PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes

By Nicola Segata, Daniela Börnigen, Xochitl C. Morgan, Curtis Huttenhower. Nature Communications, 2013

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New microbial genomes are constantly being sequenced, and it is crucial to accurately determine their taxonomic identities and evolutionary relationships. Here we report PhyloPhlAn, a new method to assign microbial phylogeny and putative taxonomy using >400 proteins optimized from among 3,737 genomes. This method measures the sequence diversity of all clades, classifies genomes from deep-branching candidate divisions through closely related subspecies and improves consistency between phylogenetic and taxonomic groupings. PhyloPhlAn improved taxonomic accuracy for existing and newly sequenced genomes, detecting 157 erroneous labels, correcting 46 and placing or refining 130 new genomes. We provide examples of accurate classifications from subspecies (Sulfolobus spp.) to phyla, and of preliminary rooting of deep-branching candidate divisions, including consistent statistical support for Caldiserica (formerly candidate division OP5). PhyloPhlAn will thus be useful for both phylogenetic assessment and taxonomic quality control of newly sequenced genomes. The final phylogenies, conserved protein sequences and open-source implementation are available online. Sequencing whole microbial genomes has become standard practice and methods to examine their phylogenetic relationships need to match the increasing demand. Segata et al. present a new computational pipeline that allows fast and accurate taxonomic assignment of microbial species.

Subfields: Food Science, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0a654947dba8f5c0553fd215592f416da6c02924

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Citations in the corpus

Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters. (2014, background): “Presence/absence pattern of APE gene clusters across all complete genomes from selected bacterial genera, mapped onto the PhyloPhLan high-resolution phylogenetic tree (Segata et al., 2013).”
Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis (2013, methodology): “Briefly, PhyloPhlAn locates 400 ubiquitous bacterial genes in a given assembly by sequence alignment in amino acid space, then builds a tree by concatenating the most discriminative positions in each gene into a single long sequence and applying FastTree (Price et al., 2010), a standard tree reconst”
Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle (2019, methodology): “The phylogenetic analyses were performed with PhyloPhlAn (Segata et al., 2013) using the ‘‘dev’’ branch of the repository as of end of June 2018 (https://bitbucket.org/nsegata/phylophlan/overview).”
ggtreeExtra: Compact Visualization of Richly Annotated Phylogenetic Data (2021, background): “Associated data sets, such as the species abundance in each sample and the number or status of target genes for each species, can be incorporated and visualized on a phylogenetic tree to reveal new insights into factors that influence microbial community dynamics (Morgan et al. 2013; Segata et al. 2”
Compact graphical representation of phylogenetic data and metadata with GraPhlAn (2015, methodology): “…GraPhlAn is not limited to microbiome data and has additionally been applied to animal and plant taxonomies ( Tree of Sex Consortium, 2014 ) and to large prokaryotic phylogenies built using reference genomes ( Baldini et al., 2014 ; Chai et al., 2014 ; Langille et al., 2013 ; Segata et al., 2013 ).”
CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes (2015, background): “For instance, the 97% ubiquity criteria used to delineate marker genes is likely not optimal, and the use of a probabilistic model for assessing the presence/absence of a gene across all genomes in a lineage may improve the inferred marker sets (Segata et al. 2013).”
Microbial strain-level population structure and genetic diversity from metagenomes (2017, neutral): “They can also be used to infer the population structure of strains directly from sets of metage-nomes, similarly to the analysis of isolate genomes using shared (core) genes for phylogenetic, population biology, and comparative genomics (Budroni et al. 2011; Segata et al. 2013; Page et al. 2015).”
Mother-to-Infant Microbial Transmission from Different Body Sites Shapes the Developing Infant Gut Microbiome (2018, methodology): “, 2015), phylogenetic profiling (Segata et al., 2013), and wholegenome strain identity inference (STAR Methods).”