Abstract
Background Eukaryotes such as fungi and protists frequently accompany bacteria and archaea in microbial communities. Unfortunately, their presence is difficult to study with ‘shotgun’ metagenomic sequencing since prokaryotic signals dominate in most environments. Recent methods for eukaryotic detection use eukaryote-specific marker genes, but they do not allow for quantification of eukaryote signal and do not incorporate strategies to handle the presence of eukaryotes that are not represented in the reference marker gene set.
Results Here we present CORRAL (for Clustering Of Related Reference ALignments), a tool for identification of eukaryotes in shotgun metagenomic data based on alignments to eukaryote-specific marker genes and Markov clustering. Using a combination of simulated datasets and large publicly available human microbiome studies, we demonstrate that our method is not only sensitive and accurate but is also capable of inferring the presence of eukaryotes not included in the marker gene reference, such as novel species and strains. Finally, we deploy CORRAL on our MicrobiomeDB.org resource, producing an atlas of eukaryotes present in various environments of the human body and linking their presence to study covariates.
Conclusion CORRAL allows eukaryotic detection to be automated and carried out at scale. Since our approach is independent of the reference used, it may be applicable to other contexts where shotgun metagenomic reads are matched against redundant but non-exhaustive databases, such as identification of novel bacterial strains or taxonomic classification of viral reads.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵# Indicates co-senior authors
New analyses, data, and figures are included in this version. As such, the text has been significantly revised, and the software has been renamed.
https://github.com/wbazant/markerAlignmentsPaper/raw/master/supplement/simulatedReads.xlsx
https://github.com/wbazant/markerAlignmentsPaper/raw/master/supplement/diabimmune.xlsx