- Award ID(s):
- 2125646
- Publication Date:
- NSF-PAR ID:
- 10352571
- Journal Name:
- International Symposium on New Frontiers in Reef Coral Biotechnology (5 May 2022, Taiwan)
- Sponsoring Org:
- National Science Foundation
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The microbiomes of tropical corals are actively studied using 16S rRNA gene amplicons to understand microbial roles in coral health, metabolism, and disease resistance. However, primers targeting bacterial and archaeal 16S rRNA genes may also amplify organelle rRNA genes from the coral, associated microbial eukaryotes, and encrusting organisms. In this manuscript, we demonstrate that standard workflows for annotating microbial taxonomy severely under-annotate mitochondrial sequences in 1272 coral microbiomes from the Earth Microbiome Project. This issue prevents annotation of >95% of reads in some samples and persists when using either Greengenes or SILVA taxonomies. Worse, mitochondrial under-annotation varies between species and across anatomy, biasing comparisons of α- and β-diversity. By supplementing existing taxonomies with diverse mitochondrial rRNA sequences, we resolve ~97% of unique unclassified sequences as mitochondrial, without increasing misannotation in mock communities. We recommend using these extended taxonomies for coral microbiome analysis and encourage vigilance regarding similar issues in other hosts.
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Abstract Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phylogeny. For example, they assume single-gene data (e.g., 16S rRNA amplicons) have evolved under the GTR model on a gene tree. Placement, however, often has a more ambitious goal: extending a (genome-wide) species tree given data from individual genes without knowing the evolutionary model. Addressing this challenging problem requires new directions. Here, we introduce Deep-learning Enabled Phylogenetic Placement (DEPP), an algorithm that learns to extend species trees using single genes without prespecified models. In simulations and on real data, we show that DEPP can match the accuracy of model-based methods without any prior knowledge of the model. We also show that DEPP can update the multilocus microbial tree-of-life with single genes with high accuracy. We further demonstrate that DEPP can combine 16S and metagenomic data onto a single tree, enabling community structure analyses that take advantage of both sources of data. [Deep learning; gene tree discordance; metagenomics; microbiome analyses; neural networks; phylogenetic placement.]
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