A Format for Phylogenetic Placements

Matsen, F. A.; Hoffman, Noah G.; Gallagher, Aaron; Stamatakis, Alexandros

doi:10.1371/journal.pone.0031009

Cited by 72 publications

(74 citation statements)

References 17 publications

(20 reference statements)

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“…The methods take the recently-standardized JSON format for phylogenetic placements [16] as input. A tutorial and demonstration applying these methods can be found at http://fhcrc.github.com/microbiome-demo/.…”

Section: Introductionmentioning

confidence: 99%

Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison

2013

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Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate “average” of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome.

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Section: Introductionmentioning

confidence: 99%

Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison

2013

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“…Reference sequences are most conveniently provided in a “reference package” containing a multiple alignment and corresponding phylogenetic tree, along with taxonomic and other annotation [27]. We created two reference packages by recruiting 16S rRNA reference sequences based on similarity to denoised reads from CF specimens, and then selecting representatives of each species using “deenurp search-sequences” and “deenurp select-references” in DeeNuRP .…”

Section: Methodsmentioning

confidence: 99%

“…We created two reference packages by recruiting 16S rRNA reference sequences based on similarity to denoised reads from CF specimens, and then selecting representatives of each species using “deenurp search-sequences” and “deenurp select-references” in DeeNuRP . Reference sequence selection for species of interest was performed by minimizing the average distance to the closest leaf (ADCL) of reads placed on a phylogenetic tree of candidate reference sequences as implemented in “guppy adcl” [27]. The first reference package (CF-named, File S2 ) was assembled from sequences in RDP-named and was used for taxonomic assignment; the second (CF-unnamed, File S3 ) was assembled by comparing denoised reads to the RDP-full-length database.…”

Section: Methodsmentioning

confidence: 99%

Rapid 16S rRNA Next-Generation Sequencing of Polymicrobial Clinical Samples for Diagnosis of Complex Bacterial Infections

et al. 2013

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Classifying individual bacterial species comprising complex, polymicrobial patient specimens remains a challenge for culture-based and molecular microbiology techniques in common clinical use. We therefore adapted practices from metagenomics research to rapidly catalog the bacterial composition of clinical specimens directly from patients, without need for prior culture. We have combined a semiconductor deep sequencing protocol that produces reads spanning 16S ribosomal RNA gene variable regions 1 and 2 (∼360 bp) with a de-noising pipeline that significantly improves the fraction of error-free sequences. The resulting sequences can be used to perform accurate genus- or species-level taxonomic assignment. We explore the microbial composition of challenging, heterogeneous clinical specimens by deep sequencing, culture-based strain typing, and Sanger sequencing of bulk PCR product. We report that deep sequencing can catalog bacterial species in mixed specimens from which usable data cannot be obtained by conventional clinical methods. Deep sequencing a collection of sputum samples from cystic fibrosis (CF) patients reveals well-described CF pathogens in specimens where they were not detected by standard clinical culture methods, especially for low-prevalence or fastidious bacteria. We also found that sputa submitted for CF diagnostic workup can be divided into a limited number of groups based on the phylogenetic composition of the airway microbiota, suggesting that metagenomic profiling may prove useful as a clinical diagnostic strategy in the future. The described method is sufficiently rapid (theoretically compatible with same-day turnaround times) and inexpensive for routine clinical use.

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“…For phylogenetic analysis, genes or gene fragments are assigned to phylogenetic lineages based on homology to genes of known phylogenetic origin. This can be done for all genes from a metagenome dataset, for example using MEGAN (Huson and Mitra 2012), or for a set of conserved phylogenetic markers which can be placed onto a trees of known sequences from isolate genomes and/or amplified from uncultivated organisms, for example using pplacer (Matsen 2012). IMG/M allows for both approaches -an overall perspective of all the genes in a dataset or on a specific set of contigs is provided through the "Phylogenetic Distribution of Genes" option on the main metagenome page or in the scaffold cart, and genes with homology to particular phyla, families, genera or species can be retrieved.…”

Section: Metagenome Analysismentioning

confidence: 99%