An inter-laboratory study to investigate the impact of the bioinformatics component on microbiome analysis using mock communities.
An inter-laboratory study to investigate the impact of the bioinformatics component on microbiome analysis using mock communities.
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DOI：
10.1038/s41598-021-89881-2
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发表时间：
2021-05-19
影响因子：
4.6
通讯作者：
Huggett JF
中科院分区：
文献类型：
--
作者：
O'Sullivan DM;Doyle RM;Temisak S;Redshaw N;Whale AS;Logan G;Huang J;Fischer N;Amos GCA;Preston MD;Marchesi JR;Wagner J;Parkhill J;Motro Y;Denise H;Finn RD;Harris KA;Kay GL;O'Grady J;Ransom-Jones E;Wu H;Laing E;Studholme DJ;Benavente ED;Phelan J;Clark TG;Moran-Gilad J;Huggett JF
尽管全基因组宏基因组学的出现，有针对性的方法（如16S rRNA基因扩增子测序）仍然是有价值的，以确定样品的微生物组成。扩增子微生物组测序可以在来自正常无菌部位的临床样品上进行，以确定感染的病因（通常是单一病原体鉴定），或者在来自更复杂的小生境的样品上进行，例如需要鉴定多种微生物的人粘膜或环境样品。这些方法经常用于确定微生物的存在及其数量或相对丰度。进行微生物群落分析需要许多技术步骤，其中许多步骤可能具有影响最终结果的可观精度和偏差。为了使这些方法得到最大的准确性，需要在不同的实验室进行比较研究。在这项研究中，我们探讨了不同实验室采用的生物信息学方法对使用16S rRNA基因扩增子测序结果进行微生物组评估的影响。从两个模拟微生物群落样品产生数据，所述样品使用跨越16S rRNA基因的五个不同可变区的引物组进行扩增。PCR测序分析包括三次技术重复过程，以确定其方法的重复性。13个实验室参与了这项研究，每个实验室都使用他们选择的管道分析了相同的FASTQ文件。本研究捕获了所使用的方法以及从生物信息学分析得到的序列注释和相对丰度输出。将结果与模拟微生物群落样品中代表每种生物体的每种靶标的绝对丰度的数字PCR评估进行比较，并与鸟枪法宏基因组序列数据的分析进行比较。该环形试验表明，当使用16S rRNA基因扩增子测序数据时，单独选择生物信息学分析管道可能导致对微生物组组成的不同估计。该研究观察到生物存在和丰度方面的差异，并为确保可重复的管道开发和应用提供了资源。当使用自定义数据库和应用高严格操作分类单位（OTU）截止限时，观察到的差异尤其普遍。为了更准确地应用测序方法，需要清楚地描述不同分析步骤的影响，并设计解决方案来协调微生物组分析结果。
Despite the advent of whole genome metagenomics, targeted approaches (such as 16S rRNA gene amplicon sequencing) continue to be valuable for determining the microbial composition of samples. Amplicon microbiome sequencing can be performed on clinical samples from a normally sterile site to determine the aetiology of an infection (usually single pathogen identification) or samples from more complex niches such as human mucosa or environmental samples where multiple microorganisms need to be identified. The methodologies are frequently applied to determine both presence of micro-organisms and their quantity or relative abundance. There are a number of technical steps required to perform microbial community profiling, many of which may have appreciable precision and bias that impacts final results. In order for these methods to be applied with the greatest accuracy, comparative studies across different laboratories are warranted. In this study we explored the impact of the bioinformatic approaches taken in different laboratories on microbiome assessment using 16S rRNA gene amplicon sequencing results. Data were generated from two mock microbial community samples which were amplified using primer sets spanning five different variable regions of 16S rRNA genes. The PCR-sequencing analysis included three technical repeats of the process to determine the repeatability of their methods. Thirteen laboratories participated in the study, and each analysed the same FASTQ files using their choice of pipeline. This study captured the methods used and the resulting sequence annotation and relative abundance output from bioinformatic analyses. Results were compared to digital PCR assessment of the absolute abundance of each target representing each organism in the mock microbial community samples and also to analyses of shotgun metagenome sequence data. This ring trial demonstrates that the choice of bioinformatic analysis pipeline alone can result in different estimations of the composition of the microbiome when using 16S rRNA gene amplicon sequencing data. The study observed differences in terms of both presence and abundance of organisms and provides a resource for ensuring reproducible pipeline development and application. The observed differences were especially prevalent when using custom databases and applying high stringency operational taxonomic unit (OTU) cut-off limits. In order to apply sequencing approaches with greater accuracy, the impact of different analytical steps needs to be clearly delineated and solutions devised to harmonise microbiome analysis results.
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影响因子：
14.9
作者：
Klindworth A;Pruesse E;Schweer T;Peplies J;Quast C;Horn M;Glöckner FO
通讯作者：
Glöckner FO
影响因子：
15.5
作者：
Kim D;Hofstaedter CE;Zhao C;Mattei L;Tanes C;Clarke E;Lauder A;Sherrill-Mix S;Chehoud C;Kelsen J;Conrad M;Collman RG;Baldassano R;Bushman FD;Bittinger K
通讯作者：
Bittinger K
影响因子：
6.4
作者：
Bokulich, Nicholas A.;Rideout, Jai Ram;Caporaso, J. Gregory
通讯作者：
Caporaso, J. Gregory
影响因子：
1.4
作者：
Amelia, Tan Suet May;Amirul, Al-Ashraf Abdullah;Bhubalan, Kesaven
通讯作者：
Bhubalan, Kesaven
影响因子：
3.7
作者：
Laurence M;Hatzis C;Brash DE
通讯作者：
Brash DE