Comparing clustering and pre-processing in taxonomy analysis

Comparative 16S rRNA gene sequencing study of subgingival microbiota of healthy subjects and patients with periodontitis from four different countries

AIM

To investigate the differences between the subgingival microbiota of healthy subjects (HS) and periodontitis patients (PP) from four different countries through a metagenomic approach.

MATERIALS AND METHODS

Subgingival samples were obtained from subjects from four different countries. Microbial composition was analysed through high-throughput sequencing of the V3-V4 region of the 16S rRNA gene. The country of origin, diagnosis and clinical and demographic variables of the subjects were used to analyse the microbial profiles.

RESULTS

In total, 506 subgingival samples were analysed: 196 from HS and 310 from patients with periodontitis. Differences in richness, diversity and microbial composition were observed when comparing samples pertaining to different countries of origin and different subject diagnoses. Clinical variables, such as bleeding on probing, did not significantly affect the bacterial composition of the samples. A highly conserved core of microbiota associated with periodontitis was detected, while the microbiota associated with periodontally HS was much more diverse.

CONCLUSIONS

Periodontal diagnosis of the subjects was the main variable explaining the composition of the microbiota in the subgingival niche. Nevertheless, the country of origin also had a significant impact on the microbiota and is therefore an important factor to consider when describing subgingival bacterial communities.

Difference in microbiome compositions of healthy peri-implant sulcus and peri-implantitis sulcus from the same patient

Objective

The objective of this study is to compare the microbiome of healthy peri-implant sulcus (C) and peri-implantitis sulcus (U) from the same patient and analyze the difference in the microbiome composition.

Materials and methods

DNA samples of subgingival biofilms from 10 C (control group) and 10 U (uncontrolled group) sites were sent to Microbiome Center in Korea Research Institute of Biomedical Science and analyzed using 16s rRNA gene amplification and sequencing (MiSeq, Illumina) and human oral microbiome database (HOMD).

Results

At the phylum level, Firmicutes and Proteobacteria were more abundant in group C, while Firmicutes and Bacteroidetes were dominant in group U. At the genus level, the core peri-implant microbiome was Streptococcus in group C. On the other hand, the core peri-implant microbiome was Porphyromonas, especially P. gingivalis in group U.

Conclusion

In this study, the microbiome composition of peri-implantitis sulcus was different from that of healthy peri-implant sulcus from the same patient. The peri-implantitis microbiome was pathogen-enriched and was similar to the microbiome associated with periodontitis.

Application of ecosystem-specific reference databases for increased taxonomic resolution in soil microbial profiling

Intensive agriculture systems have paved the way for a growing human population. However, the abundant use of mineral fertilizers and pesticides may negatively impact nutrient cycles and biodiversity. One potential alternative is to harness beneficial relationships between plants and plant-associated rhizobacteria to increase nutrient-use efficiency and provide pathogen resistance. Plant-associated microbiota profiling can be achieved using high-throughput 16S rRNA gene amplicon sequencing. However, interrogation of these data is limited by confident taxonomic classifications at high taxonomic resolution (genus- or species level) with the commonly applied universal reference databases. High-throughput full-length 16S rRNA gene sequencing combined with automated taxonomy assignment (AutoTax) can be used to create amplicon sequence variant resolved ecosystems-specific reference databases that are superior to the traditional universal reference databases. This approach was used here to create a custom reference database for bacteria and archaea based on 987,353 full-length 16S rRNA genes from Askov and Cologne soils. We evaluated the performance of the database using short-read amplicon data and found that it resulted in the increased genus- and species-level classification compared to commonly use universal reference databases. The custom database was utilized to evaluate the ecosystem-specific primer bias and taxonomic resolution of amplicon primers targeting the V5–V7 region of the 16S rRNA gene commonly used within the plant microbiome field. Finally, we demonstrate the benefits of custom ecosystem-specific databases through the analysis of V5–V7 amplicon data to identify new plant-associated microbes for two legumes and two cereal species.

Applying convolutional neural networks to speed up environmental DNA annotation in a highly diverse ecosystem

High-throughput DNA sequencing is becoming an increasingly important tool to monitor and better understand biodiversity responses to environmental changes in a standardized and reproducible way. Environmental DNA (eDNA) from organisms can be captured in ecosystem samples and sequenced using metabarcoding, but processing large volumes of eDNA data and annotating sequences to recognized taxa remains computationally expensive. Speed and accuracy are two major bottlenecks in this critical step. Here, we evaluated the ability of convolutional neural networks (CNNs) to process short eDNA sequences and associate them with taxonomic labels. Using a unique eDNA data set collected in highly diverse Tropical South America, we compared the speed and accuracy of CNNs with that of a well-known bioinformatic pipeline (OBITools) in processing a small region (60 bp) of the 12S ribosomal DNA targeting freshwater fishes. We found that the taxonomic labels from the CNNs were comparable to those from OBITools, with high correlation levels for the composition of the regional fish fauna. The CNNs enabled the processing of raw fastq files at a rate of approximately 1 million sequences per minute, which was about 150 times faster than with OBITools. Given the good performance of CNNs in the highly diverse ecosystem considered here, the development of more elaborate CNNs promises fast deployment for future biodiversity inventories using eDNA.

Metagenomics and artificial intelligence in the context of human health

Human microbiome is ubiquitous, dynamic, and site-specific consortia of microbial communities. The pathogenic nature of microorganisms within human tissues has led to an increase in microbial studies. Characterization of genera, like Streptococcus, Cutibacterium, Staphylococcus, Bifidobacterium, Lactococcus and Lactobacillus through culture-dependent and culture-independent techniques has been reported. However, due to the unique environment within human tissues, it is difficult to culture these microorganisms making their molecular studies strenuous. MGs offer a gateway to explore and characterize hidden microbial communities through a culture-independent mode by direct DNA isolation. By function and sequence-based MGs, Scientists can explore the mechanistic details of numerous microbes and their interaction with the niche. Since the data generated from MGs studies is highly complex and multi-dimensional, it requires accurate analytical tools to evaluate and interpret the data. Artificial intelligence (AI) provides the luxury to automatically learn the data dimensionality and ease its complexity that makes the disease diagnosis and disease response easy, accurate and timely. This review provides insight into the human microbiota and its exploration and expansion through MG studies. The review elucidates the significance of MGs in studying the changing microbiota during disease conditions besides highlighting the role of AI in computational analysis of MG data.

Depth effect on the prokaryotic community assemblage associated with sponges from different rocky reefs

Background

Sponge microbiomes are essential for the function and survival of their host and produce biologically active metabolites, therefore, they are ideal candidates for ecological, pharmacologic and clinical research. Next-generation sequencing (NGS) has revealed that many factors, including the environment and host, determine the composition and structure of these symbiotic communities but the controls of this variation are not well described. This study assessed the microbial communities associated with two marine sponges of the genera Aplysina (Nardo, 1834) and Ircinia (Nardo, 1833) in rocky reefs from Punta Arena de la Ventana (Gulf of California) and Pichilingue (La Paz Bay) in the coast of Baja California Sur, México to determine the relative importance of environment and host in structuring the microbiome of sponges.

Methods

Specimens of Aplysina sp were collected by scuba diving at 10 m and 2 m; Ircinia sp samples were collected at 2 m. DNA of sponge-associated prokaryotes was extracted from 1 cm³ of tissue, purified and sent for 16S amplicon sequencing. Primer trimmed pair-ended microbial 16S rDNA gene sequences were merged using Ribosomal Database Project (RDP) Paired-end Reads Assembler. Chao1, Shannon and Simpson (alpha) biodiversity indices were estimated, as well permutational analysis of variance (PERMANOVA), and Bray-Curtis distances.

Results

The most abundant phyla differed between hosts. Those phyla were: Proteobacteria, Acidobacteria, Cyanobacteria, Chloroflexi, Actinobacteria, Bacteroidetes, and Planctomycetes. In Ircinia sp the dominant phylum was Acidobacteria. Depth was the main factor influencing the microbial community, as analysis of similarities (ANOSIM) showed a significant difference between the microbial communities from different depths.

Conclusion

Microbial diversity analysis showed that depth was more important than host in structuring the Aplysina sp and Ircinia sp microbiome. This observation contrast with previous reports that the sponge microbiome is highly host specific.

AncestralClust: clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees

MOTIVATION

Clustering is a fundamental task in the analysis of nucleotide sequences. Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster divergent sequences. Traditional clustering methods have mostly focused on optimizing high speed clustering of highly similar sequences. We develop a phylogenetic clustering method which infers ancestral sequences for a set of initial clusters and then uses a greedy algorithm to cluster sequences.

RESULTS

We describe a clustering program AncestralClust, which is developed for clustering divergent sequences. We compare this method with other state-of-the-art clustering methods using datasets of homologous sequences from different species. We show that, in divergent datasets, AncestralClust has higher accuracy and more even cluster sizes than current popular methods.

AVAILABILITY AND IMPLEMENTATION

AncestralClust is an Open Source program available at https://github.com/lpipes/ancestralclust.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reprocessing 16S rRNA Gene Amplicon Sequencing Studies: (Meta)Data Issues, Robustness, and Reproducibility

High-throughput sequencing technology provides an efficient method for evaluating microbial ecology. Different bioinformatics pipelines can be used to convert 16S ribosomal RNA gene amplicon sequencing data into an operational taxonomic unit (OTU) table that is used to analyze microbial communities. It is important to assess the robustness of these pipelines, each with specific algorithms and/or parameters, and their influence on the outcome of statistical tests. Articles with publicly available datasets on the oral microbiome were searched for, and five datasets were retrieved. These were from studies on changes in microbiota related to smoking, oral cancer, caries, diabetes, or periodontitis. Next, the data was processed with four pipelines based on VSEARCH, USEARCH, mothur, and UNOISE3. OTU tables were rarefied, and differences in α-diversity and β-diversity were tested for different groups in a dataset. Finally, these results were checked for consistency among these example pipelines. Of articles that deposited data, only 57% made all sequencing and metadata available. When processing the datasets, issues were encountered, caused by read characteristics and differences between tools and their defaults in combination with a lack of detail in the methodology of the articles. In general, the four mainstream pipelines provided similar results, but importantly, P-values sometimes differed between pipelines beyond the significance threshold. Our results indicated that for published articles, the description of bioinformatics methods and data deposition should be improved, and regarding reproducibility, that analysis of multiple subsamples is required when using rarefying as library-size normalization method.

Microbial community and performance of a partial nitritation/anammox sequencing batch reactor treating textile wastewater

Implementation of onsite bioremediation technologies is essential for textile industries due to rising concerns in terms of water resources and quality. Partial nitritation-anaerobic ammonium oxidation (PN/A) processes emerged as a valid, but unexplored, solution. In this study, the performance of a PN/A pilot-scale (9 m3) sequencing batch reactor treating digital textile printing wastewater (10-40 m3 d-1) was monitored by computing nitrogen (N) removal rate and efficiencies. Moreover, the structure of the bacterial community was assessed by next generation sequencing and quantitative polymerase chain reaction (qPCR) analyses of several genes, which are involved in the N cycle. Although anaerobic ammonium oxidation activity was inhibited and denitrification occurred, N removal rate increased from 16 to 61 mg N g VSS-1 d-1 reaching satisfactory removal efficiency (up to 70%). Ammonium (18-70 mg L-1) and nitrite (16-82 mg L-1) were detected in the effluent demonstrating an unbalance between the aerobic and anaerobic ammonia oxidation activity, while constant organic N was attributed to recalcitrant azo dyes. Ratio between nitrification and anammox genes remained stable reflecting a constant ammonia oxidation activity. A prevalence of ammonium oxidizing bacteria and denitrifiers suggested the presence of alternative pathways. PN/A resulted a promising cost-effective alternative for textile wastewater N treatment as shown by the technical-economic assessment. However, operational conditions and design need further tailoring to promote the activity of the anammox bacteria.

Epi-Gene: An R-Package for Easy Pan-Genome Analysis

The main aim of this study was to develop a set of functions that can analyze the genomic data with less time consumption and memory. Epi-gene is presented as a solution to large sequence file handling and computational time problems. It uses less time and less programming skills in order to work with a large number of genomes. In the current study, some features of the Epi-gene R-package were described and illustrated by using a dataset of the 14 Aeromonas hydrophila genomes. The joining, relabeling, and conversion functions were also included in this package to handle the FASTA formatted sequences. To calculate the subsets of core genes, accessory genes, and unique genes, various Epi-gene functions have been used. Heat maps and phylogenetic genome trees were also constructed. This whole procedure was completed in less than 30 minutes. This package can only work on Windows operating systems. Different functions from other packages such as dplyr and ggtree were also used that were available in R computing environment.

Microbial assemblages and methanogenesis pathways impact methane production and foaming in manure deep-pit storages

Foam accumulation in swine manure deep-pits has been linked to explosions and flash fires that pose devastating threats to humans and livestock. It is clear that methane accumulation within these pits is the fuel for the fire; it is not understood what microbial drivers cause the accumulation and stabilization of methane. Here, we conducted a 13-month field study to survey the physical, chemical, and biological changes of pit-manure across 46 farms in Iowa. Our results showed that an increased methane production rate was associated with less digestible feed ingredients, suggesting that diet influences the storage pit’s microbiome. Targeted sequencing of the bacterial 16S rRNA and archaeal mcrA genes was used to identify microbial communities’ role and influence. We found that microbial communities in foaming and non-foaming manure were significantly different, and that the bacterial communities of foaming manure were more stable than those of non-foaming manure. Foaming manure methanogen communities were enriched with uncharacterized methanogens whose presence strongly correlated with high methane production rates. We also observed strong correlations between feed ration, manure characteristics, and the relative abundance of specific taxa, suggesting that manure foaming is linked to microbial community assemblage driven by efficient free long-chain fatty acid degradation by hydrogenotrophic methanogenesis.

Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification

Bioinformatic analysis of eDNA metabarcoding data is a crucial step toward rigorously assessing biodiversity. Many programs are now available for each step of the required analyses, but their relative abilities at providing fast and accurate species lists have seldom been evaluated. We used simulated mock communities and real fish eDNA metabarcoding data to evaluate the performance of 13 bioinformatic programs and pipelines to retrieve fish occurrence and read abundance using the 12S mt rRNA gene marker. We used four indices to compare the outputs of each program with the simulated samples: sensitivity, F-measure, root-mean-square error (RMSE) on read relative abundances, and execution time. We found marked differences among programs only for the taxonomic assignment step, both in terms of sensitivity, F-measure and RMSE. Running time was highly different between programs for each step. The fastest programs with best indices for each step were assembled into a pipeline. We compared this pipeline to pipelines constructed from existing toolboxes (OBITools, Barque, and QIIME 2). Our pipeline and Barque obtained the best performance for all indices and appear to be better alternatives to highly used pipelines for analysing fish eDNA metabarcoding data when a complete reference database is available. Analysis on real eDNA metabarcoding data also indicated differences for taxonomic assignment and execution time only. This study reveals major differences between programs during the taxonomic assignment step. The choice of algorithm for the taxonomic assignment can have a significant impact on diversity estimates and should be made according to the objectives of the study.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

16S rRNA gene-based microbiome analysis identifies candidate bacterial strains that increase the storage time of potato tubers

In the past, the potato plant microbiota and rhizosphere have been studied in detail to improve plant growth and fitness. However, less is known about the postharvest potato tuber microbiome and its role in storage stability. The storage stability of potatoes depends on genotype and storage conditions, but the soil in which tubers were grown could also play a role. To understand the ecology and functional role of the postharvest potato microbiota, we planted four potato varieties in five soil types and monitored them until the tubers started sprouting. During storage, the bacterial community of tubers was analysed by next-generation sequencing of the 16S rRNA gene amplicons. The potato tubers exhibited soil-dependent differences in sprouting behaviour. The statistical analysis revealed a strong shift of the tuber-associated bacterial community from harvest to dormancy break. By combining indicator species analysis and a correlation matrix, we predicted associations between members of the bacterial community and tuber sprouting behaviour. Based on this, we identified Flavobacterium sp. isolates, which were able to influence sprouting behaviour by inhibiting potato bud outgrowth.

Towards a disease-associated common trait of gut microbiota dysbiosis: The pivotal role of Akkermansia muciniphila

BACKGROUND

Gut microbiota exerts a crucial role in gastrointestinal (GI) and extra-intestinal (EI) disorders. In this context, Akkermansia muciniphila is pivotal for the maintenance of host health and has been correlated with several disorders.

AIM

To explore the potential role of A. muciniphila as common dysbiotic marker linked to the disease status.

METHODS

A cohort of patients affected by GI and EI disorders was enrolled and compared to healthy controls (CTRLs). A targeted-metagenomics approach combined to unsupervised cluster and machine learning (ML) analyses provided microbiota signatures.

RESULTS

Microbiota composition was associated to disease phenotype, therapies, diet and anthropometric features, identifying phenotype and therapies as the most impacting variables on microbiota ecology. Unsupervised cluster analyses identified one cluster composed by the majority of patients. DESeq2 algorithm identified ten microbial discriminatory features of patients and CTRLs clusters. Among these microbes, Akkermansia muciniphila resulted the discriminating ML node between patients and CTRLs, independently of specific GI/EI disease or confounding effects. A. muciniphila decrease represented a transversal signature of gut microbiota alteration, showing also an inverse correlation with α-diversity.

CONCLUSION

Overall, A. muciniphila decline may have a crucial role in affecting microbial ecology and in discriminating patients from healthy subjects. Its grading may be considered as a gut dysbiosis feature associated to disease-related microbiota profile.

Anaerobic phenol biodegradation: kinetic study and microbial community shifts under high-concentration dynamic loading

The anaerobic biodegradation of phenol has been realised in a sequencing batch reactor (SBR) under anaerobic conditions with phenol as sole carbon and energy source and with glucose as co-substrate. A step-change increase of phenol loading (from 100 up to 2000 mg/L of phenol concentration in the feed solution) has been applied during the acclimation phase in order to progressively induce the development of a specialised microbial consortium. This approach, combined with the dynamic sequence of operations characterising SBRs and with the high biomass retention time, led to satisfactory phenol and COD removal efficiencies with values > 70% for the highest phenol input (2000 mg/L) fed as the single carbon and energy source. Analysis of removal efficiencies and biodegradation rates suggested that the use of glucose as co-substrate did not induce a significant improvement in process performance. Kinetic tests have been performed at different initial phenol (400-1000 mg/L) and glucose (1880-0 mg/L) concentrations to kinetically characterise the developed biomass: estimated kinetic constants are suitable for application and no inhibitory effect due to high concentrations of phenol has been observed in all investigated conditions. The microbial community has been characterised at different operating conditions through molecular tools: results confirm the successful adaptation-operation approach of the microbial consortium showing a gradual increase in richness and diversity and the occurrence and selection of a high proportion of phenol-degrading genera at the end of the experimentation. Key Points • Anaerobic phenol removal in the range of 70-99% in a sequencing batch reactor. • Negligible effect of co-substrate on removal efficiencies and biodegradation rates. • No biomass inhibition due to phenol concentration in the range of 400-1000 mg/L. • Increasing phenol loads promoted the culture enrichment of phenol-degrading genera.

Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches

In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In this narrative review, we have discussed the potential influence of study design and confounding variables on the current sequencing-based oral microbiome-systemic disease link studies. The current limitations of oral microbiome-systemic link studies on type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer are discussed in this review, followed by our perspective on how artificial intelligence (AI), particularly machine learning and deep learning approaches, can be employed for predicting systemic disease and host metadata from the oral microbiome. The application of AI for predicting systemic disease as well as host metadata requires the establishment of a global database repository with microbiome sequences and annotated host metadata. However, this task requires collective efforts from researchers working in the field of oral microbiome to establish more comprehensive datasets with appropriate host metadata. Development of AI-based models by incorporating consistent host metadata will allow prediction of systemic diseases with higher accuracies, bringing considerable clinical benefits.

Phenotype Prediction from Metagenomic Data Using Clustering and Assembly with Multiple Instance Learning (CAMIL)

The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper, we demonstrate a novel, efficient, and effective Multiple Instance Learning (MIL) based computational pipeline to predict patient phenotype from metagenomic data. MIL methods have the advantage that besides predicting the clinical phenotype, we can infer the instance level label or role of microbial sequence reads in the specific disease. Specifically, we use a Bag of Words method, which has been shown to be one of the most effective and efficient MIL methods. This involves assembly of the metagenomic sequence data, clustering of the assembled contigs, extracting features from the contigs, and using an SVM classifier to predict patient labels and identify the most relevant sequence clusters. With the exception of the given labels for the patients, this entire process is de novo (unsupervised). We call our pipeline "CAMIL", which stands for Clustering and Assembly with Multiple Instance Learning. We use multiple state-of-the-art clustering methods for feature extraction, evaluation, and comparison of the performance of our proposed approach for each of these clustering methods. We also present a fast and scalable pre-clustering algorithm as a preprocessing step for our proposed pipeline. Our approach achieves efficiency by partitioning the large number of sequence reads into groups (called canopies) using locality sensitive hashing (LSH). These canopies are then refined by using state-of-the-art sequence clustering algorithms. We use data from a well-known MGWAS study of patients with Type-2 Diabetes and show that our pipeline significantly outperforms the classifier used in that paper, as well as other common MIL methods.

The bacterial community in potato is recruited from soil and partly inherited across generations

Strong efforts have been made to understand the bacterial communities in potato plants and the rhizosphere. Research has focused on the effect of the environment and plant genotype on bacterial community structures and dynamics, while little is known about the origin and assembly of the bacterial community, especially in potato tubers. The tuber microbiota, however, may be of special interest as it could play an important role in crop quality, such as storage stability. Here, we used 16S rRNA gene amplicon sequencing to study the bacterial communities that colonize tubers of different potato cultivars commonly used in Austrian potato production over three generations and grown in different soils. Statistical analysis of sequencing data showed that the bacterial community of potato tubers has changed over generations and has become more similar to the soil bacterial community, while the impact of the potato cultivar on the bacterial assemblage has lost significance over time. The communities in different tuber parts did not differ significantly, while the soil bacterial community showed significant differences to the tuber microbiota composition. Additionally, the presence of OTUs in subsequent tuber generation points to vertical transmission of a subset of the tuber microbiota. Four OTUs were common to all tuber generations and all potato varieties. In summary, we conclude that the microbiota of potato tubers is recruited from the soil largely independent from the plant variety. Furthermore, the bacterial assemblage in potato tubers consists of bacteria transmitted from one tuber generation to the next and bacteria recruited from the soil.

Improving eDNA-based protist diversity assessments using networks of amplicon sequence variants

Effective and precise grouping of highly similar sequences remains a major bottleneck in the evaluation of high-throughput sequencing datasets. Amplicon sequence variants (ASVs) offer a promising alternative that may supersede the widely used operational taxonomic units (OTUs) in environmental sequencing studies. We compared the performance of a recently developed pipeline based on the algorithm DADA2 for obtaining ASVs against a pipeline based on the algorithm SWARM for obtaining OTUs. Illumina-sequencing of 29 individual ciliate species resulted in up to 11 ASVs per species, while SWARM produced up to 19 OTUs per species. To improve the congruency between species diversity and molecular diversity, we applied sequence similarity networks (SSNs) for second-level sequence grouping into network sequence clusters (NSCs). At 100% sequence similarity in SWARM-SSNs, NSC numbers decreased from 7.9-fold overestimation without abundance filter, to 4.5-fold overestimation when an abundance filter was applied. For the DADA2-SSN approach, NSC numbers decreased from 3.5-fold to 3-fold overestimation. Rand index cluster analyses predicted best binning results between 97% and 94% sequence similarity for both DADA2-SSNs and SWARM-SSNs. Depending on the ecological questions addressed in an environmental sequencing study with protists we recommend ASVs as replacement for OTUs, best in combination with SSNs.

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.

Temporal Dynamics of Bacterial Communities in Soil and Leachate Water After Swine Manure Application

Application of swine manure to agricultural land allows recycling of plant nutrients, but excess nitrate, phosphorus and fecal bacteria impact surface and drainage water quality. While agronomic and water quality impacts are well studied, little is known about the impact of swine manure slurry on soil microbial communities. We applied swine manure to intact soil columns collected from plots maintained under chisel plow or no-till with corn and soybean rotation. Targeted 16S-rRNA gene sequencing was used to characterize and to identify shifts in bacterial communities in soil over 108 days after swine manure application. In addition, six simulated rainfalls were applied during this time. Drainage water from the columns and surface soil were sampled, and DNA was extracted and sequenced. Unique DNA sequences (OTU) associated with 12 orders of bacteria were responsible for the majority of OTUs stimulated by manure application. Proteobacteria were most prevalent, followed by Bacteroidetes, Firmicutes, Actinobacteria, and Spirochaetes. While the majority of the 12 orders decreased after day 59, relative abundances of genes associated with Rhizobiales and Actinomycetales in soil increased. Bacterial orders which were stimulated by manure application in soil had varied responses in drainage waters over the course of the experiment. We also identified a “manure-specific core” of five genera who comprised 13% of the manure community and were not significantly abundant in non-manured control soils. Of these five genera, Clostridium sensu stricto was the only genus which did not return to pre-manure relative abundance in soil by day 108. Our results show that enrichment responses after manure amendment could result from displacement of native soil bacteria by manure-borne bacteria during the application process or growth of native bacteria using manure-derived available nutrients.

Assessing Pathogen Presence in an Intensively Tile Drained, Agricultural Watershed

Increases in swine production and concomitant manure application provide beneficial nutrients for crops but also include the potential to spread pathogenic bacteria in the environment. While manure is known to contain a variety of pathogens, little is known regarding the long-term effect of manure application on fate and transport of this diverse set of pathogens into surrounding waterways. We report on the use of 16S-rRNA gene sequencing to detect pathogen-containing genera in the agriculturally dominated South Fork Iowa River watershed, home to approximately 840,000 swine in the 76,000-ha basin. DNA was extracted from monthly grab samples collected from three surface water sites and two main artificial drainage outlets. DNA sequences from water samples were matched with sequences from genera known to contain pathogens using targeted 16S rRNA amplicon sequencing. The specific genera known to contain pathogens were quantified by combining percentage of genera sequence matches with 16S rRNA gene quantitative polymerase chain reaction results. Specifically, abundances of , , and significantly increased in surface water after typical fall manure application. Additionally, the likely transport pathways for specific genera known to contain pathogens were identified. Surface water concentrations were influenced mainly by artificial drainage, whereas was primarily transported to surface waters by runoff events. The results of this study will help us to understand environmental pathways that may be useful for mitigation of the diverse set of pathogenic genera transported in agroecosystems and the capability of manure application to alter existing microbial community structures.

Gut microbiota associations with common diseases and prescription medications in a population-based cohort

The human gut microbiome has been associated with many health factors but variability between studies limits exploration of effects between them. Gut microbiota profiles are available for >2700 members of the deeply phenotyped TwinsUK cohort, providing a uniform platform for such comparisons. Here, we present gut microbiota association analyses for 38 common diseases and 51 medications within the cohort. We describe several novel associations, highlight associations common across multiple diseases, and determine which diseases and medications have the greatest association with the gut microbiota. These results provide a reference for future studies of the gut microbiome and its role in human health.

The human gut microbiome has been associated with many health factors, but variability between studies limits exploration of these effects. Here, Jackson et al. analyse gut microbiota associations for 38 common diseases and 51 medications within >2700 members of the TwinsUK cohort.

Tissue age and plant genotype affect the microbiota of apple and pear bark

Plant tissues host complex fungal and bacterial communities, and their composition is determined by host traits such as tissue age, plant genotype and environmental conditions. Despite the importance of bark as a possible reservoir of plant pathogenic microorganisms, little is known about the associated microbial communities. In this work, we evaluated the composition of fungal and bacterial communities in the pear (Abate and Williams cultivars) and apple (Golden Delicious and Gala cultivars) bark of three/four-year-old shoots (old bark) or one-year-old shoots (young bark), using a meta-barcoding approach. The results showed that both fungal and bacterial communities are dominated by genera with ubiquitous attitudes, such as Aureobasidium, Cryptococcus, Deinococcus and Hymenobacter, indicating intense microbial migration to surrounding environments. The shoot age, plant species and plant cultivar influenced the composition of bark fungal and bacterial communities. In particular, bark communities included potential biocontrol agents that could maintain an equilibrium with potential plant pathogens. The abundance of fungal (e.g. Alternaria, Penicillium, Rosellinia, Stemphylium and Taphrina) and bacterial (e.g. Curtobacterium and Pseudomonas) plant pathogens was affected by bark age and host genotype, as well as those of fungal genera (e.g. Arthrinium, Aureobasidium, Rhodotorula, Sporobolomyces) and bacterial genera (e.g. Bacillus, Brevibacillus, Methylobacterium, Sphingomonas and Stenotrophomonas) with possible biocontrol and plant growth promotion properties.

ESPRIT-Forest: Parallel clustering of massive amplicon sequence data in subquadratic time

The rapid development of sequencing technology has led to an explosive accumulation of genomic sequence data. Clustering is often the first step to perform in sequence analysis, and hierarchical clustering is one of the most commonly used approaches for this purpose. However, it is currently computationally expensive to perform hierarchical clustering of extremely large sequence datasets due to its quadratic time and space complexities. In this paper we developed a new algorithm called ESPRIT-Forest for parallel hierarchical clustering of sequences. The algorithm achieves subquadratic time and space complexity and maintains a high clustering accuracy comparable to the standard method. The basic idea is to organize sequences into a pseudo-metric based partitioning tree for sub-linear time searching of nearest neighbors, and then use a new multiple-pair merging criterion to construct clusters in parallel using multiple threads. The new algorithm was tested on the human microbiome project (HMP) dataset, currently one of the largest published microbial 16S rRNA sequence dataset. Our experiment demonstrated that with the power of parallel computing it is now compu- tationally feasible to perform hierarchical clustering analysis of tens of millions of sequences. The software is available at http://www.acsu.buffalo.edu/∼yijunsun/lab/ESPRIT-Forest.html.

Second Era of OMICS in Caries Research: Moving Past the Phase of Disillusionment

Novel approaches using OMICS techniques enable a collective assessment of multiple related biological units, including genes, gene expression, proteins, and metabolites. In the past decade, next-generation sequencing ( NGS) technologies were improved by longer sequence reads and the development of genome databases and user-friendly pipelines for data analysis, all accessible at lower cost. This has generated an outburst of high-throughput data. The application of OMICS has provided more depth to existing hypotheses as well as new insights in the etiology of dental caries. For example, the determination of complete bacterial microbiomes of oral samples rather than selected species, together with oral metatranscriptome and metabolome analyses, supports the viewpoint of dysbiosis of the supragingival biofilms. In addition, metabolome studies have been instrumental in disclosing the contributions of major pathways for central carbon and amino acid metabolisms to biofilm pH homeostasis. New, often noncultured, oral streptococci have been identified, and their phenotypic characterization has revealed candidates for probiotic therapy. Although findings from OMICS research have been greatly informative, problems related to study design, data quality, integration, and reproducibility still need to be addressed. Also, the emergence and continuous updates of these computationally demanding technologies require expertise in advanced bioinformatics for reliable interpretation of data. Despite the obstacles cited above, OMICS research is expected to encourage the discovery of novel caries biomarkers and the development of next-generation diagnostics and therapies for caries control. These observations apply equally to the study of other oral diseases.

Livebearing or egg-laying mammals: 27 decisive nucleotides of FAM168

In the present study, we determine comprehensive molecular phylogenetic relationships of the novel myelin-associated neurite-outgrowth inhibitor (MANI) gene across the entire eukaryotic lineage. Combined computational genomic and proteomic sequence analyses revealed MANI as one of the two members of the novel family with sequence similarity 168 member (FAM168) genes, consisting of FAM168A and FAM168B, having distinct genetic differences that illustrate diversification in its biological function and genetic taxonomy across the phylogenetic tree. Phylogenetic analyses based on coding sequences of these FAM168 genes revealed that they are paralogs and that the earliest emergence of these genes occurred in jawed vertebrates such as Callorhinchus milii. Surprisingly, these two genes are absent in other chordates that have a notochord at some stage in their lives, such as branchiostoma and tunicates. In the context of phylogenetic relationships among eukaryotic species, our results demonstrate the presence of FAM168 orthologs in vertebrates ranging from Callorhinchus milii to Homo sapiens, displaying distinct taxonomic clusters, comprised of fish, amphibians, reptiles, birds, and mammals. Analyses of individual FAM168 exons in our sample provide new insights into the molecular relationships between FAM168A and FAM168B (MANI) on the one hand and livebearing and egg-laying mammals on the other hand, demonstrating that a distinctive intermediate exon 4, comprised of 27 nucleotides, appears suddenly only in FAM168A and there in the livebearing mammals only but is absent from all other species including the egg-laying mammals.

OptiClust, an Improved Method for Assigning Amplicon-Based Sequence Data to Operational Taxonomic Units

Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) is a computational bottleneck in the process of analyzing microbial communities. Although this has been an active area of research, it has been difficult to overcome the time and memory demands while improving the quality of the OTU assignments. Here, we developed a new OTU assignment algorithm that iteratively reassigns sequences to new OTUs to optimize the Matthews correlation coefficient (MCC), a measure of the quality of OTU assignments. To assess the new algorithm, OptiClust, we compared it to 10 other algorithms using 16S rRNA gene sequences from two simulated and four natural communities. Using the OptiClust algorithm, the MCC values averaged 15.2 and 16.5% higher than the OTUs generated when we used the average neighbor and distance-based greedy clustering with VSEARCH, respectively. Furthermore, on average, OptiClust was 94.6 times faster than the average neighbor algorithm and just as fast as distance-based greedy clustering with VSEARCH. An empirical analysis of the efficiency of the algorithms showed that the time and memory required to perform the algorithm scaled quadratically with the number of unique sequences in the data set. The significant improvement in the quality of the OTU assignments over previously existing methods will significantly enhance downstream analysis by limiting the splitting of similar sequences into separate OTUs and merging of dissimilar sequences into the same OTU. The development of the OptiClust algorithm represents a significant advance that is likely to have numerous other applications. IMPORTANCE The analysis of microbial communities from diverse environments using 16S rRNA gene sequencing has expanded our knowledge of the biogeography of microorganisms. An important step in this analysis is the assignment of sequences into taxonomic groups based on their similarity to sequences in a database or based on their similarity to each other, irrespective of a database. In this study, we present a new algorithm for the latter approach. The algorithm, OptiClust, seeks to optimize a metric of assignment quality by shuffling sequences between taxonomic groups. We found that OptiClust produces more robust assignments and does so in a rapid and memory-efficient manner. This advance will allow for a more robust analysis of microbial communities and the factors that shape them.

Shared and host-specific microbiome diversity and functioning of grapevine and accompanying weed plants

Weeds and crop plants select their microbiota from the same pool of soil microorganisms, however, the ecology of weed microbiomes is poorly understood. We analysed the microbiomes associated with roots and rhizospheres of grapevine and four weed species (Lamium amplexicaule L., Veronica arvensis L., Lepidium draba L. and Stellaria media L.) growing in proximity in the same vineyard using 16S rRNA gene sequencing. We also isolated and characterized 500 rhizobacteria and root endophytes from L. draba and grapevine. Microbiome data analysis revealed that all plants hosted significantly different microbiomes in the rhizosphere as well as in root compartment, however, differences were more pronounced in the root compartment. The shared microbiome of grapevine and the four weed species contained 145 OTUs (54.2%) in the rhizosphere, but only nine OTUs (13.2%) in the root compartment. Seven OTUs (12.3%) were shared in all plants and compartments. Approximately 56% of the major OTUs (>1%) showed more than 98% identity to bacteria isolated in this study. Moreover, weed-associated bacteria generally showed a higher species richness in the rhizosphere, whereas the root-associated bacteria were more diverse in the perennial plants grapevine and L. draba. Overall, weed isolates showed more plant growth-promoting characteristics compared with grapevine isolates.

Assessment of Common and Emerging Bioinformatics Pipelines for Targeted Metagenomics

Targeted metagenomics, also known as metagenetics, is a high-throughput sequencing application focusing on a nucleotide target in a microbiome to describe its taxonomic content. A wide range of bioinformatics pipelines are available to analyze sequencing outputs, and the choice of an appropriate tool is crucial and not trivial. No standard evaluation method exists for estimating the accuracy of a pipeline for targeted metagenomics analyses. This article proposes an evaluation protocol containing real and simulated targeted metagenomics datasets, and adequate metrics allowing us to study the impact of different variables on the biological interpretation of results. This protocol was used to compare six different bioinformatics pipelines in the basic user context: Three common ones (mothur, QIIME and BMP) based on a clustering-first approach and three emerging ones (Kraken, CLARK and One Codex) using an assignment-first approach. This study surprisingly reveals that the effect of sequencing errors has a bigger impact on the results that choosing different amplified regions. Moreover, increasing sequencing throughput increases richness overestimation, even more so for microbiota of high complexity. Finally, the choice of the reference database has a bigger impact on richness estimation for clustering-first pipelines, and on correct taxa identification for assignment-first pipelines. Using emerging assignment-first pipelines is a valid approach for targeted metagenomics analyses, with a quality of results comparable to popular clustering-first pipelines, even with an error-prone sequencing technology like Ion Torrent. However, those pipelines are highly sensitive to the quality of databases and their annotations, which makes clustering-first pipelines still the only reliable approach for studying microbiomes that are not well described.

Distinguishing the Signals of Gingivitis and Periodontitis in Supragingival Plaque: a Cross-Sectional Cohort Study in Malawi

Periodontal disease ranges from gingival inflammation (gingivitis) to the inflammation and loss of tooth-supporting tissues (periodontitis). Previous research has focused mainly on subgingival plaque, but supragingival plaque composition is also known to be associated with disease. Quantitative modeling of bacterial abundances across the natural range of periodontal severities can distinguish which features of disease are associated with particular changes in composition. We assessed a cross-sectional cohort of 962 Malawian women for periodontal disease and used 16S rRNA gene amplicon sequencing (V5 to V7 region) to characterize the bacterial compositions of supragingival plaque samples. Associations between bacterial relative abundances and gingivitis/periodontitis were investigated by using negative binomial models, adjusting for epidemiological factors. We also examined bacterial cooccurrence networks to assess community structure. The main differences in supragingival plaque compositions were associated more with gingivitis than periodontitis, including higher bacterial diversity and a greater abundance of particular species. However, even after controlling for gingivitis, the presence of subgingival periodontitis was associated with an altered supragingival plaque. A small number of species were associated with periodontitis but not gingivitis, including members of Prevotella, Treponema, and Selenomonas, supporting a more complex disease model than a linear progression following gingivitis. Cooccurrence networks of periodontitis-associated taxa clustered according to periodontitis across all gingivitis severities. Species including Filifactor alocis and Fusobacterium nucleatum were central to this network, which supports their role in the coaggregation of periodontal biofilms during disease progression. Our findings confirm that periodontitis cannot be considered simply an advanced stage of gingivitis even when only considering supragingival plaque.

IMPORTANCE

Periodontal disease is a major public health problem associated with oral bacteria. While earlier studies focused on a small number of periodontal pathogens, it is now accepted that the whole bacterial community may be important. However, previous high-throughput marker gene sequencing studies of supragingival plaque have largely focused on high-income populations with good oral hygiene without including a range of periodontal disease severities. Our study includes a large number of low-income participants with poor oral hygiene and a wide range of severities, and we were therefore able to quantitatively model bacterial abundances as functions of both gingivitis and periodontitis. A signal associated with periodontitis remains after controlling for gingivitis severity, which supports the concept that, even when only considering supragingival plaque, periodontitis is not simply an advanced stage of gingivitis. This suggests the future possibility of diagnosing periodontitis based on bacterial occurrences in supragingival plaque.

Vector soup: high-throughput identification of Neotropical phlebotomine sand flies using metabarcoding

Phlebotomine sand flies are haematophagous dipterans of primary medical importance. They represent the only proven vectors of leishmaniasis worldwide and are involved in the transmission of various other pathogens. Studying the ecology of sand flies is crucial to understand the epidemiology of leishmaniasis and further control this disease. A major limitation in this regard is that traditional morphological-based methods for sand fly species identifications are time-consuming and require taxonomic expertise. DNA metabarcoding holds great promise in overcoming this issue by allowing the identification of multiple species from a single bulk sample. Here, we assessed the reliability of a short insect metabarcode located in the mitochondrial 16S rRNA for the identification of Neotropical sand flies, and constructed a reference database for 40 species found in French Guiana. Then, we conducted a metabarcoding experiment on sand flies mixtures of known content and showed that the method allows an accurate identification of specimens in pools. Finally, we applied metabarcoding to field samples caught in a 1-ha forest plot in French Guiana. Besides providing reliable molecular data for species-level assignations of phlebotomine sand flies, our study proves the efficiency of metabarcoding based on the mitochondrial 16S rRNA for studying sand fly diversity from bulk samples. The application of this high-throughput identification procedure to field samples can provide great opportunities for vector monitoring and eco-epidemiological studies.

Peri-implant and periodontal microbiome diversity in aggressive periodontitis patients: a pilot study

AIM

To investigate the bacterial microbiome in periodontal and peri-implant biofilms deriving from aggressive periodontitis patients (AgP) in conditions of health and disease.

MATERIAL AND METHODS

Ninety-one plaque samples were collected from 18 patients previously diagnosed and treated for AgP. The samples were taken from (i) 24 residual periodontal pockets (TD) (n = 6 patients), (ii) 24 healthy periodontal sites (TH) (n = 6 patients), (iii) 24 dental sites from the same implant patients (TM) (n = 6 patients), (iv) 5 peri-implantitis sites (II) (n = 2 patients), (v) 6 peri-mucositis sites (IM) (n = 2 patients) and (vi) 8 healthy implant sites (IH) (n = 2 patients). All subjects underwent periodontal clinical and radiographic assessments. Bacterial DNA was extracted, PCR amplified using 16S rRNA gene V5-V7 primers (barcoded amplicons 785F;1175R), purified, pooled at equimolar concentrations and sequenced (MiSeq, Illumina) yielding 250 bp paired-end reads. The 16S rRNA reads were filtered, assembled and analysed.

RESULTS

The genera Propionibacterium, Paludibacter, Staphylococcus, Filifactor, Mogibacterium, Bradyrhizobium and Acinetobacter were unique to peri-implant sites (P = 0.05). In TM samples, different proportions and bacterial spp. were found when compared with the same patients' samples at implant sites. Specifically, Actinomyces (P = 0.013) and Corynebacterium (P = 0.030) genera showed to be significantly more abundant in the TM group when compared to the II. The highest phylogenetic diversity was observed in residual periodontal pocket sites (TD). Increased annual tooth loss rate and residual pocketing was related to high proportions of the genera Actinomyces, Porphyromonas, Prevotella, Streptococcus, Actinomycetaceae, TM7-3, Selenomonas, and Dialister, Treponema, Parvimonas and Peptostreptococcus in the TD group.

CONCLUSION

Within the limitations of this pilot study, the periodontal and peri-implant microbiome presents a dissimilar taxonomic composition across different niches within AgP patients. The host response, the habitat structure and the vast coexistence of strains and species surrounding implants and teeth in health and disease are likely to be shaping the heterogeneous composition of the subgingival biofilms. The TM7 phylum was found only in TD cases. The investigation of the impact of periodontal and peri-implant keystone species on these complex ecosystems in states of health and disease seems to be essential.

Application of a Database-Independent Approach To Assess the Quality of Operational Taxonomic Unit Picking Methods

Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) allows microbial ecologists to overcome the inconsistencies and biases within bacterial taxonomy and provides a strategy for clustering similar sequences that do not have representatives in a reference database. I have applied the Matthews correlation coefficient to assess the ability of 15 reference-independent and -dependent clustering algorithms to assign sequences to OTUs. This metric quantifies the ability of an algorithm to reflect the relationships between sequences without the use of a reference and can be applied to any data set or method. The most consistently robust method was the average neighbor algorithm; however, for some data sets, other algorithms matched its performance.

The influence of a short-term gluten-free diet on the human gut microbiome

Background

A gluten-free diet (GFD) is the most commonly adopted special diet worldwide. It is an effective treatment for coeliac disease and is also often followed by individuals to alleviate gastrointestinal complaints. It is known there is an important link between diet and the gut microbiome, but it is largely unknown how a switch to a GFD affects the human gut microbiome.

Methods

We studied changes in the gut microbiomes of 21 healthy volunteers who followed a GFD for four weeks. We collected nine stool samples from each participant: one at baseline, four during the GFD period, and four when they returned to their habitual diet (HD), making a total of 189 samples. We determined microbiome profiles using 16S rRNA sequencing and then processed the samples for taxonomic and imputed functional composition. Additionally, in all 189 samples, six gut health-related biomarkers were measured.

Results

Inter-individual variation in the gut microbiota remained stable during this short-term GFD intervention. A number of taxon-specific differences were seen during the GFD: the most striking shift was seen for the family Veillonellaceae (class Clostridia), which was significantly reduced during the intervention (p = 2.81 × 10⁻⁰⁵). Seven other taxa also showed significant changes; the majority of them are known to play a role in starch metabolism. We saw stronger differences in pathway activities: 21 predicted pathway activity scores showed significant association to the change in diet. We observed strong relations between the predicted activity of pathways and biomarker measurements.

Conclusions

A GFD changes the gut microbiome composition and alters the activity of microbial pathways.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0295-y) contains supplementary material, which is available to authorized users.

De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units

Background. 16S rRNA gene sequences are routinely assigned to operational taxonomic units (OTUs) that are then used to analyze complex microbial communities. A number of methods have been employed to carry out the assignment of 16S rRNA gene sequences to OTUs leading to confusion over which method is optimal. A recent study suggested that a clustering method should be selected based on its ability to generate stable OTU assignments that do not change as additional sequences are added to the dataset. In contrast, we contend that the quality of the OTU assignments, the ability of the method to properly represent the distances between the sequences, is more important. Methods. Our analysis implemented six de novo clustering algorithms including the single linkage, complete linkage, average linkage, abundance-based greedy clustering, distance-based greedy clustering, and Swarm and the open and closed-reference methods. Using two previously published datasets we used the Matthew's Correlation Coefficient (MCC) to assess the stability and quality of OTU assignments. Results. The stability of OTU assignments did not reflect the quality of the assignments. Depending on the dataset being analyzed, the average linkage and the distance and abundance-based greedy clustering methods generated OTUs that were more likely to represent the actual distances between sequences than the open and closed-reference methods. We also demonstrated that for the greedy algorithms VSEARCH produced assignments that were comparable to those produced by USEARCH making VSEARCH a viable free and open source alternative to USEARCH. Further interrogation of the reference-based methods indicated that when USEARCH or VSEARCH were used to identify the closest reference, the OTU assignments were sensitive to the order of the reference sequences because the reference sequences can be identical over the region being considered. More troubling was the observation that while both USEARCH and VSEARCH have a high level of sensitivity to detect reference sequences, the specificity of those matches was poor relative to the true best match. Discussion. Our analysis calls into question the quality and stability of OTU assignments generated by the open and closed-reference methods as implemented in current version of QIIME. This study demonstrates that de novo methods are the optimal method of assigning sequences into OTUs and that the quality of these assignments needs to be assessed for multiple methods to identify the optimal clustering method for a particular dataset.

Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples

Microbial community analysis via high-throughput sequencing of amplified 16S rRNA genes is an essential microbiology tool. We found the popular primer pair 515F (515F-C) and 806R greatly underestimated (e.g. SAR11) or overestimated (e.g. Gammaproteobacteria) common marine taxa. We evaluated marine samples and mock communities (containing 11 or 27 marine 16S clones), showing alternative primers 515F-Y (5'-GTGYCAGCMGCCGCGGTAA) and 926R (5'-CCGYCAATTYMTTTRAGTTT) yield more accurate estimates of mock community abundances, produce longer amplicons that can differentiate taxa unresolvable with 515F-C/806R, and amplify eukaryotic 18S rRNA. Mock communities amplified with 515F-Y/926R yielded closer observed community composition versus expected (r(2)  = 0.95) compared with 515F-Y/806R (r(2)  ∼ 0.5). Unexpectedly, biases with 515F-Y/806R against SAR11 in field samples (∼4-10-fold) were stronger than in mock communities (∼2-fold). Correcting a mismatch to Thaumarchaea in the 515F-C increased their apparent abundance in field samples, but not as much as using 926R rather than 806R. With plankton samples rich in eukaryotic DNA (> 1 μm size fraction), 18S sequences averaged ∼17% of all sequences. A single mismatch can strongly bias amplification, but even perfectly matched primers can exhibit preferential amplification. We show that beyond in silico predictions, testing with mock communities and field samples is important in primer selection.

Comprehensive Meta-analysis of Ontology Annotated 16S rRNA Profiles Identifies Beta Diversity Clusters of Environmental Bacterial Communities

Comprehensive mapping of environmental microbiomes in terms of their compositional features remains a great challenge in understanding the microbial biosphere of the Earth. It bears promise to identify the driving forces behind the observed community patterns and whether community assembly happens deterministically. Advances in Next Generation Sequencing allow large community profiling studies, exceeding sequencing data output of conventional methods in scale by orders of magnitude. However, appropriate collection systems are still in a nascent state. We here present a database of 20,427 diverse environmental 16S rRNA profiles from 2,426 independent studies, which forms the foundation of our meta-analysis. We conducted a sample size adaptive all-against-all beta diversity comparison while also respecting phylogenetic relationships of Operational Taxonomic Units(OTUs). After conventional hierarchical clustering we systematically test for enrichment of Environmental Ontology terms and their abstractions in all possible clusters. This post-hoc algorithm provides a novel formalism that quantifies to what extend compositional and semantic similarity of microbial community samples coincide. We automatically visualize significantly enriched subclusters on a comprehensive dendrogram of microbial communities. As a result we obtain the hitherto most differentiated and comprehensive view on global patterns of microbial community diversity. We observe strong clusterability of microbial communities in ecosystems such as human/mammal-associated, geothermal, fresh water, plant-associated, soils and rhizosphere microbiomes, whereas hypersaline and anthropogenic samples are less homogeneous. Moreover, saline samples appear less cohesive in terms of compositional properties than previously reported.

The Gut Microbiome Contributes to a Substantial Proportion of the Variation in Blood Lipids

Supplemental Digital Content is available in the text.

Evidence suggests that the gut microbiome is involved in the development of cardiovascular disease, with the host–microbe interaction regulating immune and metabolic pathways. However, there was no firm evidence for associations between microbiota and metabolic risk factors for cardiovascular disease from large-scale studies in humans. In particular, there was no strong evidence for association between cardiovascular disease and aberrant blood lipid levels.

Efficient and Accurate OTU Clustering with GPU-Based Sequence Alignment and Dynamic Dendrogram Cutting

De novo clustering is a popular technique to perform taxonomic profiling of a microbial community by grouping 16S rRNA amplicon reads into operational taxonomic units (OTUs). In this work, we introduce a new dendrogram-based OTU clustering pipeline called CRiSPy. The key idea used in CRiSPy to improve clustering accuracy is the application of an anomaly detection technique to obtain a dynamic distance cutoff instead of using the de facto value of 97 percent sequence similarity as in most existing OTU clustering pipelines. This technique works by detecting an abrupt change in the merging heights of a dendrogram. To produce the output dendrograms, CRiSPy employs the OTU hierarchical clustering approach that is computed on a genetic distance matrix derived from an all-against-all read comparison by pairwise sequence alignment. However, most existing dendrogram-based tools have difficulty processing datasets larger than 10,000 unique reads due to high computational complexity. We address this difficulty by developing two efficient algorithms for CRiSPy: a compute-efficient GPU-accelerated parallel algorithm for pairwise distance matrix computation and a memory-efficient hierarchical clustering algorithm. Our experiments on various datasets with distinct attributes show that CRiSPy is able to produce more accurate OTU groupings than most OTU clustering applications.

Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably describe zooplankton communities?

DNA metabarcoding is a promising method for describing communities and estimating biodiversity. This approach uses high-throughput sequencing of targeted markers to identify species in a complex sample. By convention, sequences are clustered at a predefined sequence divergence threshold (often 3%) into operational taxonomic units (OTUs) that serve as a proxy for species. However, variable levels of interspecific marker variation across taxonomic groups make clustering sequences from a phylogenetically diverse dataset into OTUs at a uniform threshold problematic. In this study, we use mock zooplankton communities to evaluate the accuracy of species richness estimates when following conventional protocols to cluster hypervariable sequences of the V4 region of the small subunit ribosomal RNA gene (18S) into OTUs. By including individually tagged single specimens and "populations" of various species in our communities, we examine the impact of intra- and interspecific diversity on OTU clustering. Communities consisting of single individuals per species generated a correspondence of 59-84% between OTU number and species richness at a 3% divergence threshold. However, when multiple individuals per species were included, the correspondence between OTU number and species richness dropped to 31-63%. Our results suggest that intraspecific variation in this marker can often exceed 3%, such that a single species does not always correspond to one OTU. We advocate the need to apply group-specific divergence thresholds when analyzing complex and taxonomically diverse communities, but also encourage the development of additional filtering steps that allow identification of artifactual rRNA gene sequences or pseudogenes that may generate spurious OTUs.

Toward accurate molecular identification of species in complex environmental samples: testing the performance of sequence filtering and clustering methods

Metabarcoding has the potential to become a rapid, sensitive, and effective approach for identifying species in complex environmental samples. Accurate molecular identification of species depends on the ability to generate operational taxonomic units (OTUs) that correspond to biological species. Due to the sometimes enormous estimates of biodiversity using this method, there is a great need to test the efficacy of data analysis methods used to derive OTUs. Here, we evaluate the performance of various methods for clustering length variable 18S amplicons from complex samples into OTUs using a mock community and a natural community of zooplankton species. We compare analytic procedures consisting of a combination of (1) stringent and relaxed data filtering, (2) singleton sequences included and removed, (3) three commonly used clustering algorithms (mothur, UCLUST, and UPARSE), and (4) three methods of treating alignment gaps when calculating sequence divergence. Depending on the combination of methods used, the number of OTUs varied by nearly two orders of magnitude for the mock community (60–5068 OTUs) and three orders of magnitude for the natural community (22–22191 OTUs). The use of relaxed filtering and the inclusion of singletons greatly inflated OTU numbers without increasing the ability to recover species. Our results also suggest that the method used to treat gaps when calculating sequence divergence can have a great impact on the number of OTUs. Our findings are particularly relevant to studies that cover taxonomically diverse species and employ markers such as rRNA genes in which length variation is extensive.

NGS-eval: NGS Error analysis and novel sequence VAriant detection tooL

Massively parallel sequencing of microbial genetic markers (MGMs) is used to uncover the species composition in a multitude of ecological niches. These sequencing runs often contain a sample with known composition that can be used to evaluate the sequencing quality or to detect novel sequence variants. With NGS-eval, the reads from such (mock) samples can be used to (i) explore the differences between the reads and their references and to (ii) estimate the sequencing error rate. This tool maps these reads to references and calculates as well as visualizes the different types of sequencing errors. Clearly, sequencing errors can only be accurately calculated if the reference sequences are correct. However, even with known strains, it is not straightforward to select the correct references from databases. We previously analysed a pyrosequencing dataset from a mock sample to estimate sequencing error rates and detected sequence variants in our mock community, allowing us to obtain an accurate error estimation. Here, we demonstrate the variant detection and error analysis capability of NGS-eval with Illumina MiSeq reads from the same mock community. While tailored towards the field of metagenomics, this server can be used for any type of MGM-based reads. NGS-eval is available at http://www.ibi.vu.nl/programs/ngsevalwww/.

Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses

We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (¹H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems.