Operational Taxonomic Units Clustering Research Articles

S1308 Mucolytic Treatment of Small Bowel Aspirates Improves Symptom Correlation and Identification of Small Intestinal Bacterial Overgrowth Based on Culture and 16S rRNA Gene Sequencing

INTRODUCTION: Small intestinal bacterial overgrowth (SIBO) is defined by abnormal and excessive numbers of bacteria in the small bowel. As the viscosity of small bowel aspirates makes assessing the full microbiome profile by next generation sequencing difficult, we have used the mucolytic agent dithiothreitol (DTT) to reduce aspirate viscosity. In this study, we assessed the use of DTT in the identification and analysis of microbiome profiles in SIBO and non-SIBO subjects. METHODS: Gastrointestinal symptoms were assessed by questionnaire as part of the REIMAGINE (Revealing the Entire Intestinal Microbiota and its Associations with the Genetic, Immunologic, and Neuroendocrine Ecosystem) study. Duodenal aspirates were collected using a novel sterile dual-lumen aspiration catheter, and a subset were treated with DTT. All samples were cultured on MacConkey agar (MAC). DNA was isolated using the MagAttract PowerSoil DNA Kit. Microbiota were analyzed by 16S rRNA sequencing. Operational Taxonomic Units clustering and taxonomic analysis were performed with CLC Microbial Genomics Module v. 2.5. Statistical analysis was performed in R 3.6.0. RESULTS: Of 239 samples, 117 (79 non-SIBO and 38 SIBO, based on culture results) were treated with DTT and the remaining 122 samples (100 non-SIBO and 22 SIBO) were not treated with DTT. Overall microbiome profiles were similar in DTT (Figure 1A) and non-DTT (Figure 1B) samples. DTT-treated samples were better able to cluster subjects into SIBO and non-SIBO groups based on symptoms than non-DTT samples (P = 0.0001) (Figure 1C & D). DTT samples exhibited different relative abundances of several microbial taxa (Figure 2). Genus Klebsiella was more abundant in SIBO subjects (log fold change (LFC) = 7.167183, P < 0.0001), as was family Enterobacteriaceae (LFC = 3.631824, P < 0.0001). Using DTT-treated samples showed significant differences in bloating (P = 0.0618), diarrhea (P = 0.0065), urgency (P = 0.0274) and rectal mucous discharge (P = 0.0258) in SIBO and non-SIBO subjects. Absence of all 4 cardinal SIBO symptoms ruled out SIBO (specificity = 100%) based on culture in DTT-treated samples (sensitivity = 36.54%). CONCLUSION: While not changing the overall microbiome profile, using DTT improves the accuracy of culture results and identification of SIBO by sequencing, and better differentiates SIBO-related symptoms. Therefore, mucolytic treatment appears to be a crucial step in assessing the small bowel microbiome.Figure 1.: Overall microbiome relative abundance distribution of DTT-treated aspirate samples (A) and non-DTT-treated aspirate samples (B). Principal Coordinates Analysis of Bray Curtis distance between non-DTT (C) and non-DTT samples (D) (β-diversity analysis), colored by SIBO condition. P-value for the PERMANOVA test for the differentiation between groups in DTT data is 0.001, indicating significant separation between SIBO and non-SIBO patient.Figure 2.: Differentially expressed (significant) taxa between SIBO and non-SIBO in DTT samples.

Unique Bacterial Community of the Biofilm on Microplastics in Coastal Water.

Being immersed in seawater for a few days, microorganisms will adhere to the surface of different materials and form biofilms. After being immersed in seawater for 1week, high-throughput sequencing method was used to analyze the bacterial community structure of the biofilms on the surface of microbeads with different materials including steel, SiO2, and polyvinyl chloride (PVC). Operational taxonomic unit clustering results showed that some differences existed in the bacterial communities attached to the surface of different microbeads. Each microbead made by different material had its unique bacterial community. The heatmap indicated that the dominant genera on the surface of different microbeads were different from each other. Quantitative analysis showed that the relative abundance of dominant genera were different among different types of microbeads. Beta diversity analysis and principal component analysis showed that difference in the bacterial community on surface of steel-bead and PVC-bead was the most significant.

PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes.

BackgroundEnvironmental DNA and metabarcoding allow the identification of a mixture of species and launch a new era in bio- and eco-assessment. Many steps are required to obtain taxonomically assigned matrices from raw data. For most of these, a plethora of tools are available; each tool's execution parameters need to be tailored to reflect each experiment's idiosyncrasy. Adding to this complexity, the computation capacity of high-performance computing systems is frequently required for such analyses. To address the difficulties, bioinformatic pipelines need to combine state-of-the art technologies and algorithms with an easy to get-set-use framework, allowing researchers to tune each study. Software containerization technologies ease the sharing and running of software packages across operating systems; thus, they strongly facilitate pipeline development and usage. Likewise programming languages specialized for big data pipelines incorporate features like roll-back checkpoints and on-demand partial pipeline execution.FindingsPEMA is a containerized assembly of key metabarcoding analysis tools that requires low effort in setting up, running, and customizing to researchers’ needs. Based on third-party tools, PEMA performs read pre-processing, (molecular) operational taxonomic unit clustering, amplicon sequence variant inference, and taxonomy assignment for 16S and 18S ribosomal RNA, as well as ITS and COI marker gene data. Owing to its simplified parameterization and checkpoint support, PEMA allows users to explore alternative algorithms for specific steps of the pipeline without the need of a complete re-execution. PEMA was evaluated against both mock communities and previously published datasets and achieved results of comparable quality.ConclusionsA high-performance computing–based approach was used to develop PEMA; however, it can be used in personal computers as well. PEMA's time-efficient performance and good results will allow it to be used for accurate environmental DNA metabarcoding analysis, thus enhancing the applicability of next-generation biodiversity assessment studies.

Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences

BackgroundShotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters.ResultsTo evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2.ConclusionsOur results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/.)

A critical analysis of state-of-the-art metagenomics OTU clustering algorithms

Taxonomic profiling, using hyper-variable regions of 16S rRNA, is one of the important goals in metagenomics analysis. Operational taxonomic unit (OTU) clustering algorithms are the important tools to perform taxonomic profiling by grouping 16S rRNA sequence reads into OTU clusters. Presently various OTU clustering algorithms are available within different pipelines, even some pipelines have implemented more than one clustering algorithms, but there is less literature available for the relative performance and features of these algorithms. This makes the choice of using these methods unclear. In this study five current state-of-the-art OTU clustering algorithms (CDHIT, Mothur's Average Neighbour, SUMACLUST, Swarm, and UCLUST) have been comprehensively evaluated on the metagenomics sequencing data. It was found that in all the datasets, Mothur's average neighbour and Swarm created more number of OTU clusters. Based on normalized mutual information (NMI) and normalized information difference (NID), Swarm and Mothur's average neighbour showed better clustering qualities than others. But in terms of time complexity the greedy algorithms (SUMACLUST, CDHIT, and UCLUST) performed well. So there is a trade-off between quality and time, and it is necessary while analysing large size of 16S rRNA gene sequencing data.

Diatom metabarcoding applied to large scale monitoring networks: Optimization of bioinformatics strategies using Mothur software

Abstract Benthic diatoms are routinely used as ecological indicators in rivers. A standardized methodology is based on biofilm sampling, species identification, and counting under microscope. DNA-metabarcoding is an alternative methodology that can identify species and assess their proportion based on high-throughput DNA sequencing. Sequence data is analyzed with bioinformatics tools, and several strategies can be chosen. The strategy choice can affect communities composition and structure, and therefore the resulting ecological assessment. We wanted to optimize the bioinformatics strategy to obtain the closest results to microscopy. This was done in the framework of the Mothur pipeline. Here, 447 samples from French rivers were analyzed in the monitoring context of the European Water Framework Directive. Samples were analyzed both with DNA metabarcoding and microscopy. A usual bioinformatics strategy in Mothur includes clustering DNA-sequences into Operational Taxonomic Units (OTUs). Different algorithms exist for this. From a subsample of 142 samples, we showed that some strategies (Furthest neighbor) gave closer results to microscopy than others (Opticlust) in terms of community structure and diatom index values. However, we showed that OTU clustering was not necessary for ecological monitoring: Direct taxonomic assignment of individual sequence units (ISU) gave similar results to those obtained in microscopy. Interestingly, direct assignment enabled the detection of more species 2 to 3 times faster in terms of computation time compared to the OTU strategy. However, it remained important to remove low quality and chimeric sequences; if not, biomonitoring results differed greatly from microscopy. We showed that it was preferable to have a loose taxonomical identification threshold instead of a stringent one. This allowed detecting more species, which could participate in the index calculation and increased its performance. Indeed, in diatoms, phylogenetically neighbor species often have similar ecologies, and this explains why it is preferable, in a biomonitoring framework, to identify more species with less stringency instead of identifying few species with stringency. Finally, the best strategy (direct assignment of filtered ISU with a loose taxonomical threshold of 60%) was applied to the 447 samples covering a large diversity of ecological qualities. These data were then used to produce quality index values, using a quantification correction factor taking into account species biovolumes. Compared to microscopy, the DNA-based method assigned the same quality class for 66% of the samples, and 72% of the samples had an index value (ranging from 0 to 20) with less than one point difference from microscopy.

From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices?

AbstractAimEnvironmental DNA (eDNA) is increasingly used for analysing and modelling all‐inclusive biodiversity patterns. However, the reliability of eDNA‐based diversity estimates is commonly compromised by arbitrary decisions for curating the data from molecular artefacts. Here, we test the sensitivity of common ecological analyses to these curation steps, and identify the crucial ones to draw sound ecological conclusions.LocationValloire, French Alps.TaxonVascular plants and fungi.MethodsUsing soil eDNA metabarcoding data for plants and fungi from 20 plots sampled along a 1000‐m elevational gradient, we tested how the conclusions from three types of ecological analyses: (a) the spatial partitioning of diversity, (b) the diversity–environment relationship, and (c) the distance–decay relationship, are robust to data curation steps. Since eDNA metabarcoding data also comprise erroneous sequences with low frequencies, diversity estimates were further calculated using abundance‐based Hill numbers, which penalize rare sequences through a scaling parameter, namely the order of diversity q (Richness with q = 0, Shannon diversity with q ~ 1, Simpson diversity with q = 2).ResultsWe showed that results from different ecological analyses had varying degrees of sensitivity to data curation strategies and that the use of Shannon and Simpson diversities led to more reliable results. We demonstrated that molecular operational taxonomic unit clustering, removal of polymerase chain reaction errors and of cross‐sample contaminations had major impacts on ecological analyses.Main conclusionsIn the Era of Big Data, eDNA metabarcoding is going to be one of the major tools to describe, model and predict biodiversity in space and time. However, ignoring crucial data curation steps will impede the robustness of several ecological conclusions. Here, we propose a roadmap of crucial curation steps for different types of ecological analyses.

Phylogenetic Estimation of Community Composition and Novel Eukaryotic Lineages in Base Mine Lake: An Oil Sands Tailings Reclamation Site in Northern Alberta.

Reclamation of anthropogenically impacted environments is a critical issue worldwide. In the oil sands extraction industry of Alberta, reclamation of mining-impacted areas, especially areas affected by tailings waste, is an important aspect of the mining life cycle. A reclamation technique currently under study is water-capping, where tailings are capped by water to create an end-pit lake (EPL). Base Mine Lake (BML) is the first full-scale end-pit lake in the Alberta oil sands region. In this study, we sequenced eukaryotic 18S rRNA genes recovered from 92 samples of Base Mine Lake water in a comprehensive sampling programme covering the ice-free period of 2015. The 565 operational taxonomic units (OTUs) generated revealed a dynamic and diverse community including abundant Microsporidia, Ciliata and Cercozoa, though 41% of OTUs were not classifiable below the phylum level by comparison to 18S rRNA databases. Phylogenetic analysis of five heterotrophic phyla (Cercozoa, Fungi, Ciliata, Amoebozoa and Excavata) revealed substantial novel diversity, with many clusters of OTUs that were more similar to each other than to any reference sequence. All of these groups are entirely or mostly heterotrophic, as a relatively small number of definitively photosynthetic clades were amplified from the BML samples.

A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification.

Rapid and reliable identification of bacterial pathogens directly from patient samples is required for optimizing antimicrobial therapy. Although Sanger sequencing of the 16S ribosomal RNA (rRNA) gene is used as a molecular method, species identification and discrimination is not always achievable for bacteria as their 16S rRNA genes have sometimes high sequence homology. Recently, next generation sequencing (NGS) of the 16S–23S rRNA encoding region has been proposed for reliable identification of pathogens directly from patient samples. However, data analysis is laborious and time-consuming and a database for the complete 16S–23S rRNA encoding region is not available. Therefore, a better, faster, and stronger approach is needed for NGS data analysis of the 16S–23S rRNA encoding region. We compared speed and diagnostic accuracy of different data analysis approaches: de novo assembly followed by Basic Local Alignment Search Tool (BLAST), operational taxonomic unit (OTU) clustering, or mapping using an in-house developed 16S–23S rRNA encoding region database for the identification of bacterial species. De novo assembly followed by BLAST using the in-house database was superior to the other methods, resulting in the shortest turnaround time (2 h and 5 min), approximately 2 h less than OTU clustering and 4.5 h less than mapping, and a sensitivity of 80%. Mapping was the slowest and most laborious data analysis approach with a sensitivity of 60%, whereas OTU clustering was the least laborious approach with 70% sensitivity. Although the in-house database requires more sequence entries to improve the sensitivity, the combination of de novo assembly and BLAST currently appears to be the optimal approach for data analysis.

OTUX: V-region specific OTU database for improved 16S rRNA OTU picking and efficient cross-study taxonomic comparison of microbiomes.

Many microbiome studies employ reference-based operational taxonomic unit (OTU)-picking methods, which in general, rely on databases cataloguing reference OTUs identified through clustering full-length 16S rRNA genes. Given that the rate of accumulation of mutations are not uniform throughout the length of a 16S rRNA gene across different taxonomic clades, results of OTU identification or taxonomic classification obtained using ‘short-read’ sequence queries (as generated by next-generation sequencing platforms) can be inconsistent and of suboptimal accuracy. De novo OTU clustering results too can significantly vary depending upon the hypervariable region (V-region) targeted for sequencing. As a consequence, comparison of microbiomes profiled in different scientific studies becomes difficult and often poses a challenge in analysing new findings in context of prior knowledge. The OTUX approach of reference-based OTU-picking proposes to overcome these limitations by using ‘customized’ OTU reference databases, which can cater to different sets of short-read sequences corresponding to different 16S V-regions. The results obtained with OTUX-approach (which are in terms of OTUX-OTU identifiers) can also be ‘mapped back’ or represented in terms of other OTU database identifiers/taxonomy, e.g. Greengenes, thus allowing for easy cross-study comparisons. Validation with simulated datasets indicates more efficient, accurate, and consistent taxonomic classifications obtained using OTUX-approach, as compared with conventional methods.

Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches.

High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel “denoising” pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray–Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise.

Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks.

Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the Populus tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems.

Heritable components of the human fecal microbiome are associated with visceral fat

ABSTRACTObesity and its associated diseases are one of the major causes of death worldwide. The gut microbiota has been identified to have essential regulatory effects on human metabolism and obesity in particular. In a recent study we provided some insights into the link between the gut microbiota (GM) and adiposity, as well as host genetic modulation of these processes. Our results identify novel evidence of association between 6 adiposity phenotypes and faecal microbial operational taxonomic units (OTUs). Accumulation of visceral fat, a key risk factor for cardio-metabolic disease, has the strongest and most pervasive signature on the gut microbiota of the factors we examined. Furthermore, we observe that the adiposity-associated OTUs were classified as heritable and in some cases were also associated with host genetic variation at obesity-associated human candidate genes FHIT, TDRG1 and ELAVL4. This addendum confirms our previously published results in the TwinsUK cohort using a different approach to OTU clustering and multivariate analysis, and discusses further the importance of considering the GM as a complex ecosystem.

Scrutinizing key steps for reliable metabarcoding of environmental samples

Abstract Metabarcoding of environmental samples has many challenges and limitations that require carefully considered laboratory and analysis workflows to ensure reliable results. We explore how decisions regarding study design, laboratory set‐up, and bioinformatic processing affect the final results, and provide guidelines for reliable study of environmental samples. We evaluate the performance of four primer sets targeting COI and 16S regions characterizing arthropod diversity in bat faecal samples, and investigate how metabarcoding results are affected by parameters including: (1) number of PCR replicates per sample, (2) sequencing depth, (3) PCR replicate processing strategy (i.e. either additively, by combining the sequences obtained from the PCR replicates, or restrictively, by only retaining sequences that occur in multiple PCR replicates for each sample), (4) minimum copy number for sequences to be retained, (5) chimera removal, and (6) similarity thresholds for Operational Taxonomic Unit (OTU) clustering. Lastly, we measure within‐ and between‐taxa dissimilarities when using sequences from public databases to determine the most appropriate thresholds for OTU clustering and taxonomy assignment. Our results show that the use of multiple primer sets reduces taxonomic biases and increases taxonomic coverage. Taxonomic profiles resulting from each primer set are principally affected by how many PCR replicates are carried out per sample and how sequences are filtered across them, the sequence copy number threshold and the OTU clustering threshold. We also report considerable diversity differences between PCR replicates from each sample. Sequencing depth increases the dissimilarity between PCR replicates unless the bioinformatic strategies to remove allegedly artefactual sequences are adjusted according to the number of analysed sequences. Finally, we show that the appropriate identity thresholds for OTU clustering and taxonomy assignment differ between markers. Metabarcoding of complex environmental samples ideally requires (1) investigation of whether more than one primer sets targeting the same taxonomic group is needed to offset primer biases, (2) more than one PCR replicate per sample, (3) bioinformatic processing of sequences that balance diversity detection with removal of artefactual sequences, and (4) empirical selection of OTU clustering and taxonomy assignment thresholds tailored to each marker and the obtained taxa.

Evaluating the accuracy of amplicon-based microbiome computational pipelines on simulated human gut microbial communities

BackgroundMicrobiome studies commonly use 16S rRNA gene amplicon sequencing to characterize microbial communities. Errors introduced at multiple steps in this process can affect the interpretation of the data. Here we evaluate the accuracy of operational taxonomic unit (OTU) generation, taxonomic classification, alpha- and beta-diversity measures for different settings in QIIME, MOTHUR and a pplacer-based classification pipeline, using a novel software package: DECARD.ResultsIn-silico we generated 100 synthetic bacterial communities approximating human stool microbiomes to be used as a gold-standard for evaluating the colligative performance of microbiome analysis software. Our synthetic data closely matched the composition and complexity of actual healthy human stool microbiomes. Genus-level taxonomic classification was correctly done for only 50.4–74.8% of the source organisms. Miscall rates varied from 11.9 to 23.5%. Species-level classification was less successful, (6.9–18.9% correct); miscall rates were comparable to those of genus-level targets (12.5–26.2%). The degree of miscall varied by clade of organism, pipeline and specific settings used. OTU generation accuracy varied by strategy (closed, de novo or subsampling), reference database, algorithm and software implementation. Shannon diversity estimation accuracy correlated generally with OTU-generation accuracy. Beta-diversity estimates with Double Principle Coordinate Analysis (DPCoA) were more robust against errors introduced in processing than Weighted UniFrac. The settings suggested in the tutorials were among the worst performing in all outcomes tested.ConclusionsEven when using the same classification pipeline, the specific OTU-generation strategy, reference database and downstream analysis methods selection can have a dramatic effect on the accuracy of taxonomic classification, and alpha- and beta-diversity estimation. Even minor changes in settings adversely affected the accuracy of the results, bringing them far from the best-observed result. Thus, specific details of how a pipeline is used (including OTU generation strategy, reference sets, clustering algorithm and specific software implementation) should be specified in the methods section of all microbiome studies. Researchers should evaluate their chosen pipeline and settings to confirm it can adequately answer the research question rather than assuming the tutorial or standard-operating-procedure settings will be adequate or optimal.

Influence of methane and trichloroethylene domestication on bacterial community structure in landfill cover soil.

The biological oxidation capacity and change of microbial community structure of typical landfill cover soil cultured in situ by methane and trichloroethylene (TCE) were studied. The rates of CH4 oxidation and TCE degradation were 0.20-0.87 Μmol·g-1 soil·h-1 and 0.009-0.013 mg·L-1·h-1 among these landfill cover soils, respectively. The activity of CH4 oxidation in Shandong landfill cover soil was higher than that in landfill cover soils sampled from Guangdong, Shanghai and Chongqing. The α-diversity and microbial community structure before and after co-metabolism degradation were then investigated in all landfill cover soils by Illumina MiSeq sequencing. The results indicated that bacterial Operational Taxonomic Units (OTUs) were assigned to 39 phyla, 85 classes and 562 genera in all the commented OTUs clustering. The dominant bacterial populations were Proteobacteria, Bacteroidetes, Chloroflexi and Acidobacteria and the sum of the proportion was over 77.4% in all landfill cover soils. In addition, total percent of γ-Protebacteria, β--Protebacteria, α-Proteobacteria, Actinobacteria and Acidobacteria was over 26.5%. Specifically, the relative abundances of Methylophilaceae_uncultured, Anaerolineaceae_uncultured, Arthrobacter and Pseudomonas increased obviously after acclimated by TCE. These results suggested that there were co-metabolism degradation with non-methane compounds and aerobic assimilation of TCE besides the widely-agreed co-metabolism degradation by methanotrophs.

Reconciliation between operational taxonomic units and species boundaries.

The development of high-throughput sequencing technologies has revolutionised the field of microbial ecology via 16S rRNA gene amplicon sequencing approaches. Clustering those amplicon sequencing reads into operational taxonomic units (OTUs) using a fixed cut-off is a commonly used approach to estimate microbial diversity. A 97% threshold was chosen with the intended purpose that resulting OTUs could be interpreted as a proxy for bacterial species. Our results show that the robustness of such a generalised cut-off is questionable when applied to short amplicons only covering one or two variable regions of the 16S rRNA gene. It will lead to biases in diversity metrics and makes it hard to compare results obtained with amplicons derived with different primer sets. The method introduced within this work takes into account the differential evolutional rates of taxonomic lineages in order to define a dynamic and taxonomic-dependent OTU clustering cut-off score. For a taxonomic family consisting of species showing high evolutionary conservation in the amplified variable regions, the cut-off will be more stringent than 97%. By taking into consideration the amplified variable regions and the taxonomic family when defining this cut-off, such a threshold will lead to more robust results and closer correspondence between OTUs and species. This approach has been implemented in a publicly available software package called DynamiC.

MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets.

High-throughput sequencing-based metagenomics has garnered considerable interest in recent years. Numerous methods and tools have been developed for the analysis of metagenomic data. However, it is still a daunting task to install a large number of tools and complete a complicated analysis, especially for researchers with minimal bioinformatics backgrounds. To address this problem, we constructed an automated software named MetaDP for 16S rRNA sequencing data analysis, including data quality control, operational taxonomic unit clustering, diversity analysis, and disease risk prediction modeling. Furthermore, a support vector machine-based prediction model for intestinal bowel syndrome (IBS) was built by applying MetaDP to microbial 16S sequencing data from 108 children. The success of the IBS prediction model suggests that the platform may also be applied to other diseases related to gut microbes, such as obesity, metabolic syndrome, or intestinal cancer, among others (http://metadp.cn:7001/).

Diversity study of microbial community in bacon using metagenomic analysis

AbstractThis study is aimed to explore the diversity and dynamics of microorganisms in bacon. The microbes in 36 bacon samples collected from different plots (BC, BL, and BY) and process stages (stage I, II, III, and IV) were investigated by 16S rDNA sequencing. Quality control and operational taxonomic units clustering was performed by Quantitative Insights Into Microbial Ecology software. Total 1,460 representative sequences were identified. The relative abundance of Firmicutes in BC samples was significantly lower than BY samples (p value = .0304). The relative abundance of Proteobacteria in BC samples was significantly higher than BL (p value = .0072) and BY (p value = .0258) samples. The relative abundance of Proteobacteria in stage I was higher than stage IV (p value = .0148). Similarity clustering tree showed that Staphylococcus, Sphingomonas, Pseudomonas, Enterobacteriaceae, and Leuconostoc had higher abundances. These dominant categories might be used for controlling the transformation process and improving flavor and safety of bacon.Practical applicationsBacon is a well‐known fermented meat product that contains many microbes. Using 16S rDNA sequencing, the microbial community in 36 bacon samples collected from different plots and different process stages were investigated. After quality control and OUT clustering, the representative sequences of operational taxonomic units were annotated by Recombination Detection Program‐classifier method. Alpha diversity analysis, species composition analysis and abundance difference analysis separately were performed using Mothur software, Quantitative Insights Into Microbial Ecology software and the metastats command in Mothur. Staphylococcus was abundant in BY samples, which could result in infection. Sphingomonas, which was a major bacteria in BC samples, might affect the flavor of bacon in the stage I of processing. Additionally, Pseudomonas, Enterobacteriaceae, and Leuconostoc might have correlation with spoilage of bacon. These findings might be valuable for finding the bacterial species that can be used for controlling the transformation process of raw materials and improving flavor and safety of bacon.

A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units.

A variety of methods are available to collapse 16S rRNA gene sequencing reads to the operational taxonomic units (OTUs) used in microbiome analyses. A number of studies have aimed to compare the quality of the resulting OTUs. However, in the absence of a standard method to define and enumerate the different taxa within a microbial community, existing comparisons have been unable to compare the ability of clustering methods to generate units that accurately represent functional taxonomic segregation. We have previously demonstrated heritability of the microbiome and we propose this as a measure of each methods’ ability to generate OTUs representing biologically relevant units. Our approach assumes that OTUs that best represent the functional units interacting with the hosts’ properties will produce the highest heritability estimates. Using 1,750 unselected individuals from the TwinsUK cohort, we compared 11 approaches to OTU clustering in heritability analyses. We find that de novo clustering methods produce more heritable OTUs than reference based approaches, with VSEARCH and SUMACLUST performing well. We also show that differences resulting from each clustering method are minimal once reads are collapsed by taxonomic assignment, although sample diversity estimates are clearly influenced by OTU clustering approach. These results should help the selection of sequence clustering methods in future microbiome studies, particularly for studies of human host-microbiome interactions.