An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea
TL;DR: A ‘taxonomy to tree’ approach for transferring group names from an existing taxonomy to a tree topology is developed and used to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences.
Abstract: 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/.
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TL;DR: The results demonstrate that phylogeny and function are sufficiently linked that this 'predictive metagenomic' approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
Abstract: Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community's functional capabilities. Here we describe PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this 'predictive metagenomic' approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
6,860 citations
Journal Article•
TL;DR: The Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far, finding the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals.
Abstract: Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.
6,350 citations
TL;DR: An objective measure of genome quality is proposed that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities and is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches.
Abstract: Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of “marker” genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.
5,788 citations
Cites methods from "An improved Greengenes taxonomy wit..."
...Internal nodes were assigned taxonomic labels using tax2tree (McDonald et al. 2012)....
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TL;DR: Comparison of melanoma growth in mice harboring distinct commensal microbiota and observed differences in spontaneous antitumor immunity, suggests that manipulating the microbiota may modulate cancer immunotherapy.
Abstract: T cell infiltration of solid tumors is associated with favorable patient outcomes, yet the mechanisms underlying variable immune responses between individuals are not well understood. One possible modulator could be the intestinal microbiota. We compared melanoma growth in mice harboring distinct commensal microbiota and observed differences in spontaneous antitumor immunity, which were eliminated upon cohousing or after fecal transfer. Sequencing of the 16S ribosomal RNA identified Bifidobacterium as associated with the antitumor effects. Oral administration of Bifidobacterium alone improved tumor control to the same degree as programmed cell death protein 1 ligand 1 (PD-L1)–specific antibody therapy (checkpoint blockade), and combination treatment nearly abolished tumor outgrowth. Augmented dendritic cell function leading to enhanced CD8+ T cell priming and accumulation in the tumor microenvironment mediated the effect. Our data suggest that manipulating the microbiota may modulate cancer immunotherapy.
2,537 citations
TL;DR: The results illustrate the importance of parameter tuning for optimizing classifier performance, and the recommendations regarding parameter choices for these classifiers under a range of standard operating conditions are made.
Abstract: Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier (
https://github.com/qiime2/q2-feature-classifier
), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated “novel” marker-gene sequences, are available in our extensible benchmarking framework, tax-credit (
https://github.com/caporaso-lab/tax-credit-data
). Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.
2,475 citations
Cites methods from "An improved Greengenes taxonomy wit..."
...Challenges in this process include the short length of typical sequencing reads with current technology, sequencing and PCR errors [4], selection of appropriate marker genes that contain sufficient heterogeneity to differentiate target species but that are homogeneous enough in some regions to design broad-spectrum primers, quality of reference sequence annotations [5], and selection of a method that accurately predicts the taxonomic affiliation of millions of sequences at low computational cost....
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...Classification of bacterial/archaeal 16S rRNA gene sequences was made using the Greengenes (13_8 release) [5] reference sequence database preclustered at 99% ID, with amplicons for the domain of interest extracted using primers 27F/1492R [27], 515F/806R [28], or 27F/534R [29] with q2-feature-classifier’s extract_reads method....
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...the classic precision, recall, and F-measure metrics [5] (novel taxa use the standard calculations as described below, but modified definitions for true positive (TP), false positive (FP), and false negative (FN), as described...
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References
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Book•
01 May 1989
TL;DR: BCL3 and Sheehy cite Bergey's manual of determinative bacteriology of which systematic bacteriology, first edition, is an expansion.
Abstract: BCL3 and Sheehy cite Bergey's manual of determinative bacteriology of which systematic bacteriology, first edition, is an expansion. With v.4 the set is complete. The volumes cover, roughly, v.1, the Gram-negatives except those in v.3 ($87.95); v.2, the Gram-positives less actinomycetes ($71.95); v.
16,172 citations
TL;DR: The RDP Classifier can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes, and the majority of the classification errors appear to be due to anomalies in the current taxonomies.
Abstract: The Ribosomal Database Project (RDP) Classifier, a naive Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (≥95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
16,048 citations
"An improved Greengenes taxonomy wit..." refers methods in this paper
...In this paper, we found that retraining the RDP classifier (Wang et al., 2007) using taxa from the An improved Greengenes taxonomy D McDonald et al...
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...In this paper, we found that retraining the RDP classifier (Wang et al., 2007) using taxa from the The ISME Journal 0 50000 100000 150000 200000 250000 300000 K in gd om P hy lu m C la ss O rd er F am ily G en us S pe ci es S eq ue nc es c la ss ifi ed to a t m os t.....
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TL;DR: Improvements to FastTree are described that improve its accuracy without sacrificing scalability, and FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments.
Abstract: Background
We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability.
10,010 citations
"An improved Greengenes taxonomy wit..." refers methods in this paper
...A tree of the remaining 408 135 filtered sequences, (tree_16S_all_gg_2011_1) was built using FastTree v2....
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...Therefore, in order to address the classification of these groups we amended tree_16S_all_gg_2011_1 with 765 mostly partial length sequences from GenBank and generated a new de novo tree using FastTree; tree_16S_candiv_gg_2011_1....
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..., 2009), we constructed a phylogenetic tree using FastTree2 (Price et al., 2010) containing 408 135 sequences (tree_16S_all_gg_2011_1)....
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...For evaluation of NCBI-defined candidate phyla, we added 765 mostly partial length sequences, that failed the Greengenes filtering procedure but were required for the evaluation, to the alignment using PyNAST (Caporaso et al., 2010; based on the 29 November, 2010 Greengenes OTU templates) and generated a second FastTree (tree_16S_candiv_gg_ 2011_1) using the parameters described above....
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...The Greengenes taxonomy is currently based on a de novo phylogenetic tree of 408 135 quality-filtered sequences calculated using FastTree (Price et al., 2010)....
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TL;DR: In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI’s website.
Abstract: In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's website. NCBI resources include Entrez, PubMed, PubMed Central, LocusLink, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, SARS Coronavirus Resource, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD) and the Conserved Domain Architecture Retrieval Tool (CDART). Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih.gov.
9,604 citations