Reprocessing 16S rRNA Gene Amplicon Sequencing Studies: (Meta)Data Issues, Robustness, and Reproducibility
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ABSTRACT:
SUBMITTER: Kang X
PROVIDER: S-EPMC8566820 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Microbial amplicon sequencing studies are an important tool in biological and biomedical research. Widespread 16S rRNA gene microbial surveys have shed light on the structure of many ecosystems inhabited by bacteria, including the human body. However, specialized software and algorithms are needed to convert raw sequencing data into biologically meaningful information (i.e. tables of bacterial counts). While different bioinformatic pipelines are available in a rapidly changing and improving field, users are often unaware of limitations and biases associated with individual pipelines and there is a lack of agreement regarding best practices. Here, we compared six bioinformatic pipelines for the analysis of amplicon sequence data: three OTU-level flows (QIIME-uclust, MOTHUR, and USEARCH-UPARSE) and three ASV-level (DADA2, Qiime2-Deblur, and USEARCH-UNOISE3). We tested workflows with different quality control options, clustering algorithms, and cutoff parameters on a mock community as well as on a large (N = 2170) recently published fecal sample dataset from the multi-ethnic HELIUS study. We assessed the sensitivity, specificity, and degree of consensus of the different outputs. DADA2 offered the best sensitivity, at the expense of decreased specificity compared to USEARCH-UNOISE3 and Qiime2-Deblur. USEARCH-UNOISE3 showed the best balance between resolution and specificity. OTU-level USEARCH-UPARSE and MOTHUR performed well, but with lower specificity than ASV-level pipelines. QIIME-uclust produced large number of spurious OTUs as well as inflated alpha-diversity measures and should be avoided in future studies. This study provides guidance for researchers using amplicon sequencing to gain biological insights.
| S-EPMC6964864 | biostudies-literature
Project description:Short-amplicon 16S rRNA gene sequencing is currently the method of choice for studies investigating microbiomes. However, comparative studies on differences in procedures are scarce. We sequenced human stool samples and mock communities with increasing complexity using a variety of commonly used protocols. Short amplicons targeting different variable regions (V-regions) or ranges thereof (V1-V2, V1-V3, V3-V4, V4, V4-V5, V6-V8, and V7-V9) were investigated for differences in the composition outcome due to primer choices. Next, the influence of clustering (operational taxonomic units [OTUs], zero-radius OTUs [zOTUs], and amplicon sequence variants [ASVs]), different databases (GreenGenes, the Ribosomal Database Project, Silva, the genomic-based 16S rRNA Database, and The All-Species Living Tree), and bioinformatic settings on taxonomic assignment were also investigated. We present a systematic comparison across all typically used V-regions using well-established primers. While it is known that the primer choice has a significant influence on the resulting microbial composition, we show that microbial profiles generated using different primer pairs need independent validation of performance. Further, comparing data sets across V-regions using different databases might be misleading due to differences in nomenclature (e.g., Enterorhabdus versus Adlercreutzia) and varying precisions in classification down to genus level. Overall, specific but important taxa are not picked up by certain primer pairs (e.g., Bacteroidetes is missed using primers 515F-944R) or due to the database used (e.g., Acetatifactor in GreenGenes and the genomic-based 16S rRNA Database). We found that appropriate truncation of amplicons is essential and different truncated-length combinations should be tested for each study. Finally, specific mock communities of sufficient and adequate complexity are highly recommended.IMPORTANCE In 16S rRNA gene sequencing, certain bacterial genera were found to be underrepresented or even missing in taxonomic profiles when using unsuitable primer combinations, outdated reference databases, or inadequate pipeline settings. Concerning the last, quality thresholds as well as bioinformatic settings (i.e., clustering approach, analysis pipeline, and specific adjustments such as truncation) are responsible for a number of observed differences between studies. Conclusions drawn by comparing one data set to another (e.g., between publications) appear to be problematic and require independent cross-validation using matching V-regions and uniform data processing. Therefore, we highlight the importance of a thought-out study design including sufficiently complex mock standards and appropriate V-region choice for the sample of interest. The use of processing pipelines and parameters must be tested beforehand.
| S-EPMC8544895 | biostudies-literature
Project description:Background16S rRNA amplicon sequencing is an accurate method of detecting microbial infection without culture. It is unclear if sequencing has additional benefits over routine diagnostic methods for Helicobacter pylori testing.MethodsWe enrolled Mongolian volunteers with dyspepsia. Using routine diagnostic methods, positive H. pylori was defined as positive results on histology/immunohistochemistry, culture, rapid urease test, or serology; negative H. pylori was defined by negative results from all these tests. We performed 16S rRNA sequencing on gastric biopsy specimens and calculated cutoffs for operational taxonomic units (OTUs) and relative abundance (RA) to define positive results using ROC curves.ResultsWe examined 161 individuals with a mean age of 43.6 years, and 64.6% were women. Using routine diagnostic methods, 122 (75.8%) participants were H. pylori positive, the sensitivity and specificity for 16S rRNA sequencing were 94.3% and 82.1% or 93.4% and 82.1% when cutoff values were set to 1113 (OTU number) or 4.4% RA, respectively (both p < .001). When combining the validated values, the concordance rate was high (91.1%); however, 16S rRNA sequencing had additional positive yield in 9 cases (5.6%) compared with routine diagnostic methods, and much greater additional positive yield compared to histopathology/IHC, culture, RUT, serology separately with 12 (7.4%), 37 (23.0%) and 43 (26.7%).Conclusion16S rRNA amplicon sequencing detects potentially important proportion of H. pylori-positive cases that test negative with routine diagnostic methods. The quantitative number of H. pylori can help to understand how it can be changing by diseases and RA give opportunity to understand how H. pylori communicate with other microbiota.
| S-EPMC8122063 | biostudies-literature
Project description:The human impact on natural habitats is increasing the complexity of human-wildlife interactions and leading to the emergence of infectious diseases worldwide. Highly successful synanthropic wildlife species, such as rodents, will undoubtedly play an increasingly important role in transmitting zoonotic diseases. We investigated the potential for recent developments in 16S rRNA amplicon sequencing to facilitate the multiplexing of the large numbers of samples needed to improve our understanding of the risk of zoonotic disease transmission posed by urban rodents in West Africa. In addition to listing pathogenic bacteria in wild populations, as in other high-throughput sequencing (HTS) studies, our approach can estimate essential parameters for studies of zoonotic risk, such as prevalence and patterns of coinfection within individual hosts. However, the estimation of these parameters requires cleaning of the raw data to mitigate the biases generated by HTS methods. We present here an extensive review of these biases and of their consequences, and we propose a comprehensive trimming strategy for managing these biases. We demonstrated the application of this strategy using 711 commensal rodents, including 208 Mus musculus domesticus, 189 Rattus rattus, 93 Mastomys natalensis, and 221 Mastomys erythroleucus, collected from 24 villages in Senegal. Seven major genera of pathogenic bacteria were detected in their spleens: Borrelia, Bartonella, Mycoplasma, Ehrlichia, Rickettsia, Streptobacillus, and Orientia. Mycoplasma, Ehrlichia, Rickettsia, Streptobacillus, and Orientia have never before been detected in West African rodents. Bacterial prevalence ranged from 0% to 90% of individuals per site, depending on the bacterial taxon, rodent species, and site considered, and 26% of rodents displayed coinfection. The 16S rRNA amplicon sequencing strategy presented here has the advantage over other molecular surveillance tools of dealing with a large spectrum of bacterial pathogens without requiring assumptions about their presence in the samples. This approach is therefore particularly suitable to continuous pathogen surveillance in the context of disease-monitoring programs. IMPORTANCE Several recent public health crises have shown that the surveillance of zoonotic agents in wildlife is important to prevent pandemic risks. High-throughput sequencing (HTS) technologies are potentially useful for this surveillance, but rigorous experimental processes are required for the use of these effective tools in such epidemiological contexts. In particular, HTS introduces biases into the raw data set that might lead to incorrect interpretations. We describe here a procedure for cleaning data before estimating reliable biological parameters, such as positivity, prevalence, and coinfection, using 16S rRNA amplicon sequencing on an Illumina MiSeq platform. This procedure, applied to 711 rodents collected in West Africa, detected several zoonotic bacterial species, including some at high prevalence, despite their never before having been reported for West Africa. In the future, this approach could be adapted for the monitoring of other microbes such as protists, fungi, and even viruses.
| S-EPMC5069956 | biostudies-literature
Project description:Conventional wisdom holds that PCR amplification for sequencing should employ pooled replicate reactions to reduce bias due to jackpot effects and chimera formation. However, modern amplicon data analysis employs methods that may be less sensitive to such artifacts. Here we directly compare results from single versus triplicate reactions for 16S amplicon sequencing and find no significant impact of adopting a less labor-intensive single-reaction protocol.
| S-EPMC7030937 | biostudies-literature
Project description:Data on the 16S rRNA gene amplicon sequences from Pteris vittata rhizosphere soils are reported. The following phyla were recorded in arsenic-rich soils: Actinobacteria (59%), Proteobacteria (26%), Chloroflexi (17%), and Acidobacteria (9%). Actinobacteria (45%), Proteobacteria (22%), Chloroflexi (10%), and Acidobacteria (11%) were in natural-mineral soils.
| S-EPMC10019286 | biostudies-literature
Project description:Ureteral stents are commonly used medical devices that harbor a unique and patient-specific microbial community. This protocol describes an optimized procedure for high-quality DNA extraction from both urine and ureteral stent samples for the purpose of downstream microbiota characterization by amplicon sequencing. Detailed instruction is provided for 16S rRNA gene V4 region sequencing with the Illumina platform, which enables accurate and reproducible microbiota profiling of low bacterial abundance urine and stent samples. For complete details on the use and execution of this protocol, please refer to Al et al. (2020).
| S-EPMC8055609 | biostudies-literature
Project description:One of the major methods to identify microbial community composition, to unravel microbial population dynamics, and to explore microbial diversity in environmental samples is high-throughput DNA- or RNA-based 16S rRNA (gene) amplicon sequencing in combination with bioinformatics analyses. However, focusing on environmental samples from contrasting habitats, it was not systematically evaluated (i) which analysis methods provide results that reflect reality most accurately, (ii) how the interpretations of microbial community studies are biased by different analysis methods and (iii) if the most optimal analysis workflow can be implemented in an easy-to-use pipeline. Here, we compared the performance of 16S rRNA (gene) amplicon sequencing analysis tools (i.e., Mothur, QIIME1, QIIME2, and MEGAN) using three mock datasets with known microbial community composition that differed in sequencing quality, species number and abundance distribution (i.e., even or uneven), and phylogenetic diversity (i.e., closely related or well-separated amplicon sequences). Our results showed that QIIME2 outcompeted all other investigated tools in sequence recovery (>10 times fewer false positives), taxonomic assignments (>22% better F-score) and diversity estimates (>5% better assessment), suggesting that this approach is able to reflect the in situ microbial community most accurately. Further analysis of 24 environmental datasets obtained from four contrasting terrestrial and freshwater sites revealed dramatic differences in the resulting microbial community composition for all pipelines at genus level. For instance, at the investigated river water sites Sphaerotilus was only reported when using QIIME1 (8% abundance) and Agitococcus with QIIME1 or QIIME2 (2 or 3% abundance, respectively), but both genera remained undetected when analyzed with Mothur or MEGAN. Since these abundant taxa probably have implications for important biogeochemical cycles (e.g., nitrate and sulfate reduction) at these sites, their detection and semi-quantitative enumeration is crucial for valid interpretations. A high-performance computing conformant workflow was constructed to allow FAIR (Findable, Accessible, Interoperable, and Re-usable) 16S rRNA (gene) amplicon sequence analysis starting from raw sequence files, using the most optimal methods identified in our study. Our presented workflow should be considered for future studies, thereby facilitating the analysis of high-throughput 16S rRNA (gene) sequencing data substantially, while maximizing reliability and confidence in microbial community data analysis.
| S-EPMC7645116 | biostudies-literature
Project description:The human bladder contains bacteria, even in the absence of infection. Interest in studying these bacteria and their association with bladder conditions is increasing. However, the chosen experimental method can limit the resolution of the taxonomy that can be assigned to the bacteria found in the bladder. 16S rRNA amplicon sequencing is commonly used to identify bacteria in urinary specimens, but it is typically restricted to genus-level identification. Our primary aim here was to determine if accurate species-level identification of bladder bacteria is possible using 16S rRNA amplicon sequencing. We evaluated the ability of different classification schemes, each consisting of combinations of a reference database, a 16S rRNA gene variable region, and a taxonomic classification algorithm to correctly classify bladder bacteria. We show that species-level identification is possible and that the reference database chosen is the most important component, followed by the 16S variable region sequenced. IMPORTANCE Accurate species-level identification from culture-independent techniques is of importance for microbial niches that are less well characterized, such as that of the bladder. 16S rRNA amplicon sequencing, a common culture-independent way to identify bacteria, is often critiqued for lacking species-level resolution. Here, we extensively evaluate classification schemes for species-level bacterial annotation of 16S amplicon data from bladder bacteria. Our results show that the proper choice of taxonomic database and variable region of the 16S rRNA gene sequence makes species level identification possible. We also show that this improvement can be achieved through the more careful application of existing methods and resources. Species-level information may deepen our understanding of associations between bacteria in the bladder and bladder conditions such as lower urinary tract symptoms and urinary tract infections.
| S-EPMC8547459 | biostudies-literature
Project description:High-throughput sequencing of 16S rRNA gene amplicons (16S-seq) has become a widely deployed method for profiling complex microbial communities but technical pitfalls related to data reliability and quantification remain to be fully addressed. In this work, we have developed and implemented a set of synthetic 16S rRNA genes to serve as universal spike-in standards for 16S-seq experiments. The spike-ins represent full-length 16S rRNA genes containing artificial variable regions with negligible identity to known nucleotide sequences, permitting unambiguous identification of spike-in sequences in 16S-seq read data from any microbiome sample. Using defined mock communities and environmental microbiota, we characterized the performance of the spike-in standards and demonstrated their utility for evaluating data quality on a per-sample basis. Further, we showed that staggered spike-in mixtures added at the point of DNA extraction enable concurrent estimation of absolute microbial abundances suitable for comparative analysis. Results also underscored that template-specific Illumina sequencing artifacts may lead to biases in the perceived abundance of certain taxa. Taken together, the spike-in standards represent a novel bioanalytical tool that can substantially improve 16S-seq-based microbiome studies by enabling comprehensive quality control along with absolute quantification.
| S-EPMC5389483 | biostudies-literature