SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

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.

/pdf/dada2-high-resolution-sample-inference-from-illumina-ygkdeyoxho.pdf

DADA2: High-resolution sample inference from Illumina amplicon data

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.

/pdf/phyloseq-an-r-package-for-reproducible-interactive-analysis-1ez1xx3dlr.pdf

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

The Human Microbiome Project Consortium reports the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome.

/pdf/structure-function-and-diversity-of-the-healthy-human-4mecpxyl8r.pdf

Structure, function and diversity of the healthy human microbiome

Supplementary Figure 1 Overview of the analysis pipeline. Supplementary Table 1 Details of conventionally raised and conventionalized mouse samples. Supplementary Discussion Expanded discussion of QIIME analyses presented in the main text; Sequencing of 16S rRNA gene amplicons; QIIME analysis notes; Expanded Figure 1 legend; Links to raw data and processed output from the runs with and without denoising.

/pdf/qiime-allows-analysis-of-high-throughput-community-2ob4mkjrat.pdf

QIIME allows analysis of high-throughput community sequencing data.

Research to understand and control microbiological risks associated with the consumption of fresh fruits and vegetables has examined many environments in the farm to fork continuum. An important data gap however, that remains poorly studied is the baseline description of microflora that may be associated with plant anatomy either endemically or in response to environmental pressures. Specific anatomical niches of plants may contribute to persistence of human pathogens in agricultural environments in ways we have yet to describe. Tomatoes have been implicated in outbreaks of Salmonella at least 17 times during the years spanning 1990 to 2010. Our research seeks to provide a baseline description of the tomato microbiome and possibly identify whether or not there is something distinctive about tomatoes or their growing ecology that contributes to persistence of Salmonella in this important food crop. DNA was recovered from washes of epiphytic surfaces of tomato anatomical organs; leaves, stems, roots, flowers and fruits of Solanum lycopersicum (BHN602), grown at a site in close proximity to commercial farms previously implicated in tomato-Salmonella outbreaks. DNA was amplified for targeted 16S and 18S rRNA genes and sheared for shotgun metagenomic sequencing. Amplicons and metagenomes were used to describe “native” bacterial microflora for diverse anatomical parts of Virginia-grown tomatoes. Distinct groupings of microbial communities were associated with different tomato plant organs and a gradient of compositional similarity could be correlated to the distance of a given plant part from the soil. Unique bacterial phylotypes (at 95% identity) were associated with fruits and flowers of tomato plants. These include Microvirga, Pseudomonas, Sphingomonas, Brachybacterium, Rhizobiales, Paracocccus, Chryseomonas and Microbacterium. The most frequently observed bacterial taxa across aerial plant regions were Pseudomonas and Xanthomonas. Dominant fungal taxa that could be identified to genus with 18S amplicons included Hypocrea, Aureobasidium and Cryptococcus. No definitive presence of Salmonella could be confirmed in any of the plant samples, although 16S sequences suggested that closely related genera were present on leaves, fruits and roots.

/pdf/baseline-survey-of-the-anatomical-microbial-ecology-of-an-5ep5uay4sc.pdf

Baseline survey of the anatomical microbial ecology of an important food plant: Solanum lycopersicum (tomato)

https://www.pure.ed.ac.uk/ws/files/296222401/CELL_D_22_01657_R1_2_noreviews.pdf

Antonio Gonzalez Peña

Papers

QIIME allows analysis of high-throughput community sequencing data.

Baseline survey of the anatomical microbial ecology of an important food plant: Solanum lycopersicum (tomato)

Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course