Ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses

Abstract

Background: Fungi play critical roles in many ecosystems, cause serious diseases in plants and animals, and pose significant threats to human health and structural integrity problems in built environments. While most fungal diversity remains unknown, the development of PCR primers for the internal transcribed spacer (ITS) combined with next-generation sequencing has substantially improved our ability to profile fungal microbial diversity. Although the high sequence variability in the ITS region facilitates more accurate species identification, it also makes multiple sequence alignment and phylogenetic analysis unreliable across evolutionarily distant fungi because the sequences are hard to align accurately. To address this issue, we created ghost-tree, a bioinformatics tool that integrates sequence data from two genetic markers into a single phylogenetic tree that can be used for diversity analyses. Our approach starts with a "foundation" phylogeny based on one genetic marker whose sequences can be aligned across organisms spanning divergent taxonomic groups (e.g., fungal families). Then, "extension" phylogenies are built for more closely related organisms (e.g., fungal species or strains) using a second more rapidly evolving genetic marker. These smaller phylogenies are then grafted onto the foundation tree by mapping taxonomic names such that each corresponding foundation-tree tip would branch into its new "extension tree" child.

Results: We applied ghost-tree to graft fungal extension phylogenies derived from ITS sequences onto a foundation phylogeny derived from fungal 18S sequences. Our analysis of simulated and real fungal ITS data sets found that phylogenetic distances between fungal communities computed using ghost-tree phylogenies explained significantly more variance than non-phylogenetic distances. The phylogenetic metrics also improved our ability to distinguish small differences (effect sizes) between microbial communities, though results were similar to non-phylogenetic methods for larger effect sizes.

Conclusions: The Silva/UNITE-based ghost tree presented here can be easily integrated into existing fungal analysis pipelines to enhance the resolution of fungal community differences and improve understanding of these communities in built environments. The ghost-tree software package can also be used to develop phylogenetic trees for other marker gene sets that afford different taxonomic resolution, or for bridging genome trees with amplicon trees.

Availability: ghost-tree is pip-installable. All source code, documentation, and test code are available under the BSD license at https://github.com/JTFouquier/ghost-tree .

Fig. 1

ghost-tree workflow diagram

Fig. 2

Principal coordinates comparing unsimulated (real) samples based on a Jaccard distances, b Bray-Curtis distances, c unweighted UniFrac distances where trees are computed using FastTree, d weighted UniFrac distances where trees are computed using FastTree, e unweighted UniFrac distances where trees are computed using ghost-tree, and f weighted UniFrac distances where trees are computed using ghost-tree. Blue points are simulated and real human saliva samples, and red points are simulated and real restroom surface samples. Plots were made using EMPeror software [26]

Fig. 3

Principal coordinates comparing simulated samples based on a Jaccard distances to analyze FastTree-simulated communities (FTSCs), b Bray-Curtis distances to analyze FTSCs, c Jaccard distances to analyze ghost-tree-simulated communities (GTSCs), d Bray-Curtis distances to analyze GTSCs, e unweighted UniFrac distances where trees are computed using FastTree to analyze FTSCs, f weighted UniFrac distances where trees are computed using FastTree to analyze FTSCs, g unweighted UniFrac distances where trees are computed using FastTree to analyze GTSCs, h weighted UniFrac distances where trees are computed using FastTree to analyze GTSCs, i unweighted UniFrac distances where trees are computed using ghost-tree to analyze FTSCs, j weighted UniFrac distances where trees are computed using ghost-tree to analyze FTSCs, k unweighted UniFrac distances where trees are computed using ghost-tree to analyze GTSCs, and l weighted UniFrac distances where trees are computed using ghost-tree to analyze GTSCs. Blue points are simulated and real human saliva samples, and red points are simulated and real restroom surface samples. Plots were made using EMPeror software [26]