We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.

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DADA2: High-resolution sample inference from Illumina amplicon data

Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.

UPARSE: highly accurate OTU sequences from microbial amplicon reads

Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

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phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract A bottleneck for microbial community experiments with many samples and/or replicates is the fast quantification of individual taxon abundances, which is commonly achieved through sequencing marker genes such as the 16S rRNA gene. Here, we propose a new approach for high-throughput and high-quality enumeration of human gut bacteria in a defined community, combining flow cytometry and supervised classification to identify and quantify species mixed in silico and in defined communities in vitro. We identified species in a 5-species in silico community with an F1 score of 71%. In addition, we demonstrate in vitro that our method performs equally well or better than 16S rRNA gene sequencing in two-species cocultures and agrees with 16S rRNA gene sequencing data on the most abundant species in a four-species community. We found that shape and size differences alone are insufficient to distinguish species, and that it is thus necessary to exploit the multivariate nature of flow cytometry data. Finally, we observed that variability of flow cytometry data across replicates differs between gut bacterial species. In conclusion, the performance of supervised classification of gut species in flow cytometry data is species-dependent, but is for some combinations accurate enough to serve as a faster alternative to 16S rRNA gene sequencing. 

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Fast quantification of gut bacterial species in cocultures using flow cytometry and supervised classification

/pdf/p854-quantitative-microbiome-profiling-changes-the-described-4y0cubba1d.pdf

P854 Quantitative microbiome profiling changes the described dysbiotic state in inflammatory bowel disease

Editorial overview: It's the ecology, stupid: microbiome research in the post-stamp collecting age.

As wheat (Triticum aestivum) is an important staple food across the world, preservation of stable yields and increased productivity are major objectives in breeding programs. Drought is a global concern because its adverse impact is expected to be amplified in the future due to the current climate change. Here, we analyzed the effects of edaphic, environmental, and host factors on the wheat root microbiomes collected in soils from six regions in Belgium. Amplicon sequencing analysis of unplanted soil and wheat root endosphere samples indicated that the microbial community variations can be significantly explained by soil pH, microbial biomass, wheat genotype, and soil sodium and iron levels. Under drought stress, the biodiversity in the soil decreased significantly, but increased in the root endosphere community, where specific soil parameters seemingly determine the enrichment of bacterial groups. Indeed, we identified a cluster of drought-enriched bacteria that significantly correlated with soil compositions. Interestingly, integration of a functional analysis further revealed a strong correlation between the same cluster of bacteria and β-glucosidase and osmoprotectant proteins, two functions known to be involved in coping with drought stress. By means of this in silico analysis, we identified amplicon sequence variants (ASVs) that could potentially protect the plant from drought stress and validated them in planta. Yet, ASVs based on 16S rRNA sequencing data did not completely distinguish individual isolates because of their intrinsic short sequences. Our findings support the efforts to maintain stable crop yields under drought conditions through implementation of root microbiome analyses.

Interactions between soil compositions and the wheat root microbiome under drought stress: From an in silico to in planta perspective.

We read with great interest the study by Asnicar and colleagues, describing the exploration of gut microbiota composition in relation to gut transit time using the ‘blue dye’ method.1 In agreement with previous research, the authors provided convincing evidence that longer gut transit times are linked to increasing relative abundances of microbial species (ie, Akkermansia muciniphila, Bacteroides spp. and Alistipes spp. ).1–3 However, despite practical advantages, the ‘blue dye’ method does not allow to distinguish between segmental transit times.

In a recent study, we investigated segmental transit time and passage-related variation in pressure and pH in 22 individuals (11 subjects with normal weight and 11 subjects with obesity) using a wireless motility capsule (SmartPill©).4 During capsule passage, participants were asked to collect a faecal sample. Samples were subjected to 16S rRNA gene amplicon sequencing according to Valles-Colomer et al .5 Associations between clinical and SmartPill© variables and faecal microbiota community variation were assessed using single and stepwise multivariate distance-based redundancy analyses (dbRDA). Next, we explored associations between the microbiome covariates identified and participants’ enterotypes using Kruskal-Wallis test with post-hoc pairwise Dunn’s test.6 Finally, correlations between relative abundances of genera and significant variables were assessed …

Jeroen Raes

Papers

Fast quantification of gut bacterial species in cocultures using flow cytometry and supervised classification

P854 Quantitative microbiome profiling changes the described dysbiotic state in inflammatory bowel disease

Editorial overview: It's the ecology, stupid: microbiome research in the post-stamp collecting age.

Interactions between soil compositions and the wheat root microbiome under drought stress: From an in silico to in planta perspective.

Specific contributions of segmental transit times to gut microbiota composition.