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. 2015 Jun 18:3:e1029.
doi: 10.7717/peerj.1029. eCollection 2015.

Compact graphical representation of phylogenetic data and metadata with GraPhlAn

Francesco Asnicar  1 , George Weingart  2 , Timothy L Tickle  3 , Curtis Huttenhower  4 , Nicola Segata  1
Affiliations

VSports - Compact graphical representation of phylogenetic data and metadata with GraPhlAn

"VSports最新版本" Francesco Asnicar et al. PeerJ. .

"VSports" Abstract

The increased availability of genomic and metagenomic data poses challenges at multiple analysis levels, including visualization of very large-scale microbial and microbial community data paired with rich metadata. We developed GraPhlAn (Graphical Phylogenetic Analysis), a computational tool that produces high-quality, compact visualizations of microbial genomes and metagenomes. This includes phylogenies spanning up to thousands of taxa, annotated with metadata ranging from microbial community abundances to microbial physiology or host and environmental phenotypes. GraPhlAn has been developed as an open-source command-driven tool in order to be easily integrated into complex, publication-quality bioinformatics pipelines. It can be executed either locally or through an online Galaxy web application VSports手机版. We present several examples including taxonomic and phylogenetic visualization of microbial communities, metabolic functions, and biomarker discovery that illustrate GraPhlAn's potential for modern microbial and community genomics. .

Keywords: Graphical representation; Metagenomics; Phylogenetic visualization; Phylogenomics V体育安卓版. .

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Schematic and simplified example of GraPhlAn visualization of annotated phylogenies and taxonomies.
The software can start from a tree in Newick, Nexus, PhyloXML, or plain text formats. The “default plot” (A) produces a basic visualization of the tree’s hierarchical structure. Through an annotation file, it is possible to configure a number of options that affect the appearance of the tree. For instance, some global parameters will affect the whole tree structure, such as the color and thickness of branches (“set global options,” B). The same annotation file can act on specific nodes, customizing their shape, size, and color (“set node options,” C). Labels and background colors for specific branches in the tree can also be configured (“set label options,” D). External to the circular area of the tree, the annotation file can include directives for plotting different shapes, heatmap colors, or bar-plots representing quantitative taxon traits (“set external ring options,” E).
Figure 2
Figure 2. A large, 3,737 genome phylogeny annotated with functional genomic properties.
We used the phylogenetic tree built using PhyloPhlAn (Segata et al., 2013) on all available microbial genomes as of 2013 and annotated the presence of ATP synthesis and Fatty Acid metabolism functional modules (as annotated in KEGG) and the genome length for all genomes. Colors and background annotation highlight bacterial phyla, and the functional information is reported in external rings. ATP synthesis rings visualize the presence (or absence) of each module, while Fatty Acid metabolism capability is represented with a gradient color. Data used in this image are available as indicated in the “Datasets used” paragraph, under “Materials and Methods” section.
Figure 3
Figure 3. Taxonomic comparison between HMP and MetaHIT stool samples.
The taxonomic cladogram shows a comparison between the MetaHIT and HMP studies limited to samples from the gut (for the latter) and from healthy subjects (for the former). This image has been generated by GraPhlAn using input files from the supporting “export2graphlan” script (see “Materials and Methods”) applied on the output of MetaPhlAn2 (Segata et al., 2012) and LEfSe (Segata et al., 2011). Colors distinguish between HMP (green) and MetaHIT (blue), while the intensity reflects the LDA score, an indicator of the effect sizes of the significant differences. The size of the nodes correlates with their relative and logarithmically scaled abundances. Data used for this image is available as indicated under “Datasets used” paragraph in the “Materials and Methods” section.
Figure 4
Figure 4. Comparison of microbial community metabolic pathway abundances between HMP and MetaHIT.
Comparison of functional pathway abundances from the HMP (green) and MetaHIT (blue). This is the functional counterpart of the plot in Fig. 3 and was obtained applying GraPhlAn on HUMAnN (Abubucker et al., 2012) metabolic profiling. The intensity of the color represents the LDA score, and the sizes of the nodes are proportional to the pathway relative abundance estimated by HUMAnN. Three major groups are automatically highlighted by specifying them to the export2graphlan script: Environmental Information Processing, Genetic Information Processing, and Metabolism. Data used for this image is available as indicated under “Datasets used” paragraph in “Materials and Methods” section.
Figure 5
Figure 5. Integration of GraPhlAn into existing analyses pipelines.
We developed a conversion framework called “export2graphlan” that can deal with several output formats from different analysis pipelines, generating the necessary input files for GraPhlAn. Export2graphlan directly supports MetaPhlAn2, LEfSe, and HUMAnN output files. In addition, it can also accept BIOM files (both version 1 and 2), making GraPhlAn available for tools supporting this format including the QIIME and mothur systems. The tools can be ran on local machine as well as through the Galaxy web system using the modules reported in green boxes.
See this image and copyright information in PMC

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