A 16S rRNA gene database (http://greengenes.lbl.gov) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria.
Reference phylogenies are crucial for providing a taxonomic framework for interpretation of marker gene and metagenomic surveys, which continue to reveal novel species at a remarkable rate. Greengenes is a dedicated full-length 16S rRNA gene database that provides users with a curated taxonomy based on de novo tree inference. We developed a 'taxonomy to tree' approach for transferring group names from an existing taxonomy to a tree topology, and used it to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408,315 sequences. We also incorporated explicit rank information provided by the NCBI taxonomy to group names (by prefixing rank designations) for better user orientation and classification consistency. The resulting merged taxonomy improved the classification of 75% of the sequences by one or more ranks relative to the original NCBI taxonomy with the most pronounced improvements occurring in under-classified environmental sequences. We also assessed candidate phyla (divisions) currently defined by NCBI and present recommendations for consolidation of 34 redundantly named groups. All intermediate results from the pipeline, which includes tree inference, jackknifing and transfer of a donor taxonomy to a recipient tree (tax2tree) are available for download. The improved Greengenes taxonomy should provide important infrastructure for a wide range of megasequencing projects studying ecosystems on scales ranging from our own bodies (the Human Microbiome Project) to the entire planet (the Earth Microbiome Project). The implementation of the software can be obtained from http://sourceforge.net/projects/tax2tree/.
MOTIVATION: The Nearest Alignment Space Termination (NAST) tool is commonly used in sequence-based microbial ecology community analysis, but due to the limited portability of the original implementation, it has not been as widely adopted as possible. Python Nearest Alignment Space Termination (PyNAST) is a complete reimplementation of NAST, which includes three convenient interfaces: a Mac OS X GUI, a command-line interface and a simple application programming interface (API). RESULTS: The availability of PyNAST will make the popular NAST algorithm more portable and thereby applicable to datasets orders of magnitude larger by allowing users to install PyNAST on their own hardware. Additionally because users can align to arbitrary template alignments, a feature not available via the original NAST web interface, the NAST algorithm will be readily applicable to novel tasks outside of microbial community analysis. AVAILABILITY: PyNAST is available at http://pynast.sourceforge.net.
Disease-suppressive soils are exceptional ecosystems in which crop plants suffer less from specific soil-borne pathogens than expected owing to the activities of other soil microorganisms. For most disease-suppressive soils, the microbes and mechanisms involved in pathogen control are unknown. By coupling PhyloChip-based metagenomics of the rhizosphere microbiome with culture-dependent functional analyses, we identified key bacterial taxa and genes involved in suppression of a fungal root pathogen. More than 33,000 bacterial and archaeal species were detected, with Proteobacteria, Firmicutes, and Actinobacteria consistently associated with disease suppression. Members of the γ-Proteobacteria were shown to have disease-suppressive activity governed by nonribosomal peptide synthetases. Our data indicate that upon attack by a fungal root pathogen, plants can exploit microbial consortia from soil for protection against infections.
Diving into Deep Water The Deepwater Horizon oil spill in the Gulf of Mexico was one of the largest oil spills on record. Its setting at the bottom of the sea floor posed an unanticipated risk as substantial amounts of hydrocarbons leaked into the deepwater column. Three separate cruises identified and sampled deep underwater hydrocarbon plumes that existed in May and June, 2010—before the well head was ultimately sealed. Camilli et al. (p. 201 ; published online 19 August) used an automated underwater vehicle to assess the dimensions of a stabilized, diffuse underwater plume of oil that was 22 miles long and estimated the daily quantity of oil released from the well, based on the concentration and dimensions of the plume. Hazen et al. (p. 204 ; published online 26 August) also observed an underwater plume at the same depth and found that hydrocarbon-degrading bacteria were enriched in the plume and were breaking down some parts of the oil. Finally, Valentine et al. (p. 208 ; published online 16 September) found that natural gas, including propane and ethane, were also present in hydrocarbon plumes. These gases were broken down quickly by bacteria, but primed the system for biodegradation of larger hydrocarbons, including those comprising the leaking crude oil. Differences were observed in dissolved oxygen levels in the plumes (a proxy for bacterial respiration), which may reflect differences in the location of sampling or the aging of the plumes.