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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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Organelles in the ointment: improved detection of cryptic mitochondrial reads resolves many unknown sequences in cross-species microbiome analyses
Abstract The genomes of mitochondria and chloroplasts contain ribosomal RNA (rRNA) genes, reflecting their ancestry as free-living bacteria. These organellar rRNAs are often amplified in microbiome studies of animals and plants. If identified, they can be discarded, merely reducing sequencing depth. However, we identify certain high-abundance organeller RNAs not identified by common pipelines, which may compromise statistical analysis of microbiome structure and diversity. We quantified this by reanalyzing 7459 samples from seven 16S rRNA studies, including microbiomes from 927 unique animal genera. We find that under-annotation of cryptic mitochondrial and chloroplast reads affects multiple of these large-scale cross-species microbiome comparisons, and varies between host species, biasing comparisons. We offer a straightforward solution: supplementing existing taxonomies with diverse organelle rRNA sequences. This resolves up to 97% of unique unclassified sequences in some entire studies as mitochondrial (14% averaged across all studies), without increasing false positive annotations in mitochondria-free mock communities. Improved annotation decreases the proportion of unknown sequences by ≥10-fold in 2262 of 7459 samples (30%), spanning five of seven major studies examined. We recommend leveraging organelle sequence diversity to better identify organelle gene sequences in microbiome studies, and provide code, data resources and tutorials that implement this approach.
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- PAR ID:
- 10559413
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- ISME Communications
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2730-6151
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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