Taxonomic Outline of the Bacteria and Archaea

Cole, James R.; Lilburn, Timothy; Harrison, Scott H.; Euzéby, J.; Tindall, Brian J.

doi:10.1601/toba7.7

Cited by 253 publications

(63 citation statements)

References 11 publications

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“…Taxonomic Assignment to 16S Reads. RDP classifier (v 2.1) software was used (29) to classify the sequences according to the taxonomy proposed by Garrity et al (30), maintained at the Ribosomal Database Project (RDP 10 database, Update 18). RDP classifier also emits, for each taxonomic rank, a confidence estimate (CE) based on a bootstrapping procedure, allowing to append the notation of "_uncertain" to assignments with CE lower than a defined cutoff , usually 50% (Table S4).…”

Section: Methodsmentioning

confidence: 99%

Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa

Filippo

Cavalieri

Paola

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

4,774

301

3,729

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Gut microbial composition depends on different dietary habits just as health depends on microbial metabolism, but the association of microbiota with different diets in human populations has not yet been shown. In this work, we compared the fecal microbiota of European children (EU) and that of children from a rural African village of Burkina Faso (BF), where the diet, high in fiber content, is similar to that of early human settlements at the time of the birth of agriculture. By using high-throughput 16S rDNA sequencing and biochemical analyses, we found significant differences in gut microbiota between the two groups. BF children showed a significant enrichment in Bacteroidetes and depletion in Firmicutes (P < 0.001), with a unique abundance of bacteria from the genus Prevotella and Xylanibacter, known to contain a set of bacterial genes for cellulose and xylan hydrolysis, completely lacking in the EU children. In addition, we found significantly more short-chain fatty acids (P < 0.001) in BF than in EU children. Also, Enterobacteriaceae (Shigella and Escherichia) were significantly underrepresented in BF than in EU children (P < 0.05). We hypothesize that gut microbiota coevolved with the polysaccharide-rich diet of BF individuals, allowing them to maximize energy intake from fibers while also protecting them from inflammations and noninfectious colonic diseases. This study investigates and compares human intestinal microbiota from children characterized by a modern western diet and a rural diet, indicating the importance of preserving this treasure of microbial diversity from ancient rural communities worldwide. metagenomics | nutrigenomics | biodiversity | 454-pyrosequencing | shortchain fatty acids

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Section: Methodsmentioning

confidence: 99%

Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa

Filippo

Cavalieri

Paola

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

4,774

301

3,729

View full text Add to dashboard Cite

show abstract

“…For the taxonomysupervised analysis, all sequences were allocated into taxonomy bins of genus and artificial unclassified taxa provided by the RDP classifier at 80, 50, and 0% confidence thresholds (11). The RDP classifier was trained using the Taxonomic Outline of the Bacteria and Archaea (TOBA), release 7.8 (27) augmented with unofficial taxa to cover regions of diversity not covered in the formal bacterial taxonomy. Each of the lowest taxonomy units, i.e., genera and unclassified taxa were considered as taxonomy bins.…”

Section: Methodsmentioning

confidence: 99%

Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

Sul

Cole

Jesus

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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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...

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“…These methanogens closely interact with anaerobic syntrophs (that is, fermentative heterotrophs and proton-reducing bacteria) by converting important intermediates such as hydrogen, formate and acetate, which are derived from the breakdown of complex organic matter, to methane and carbon dioxide (Schink, 1997;Hattori, 2008). Based on 16S rRNA sequence as the phylogenetic marker, methanogens include mainly members of the phylum Euryarchaeota of the domain Archaea (Garrity et al, 2007) and can be assigned into at least 29 genera from the classes Methanomicrobia, Methanobacteria, Methanococci and Methanopyri within the phylum Euryarchaeota (Figure 1). …”

Section: Introductionmentioning

confidence: 99%

Quantitative detection of culturable methanogenic archaea abundance in anaerobic treatment systems using the sequence-specific rRNA cleavage method

et al. 2009

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A method based on sequence-specific cleavage of rRNA with ribonuclease H was used to detect almost all known cultivable methanogens in anaerobic biological treatment systems. To do so, a total of 40 scissor probes in different phylogeny specificities were designed or modified from previous studies, optimized for their specificities under digestion conditions with 32 methanogenic reference strains, and then applied to detect methanogens in sludge samples taken from 6 different anaerobic treatment processes. Among these processes, known aceticlastic and hydrogenotrophic groups of methanogens from the families Methanosarcinaceae, Methanosaetaceae, Methanobacteriaceae, Methanothermaceae and Methanocaldococcaceae could be successfully detected and identified down to the genus level. Within the aceticlastic methanogens, the abundances of mesophilic Methanosaeta accounted for 5.7-48.5% of the total archaeal populations in mesophilic anaerobic processes, and those of Methanosarcina represented 41.7% of the total archaeal populations in thermophilic processes. For hydrogenotrophic methanogens, members of the Methanomicrobiales, Methanobrevibacter and Methanobacterium were detected in mesophilic processes (1.2-17.2%), whereas those of Methanothermobacter, Methanothermaceae and Methanocaldococcaceae were detected in thermophilic process (2.0-4.8%). Overall results suggested that those hierarchical scissor probes developed could be effective for rapid and possibly on-site monitoring of targeted methanogens in different microbial environments.

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Taxonomic Outline of the Bacteria and Archaea

Cited by 253 publications

References 11 publications

Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa

Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa

Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

Quantitative detection of culturable methanogenic archaea abundance in anaerobic treatment systems using the sequence-specific rRNA cleavage method

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