The Ribosomal Database Project (RDP) provides researchers with quality-controlled bacterial and archaeal small subunit rRNA alignments and analysis tools. An improved alignment strategy uses the Infernal secondary structure aware aligner to provide a more consistent higher quality alignment and faster processing of user sequences. Substantial new analysis features include a new Pyrosequencing Pipeline that provides tools to support analysis of ultra high-throughput rRNA sequencing data. This pipeline offers a collection of tools that automate the data processing and simplify the computationally intensive analysis of large sequencing libraries. In addition, a new Taxomatic visualization tool allows rapid visualization of taxonomic inconsistencies and suggests corrections, and a new class Assignment Generator provides instructors with a lesson plan and individualized teaching materials. Details about RDP data and analytical functions can be found at http://rdp.cme.msu.edu/.

The Ribosomal Database Project (RDP-II) provides the research community with aligned and annotated rRNA gene sequences, along with analysis services and a phylogenetically consistent taxonomic framework for these data. Updated monthly, these services are made available through the RDP-II website (http://rdp.cme.msu.edu/). RDP-II release 9.21 (August 2004) contains 101 632 bacterial small subunit rRNA gene sequences in aligned and annotated format. High-throughput tools for initial taxonomic placement, identification of related sequences, probe and primer testing, data navigation and subalignment download are provided. The RDP-II email address for questions or comments is rdpstaff@msu.edu.

Substantial new features have been implemented at the Ribosomal Database Project in response to the increased importance of high-throughput rRNA sequence analysis in microbial ecology and related disciplines. The most important changes include quality analysis, including chimera detection, for all available rRNA sequences and the introduction of myRDP Space, a new web component designed to help researchers place their own data in context with the RDP's data. In addition, new video tutorials describe how to use RDP features. Details about RDP data and analytical functions can be found at the RDP-II website ().

High-throughput sequencing of 16S rRNA genes has increased our understanding of microbial community structure, but now even higher-throughput methods to the Illumina scale allow the creation of much larger datasets with more samples and orders-ofmagnitude more sequences that swamp current analytic methods. We developed a method capable of handling these larger datasets on the basis of assignment of sequences into an existing taxonomy using a supervised learning approach (taxonomy-supervised analysis). We compared this method with a commonly used clustering approach based on sequence similarity (taxonomy-unsupervised analysis). We sampled 211 different bacterial communities from various habitats and obtained w1.3 million 16S rRNA sequences spanning the V4 hypervariable region by pyrosequencing. Both methodologies gave similar ecological conclusions in that β-diversity measures calculated by using these two types of matrices were significantly correlated to each other, as were the ordination configurations and hierarchical clustering dendrograms. In addition, our taxonomy-supervised analyses were also highly correlated with phylogenetic methods, such as UniFrac. The taxonomy-supervised analysis has the advantages that it is not limited by the exhaustive computation required for the alignment and clustering necessary for the taxonomy-unsupervised analysis, is more tolerant of sequencing errors, and allows comparisons when sequences are from different regions of the 16S rRNA gene. With the tremendous expansion in 16S rRNA data acquisition underway, the taxonomy-supervised approach offers the potential to provide more rapid and extensive community comparisons across habitats and samples.taxonomy bin | operational taxonomic unit T he increasing abundance of 16S rRNA gene sequences stimulated by reduced sequencing costs and greatly expanded parallel capacities is providing a more encompassing view of microbial communities (1). Although the short read lengths provided by the current technologies make it more challenging to assign sequences to bacterial taxonomy, the depth and replication provided are powerful advantages (2-4).Information on bacterial community structure can be compiled in a matrix where different communities are represented as rows and "species" as columns, i.e., a community-by-species matrix. When describing bacterial community relationships based on 16S rRNA gene sequences, each sequence is allocated to a species, usually termed an operational taxonomic unit (OTU), by alignment-based clustering at a specified nucleotide distance, often at a 97% identity. This community-by-OTU matrix, which is based exclusively on nucleotide distances among 16S rRNA gene sequences, has bacterial communities as rows with OTU as columns. This community-by-OTU matrix can be used to measure dissimilarities between bacterial communities (β-diversity) either by presence/absence or abundance data. These dissimilarities combined in a distance matrix can be used for bacterial community comparisons by ordination and clustering m...

Given the growing wealth of downstream information, the integration of molecular and non-molecular data on a given organism has become a major challenge. For micro-organisms, this information now includes a growing collection of sequenced genes and complete genomes, and for communities of organisms it includes metagenomes. Integration of the data is facilitated by the existence of authoritative, community-recognized, consensus identifiers that may form the heart of so-called information knuckles. The Genomic Standards Consortium (GSC) is building a mapping of identifiers across a group of federated databases with the aim to improve navigation across these resources and to enable the integration of their information in the near future. In particular, this is possible because of the existence of INSDC Genome Project Identifiers (GPIDs) and accession numbers, and the ability of the community to define new consensus identifiers such as the culture identifiers used in the StrainInfo.net bioportal. Here we outline (1) the general design of the Genomic Rosetta Stone project, (2) introduce example linkages between key databases (that cover information about genomes, 16S rRNA gene sequences, and microbial biological resource centers), and (3) make an open call for participation in this project providing a vision for its future use.

Microbes are key components of the soil environment and are important contributors to the sustainability of agricultural systems, which is especially significant for biofuel crops growing on marginal lands. We studied bacterial communities in the rhizosphere of five biofuel crops cultivated in four locations in Michigan to determine which factors were correlated to changes in the structure of those communities. Three of these sites were marginal lands in that two were not suitable for conventional agriculture and one was regulated as a brownfield due to prior industrial pollution. Bacterial community composition and structure were assessed by 454 sequencing of the 16S rRNA gene. A total of 387,111 sequences were used for multivariate statistical analysis and to test for correlation between community structure and environmental variables such as plant species, soil attributes, and location. The most abundant bacterial phyla found in the rhizosphere of all crops were Acidobacteria, Proteobacteria, Actinobacteria, and Verrucomicrobia. Bacterial communities grouped by location rather than by crop and their structures were correlated to soil attributes, principally pH, organic matter, and nutrients. The effect of plant species was low but significant, and interactions between locations, plant species, and soil attributes account for most of the explained variation in the structure of bacterial communities, showing a complex relationship between bacterial populations and their environment. Bacterial diversity was higher in the agricultural sites compared to adjacent forest sites, indicating that the cultivation of those biofuel crops increased the rRNA diversity.