relied on novel heuristic approaches. We propose NINJA Is Not Just Another OTU-Picking Solution (NINJA-OPS, or NINJA for short), a fast and highly accurate novel method enabling reference-based marker gene matching (picking Operational Taxonomic Units, or OTUs). NINJA takes advantage of the Burrows-Wheeler (BW) alignment using an artificial reference chromosome composed of concatenated reference sequences, the “concatesome,” as the BW input. Other features include automatic support for paired-end reads with arbitrary insert sizes. NINJA is also free and open source and implements several pre-filtering methods that elicit substantial speedup when coupled with existing tools. We applied NINJA to several published microbiome studies, obtaining accuracy similar to or better than previous reference-based OTU-picking methods while achieving an order of magnitude or more speedup and using a fraction of the memory footprint. NINJA is a complete pipeline that takes a FASTA-formatted input file and outputs a QIIME-formatted taxonomy-annotated BIOM file for an entire MiSeq run of human gut microbiome 16S genes in under 10 minutes on a dual-core laptop.     

Simple tools for assembling and searching high-density picolitre pyrophosphate sequence data

Background

The advent of pyrophosphate sequencing makes large volumes of sequencing data available at a lower cost than previously possible. However, the short read lengths are difficult to assemble and the large dataset is difficult to handle. During the sequencing of a virus from the tsetse fly, Glossina pallidipes, we found the need for tools to search quickly a set of reads for near exact text matches.

Methods

A set of tools is provided to search a large data set of pyrophosphate sequence reads under a "live" CD version of Linux on a standard PC that can be used by anyone without prior knowledge of Linux and without having to install a Linux setup on the computer. The tools permit short lengths of de novo assembly, checking of existing assembled sequences, selection and display of reads from the data set and gathering counts of sequences in the reads.

Results

Demonstrations are given of the use of the tools to help with checking an assembly against the fragment data set; investigating homopolymer lengths, repeat regions and polymorphisms; and resolving inserted bases caused by incomplete chain extension.

Conclusion

The additional information contained in a pyrophosphate sequencing data set beyond a basic assembly is difficult to access due to a lack of tools. The set of simple tools presented here would allow anyone with basic computer skills and a standard PC to access this information.     

Microbiome profiling by illumina sequencing of combinatorial sequence-tagged PCR products

We developed a low-cost, high-throughput microbiome profiling method that uses combinatorial sequence tags attached to PCR primers that amplify the rRNA V6 region. Amplified PCR products are sequenced using an Illumina paired-end protocol to generate millions of overlapping reads. Combinatorial sequence tagging can be used to examine hundreds of samples with far fewer primers than is required when sequence tags are incorporated at only a single end. The number of reads generated permitted saturating or near-saturating analysis of samples of the vaginal microbiome. The large number of reads allowed an in-depth analysis of errors, and we found that PCR-induced errors composed the vast majority of non-organism derived species variants, an observation that has significant implications for sequence clustering of similar high-throughput data. We show that the short reads are sufficient to assign organisms to the genus or species level in most cases. We suggest that this method will be useful for the deep sequencing of any short nucleotide region that is taxonomically informative; these include the V3, V5 regions of the bacterial 16S rRNA genes and the eukaryotic V9 region that is gaining popularity for sampling protist diversity.     

A beginners guide to SNP calling from high-throughput DNA-sequencing data

High-throughput DNA sequencing (HTS) is of increasing importance in the life sciences. One of its most prominent applications is the sequencing of whole genomes or targeted regions of the genome such as all exonic regions (i.e., the exome). Here, the objective is the identification of genetic variants such as single nucleotide polymorphisms (SNPs). The extraction of SNPs from the raw genetic sequences involves many processing steps and the application of a diverse set of tools. We review the essential building blocks for a pipeline that calls SNPs from raw HTS data. The pipeline includes quality control, mapping of short reads to the reference genome, visualization and post-processing of the alignment including base quality recalibration. The final steps of the pipeline include the SNP calling procedure along with filtering of SNP candidates. The steps of this pipeline are accompanied by an analysis of a publicly available whole-exome sequencing dataset. To this end, we employ several alignment programs and SNP calling routines for highlighting the fact that the choice of the tools significantly affects the final results.     

jackalope: A swift,versatile phylogenomic and high‐throughput sequencing simulator

High‐throughput sequencing (HTS) is central to the study of population genomics and has an increasingly important role in constructing phylogenies. Choices in research design for sequencing projects can include a wide range of factors, such as sequencing platform, depth of coverage and bioinformatic tools. Simulating HTS data better informs these decisions, as users can validate software by comparing output to the known simulation parameters. However, current standalone HTS simulators cannot generate variant haplotypes under even somewhat complex evolutionary scenarios, such as recombination or demographic change. This greatly reduces their usefulness for fields such as population genomics and phylogenomics. Here I present the R package jackalope that simply and efficiently simulates (i) sets of variant haplotypes from a reference genome and (ii) reads from both Illumina and Pacific Biosciences platforms. Haplotypes can be simulated using phylogenies, gene trees, coalescent‐simulation output, population‐genomic summary statistics, and Variant Call Format (VCF) files. jackalope can simulate single, paired‐end or mate‐pair Illumina reads, as well as reads from Pacific Biosciences. These simulations include sequencing errors, mapping qualities, multiplexing and optical/PCR duplicates. It can read reference genomes from fasta files and can simulate new ones, and all outputs can be written to standard file formats. jackalope is available for Mac, Windows and Linux systems.     

Benchmarking of 16S rRNA gene databases using known strain sequences

16S rRNA gene analysis is the most convenient and robust method for microbiome studies. Inaccurate taxonomic assignment of bacterial strains could have deleterious effects as all downstream analyses rely heavily on the accurate assessment of microbial taxonomy. The use of mock communities to check the reliability of the results has been suggested. However, often the mock communities used in most of the studies represent only a small fraction of taxa and are used mostly as validation of sequencing run to estimate sequencing artifacts. Moreover, a large number of databases and tools available for classification and taxonomic assignment of the 16S rRNA gene make it challenging to select the best-suited method for a particular dataset. In the present study, we used authentic and validly published 16S rRNA gene type strain sequences (full length, V3-V4 region) and analyzed them using a widely used QIIME pipeline along with different parameters of OTU clustering and QIIME compatible databases. Data Analysis Measures (DAM) revealed a high discrepancy in ratifying the taxonomy at different taxonomic hierarchies. Beta diversity analysis showed clear segregation of different DAMs. Limited differences were observed in reference data set analysis using partial (V3-V4) and full-length 16S rRNA gene sequences, which signify the reliability of partial 16S rRNA gene sequences in microbiome studies. Our analysis also highlights common discrepancies observed at various taxonomic levels using various methods and databases.     

MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data

There is increasing interest in employing shotgun sequencing, rather than amplicon sequencing, to analyze microbiome samples. Typical projects may involve hundreds of samples and billions of sequencing reads. The comparison of such samples against a protein reference database generates billions of alignments and the analysis of such data is computationally challenging. To address this, we have substantially rewritten and extended our widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. The new program is called MEGAN Community Edition (CE) and is open source. By integrating MEGAN CE with our high-throughput DNA-to-protein alignment tool DIAMOND and by providing a new program MeganServer that allows access to metagenome analysis files hosted on a server, we provide a straightforward, yet powerful and complete pipeline for the analysis of metagenome shotgun sequences. We illustrate how to perform a full-scale computational analysis of a metagenomic sequencing project, involving 12 samples and 800 million reads, in less than three days on a single server. All source code is available here: https://github.com/danielhuson/megan-ce     

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

One of the major questions in microbial ecology is “who is there?” This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.     

Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies

The advent of next generation sequencing has coincided with a growth in interest in using these approaches to better understand the role of the structure and function of the microbial communities in human, animal, and environmental health. Yet, use of next generation sequencing to perform 16S rRNA gene sequence surveys has resulted in considerable controversy surrounding the effects of sequencing errors on downstream analyses. We analyzed 2.7×10(6) reads distributed among 90 identical mock community samples, which were collections of genomic DNA from 21 different species with known 16S rRNA gene sequences; we observed an average error rate of 0.0060. To improve this error rate, we evaluated numerous methods of identifying bad sequence reads, identifying regions within reads of poor quality, and correcting base calls and were able to reduce the overall error rate to 0.0002. Implementation of the PyroNoise algorithm provided the best combination of error rate, sequence length, and number of sequences. Perhaps more problematic than sequencing errors was the presence of chimeras generated during PCR. Because we knew the true sequences within the mock community and the chimeras they could form, we identified 8% of the raw sequence reads as chimeric. After quality filtering the raw sequences and using the Uchime chimera detection program, the overall chimera rate decreased to 1%. The chimeras that could not be detected were largely responsible for the identification of spurious operational taxonomic units (OTUs) and genus-level phylotypes. The number of spurious OTUs and phylotypes increased with sequencing effort indicating that comparison of communities should be made using an equal number of sequences. Finally, we applied our improved quality-filtering pipeline to several benchmarking studies and observed that even with our stringent data curation pipeline, biases in the data generation pipeline and batch effects were observed that could potentially confound the interpretation of microbial community data.